NAMIC Wiki - User contributions [en]

2011 Scientific Progress Report

2011-04-15T15:52:15Z

Medcomparts: /* Highlights (Will Schroeder) */

=Report Outline=

==Introduction==

==Highlights (Will Schroeder, Missing)==

*Advanced algorithms

*NA-MIC Kit

*Table of Downloads

*Dissemination: SECTION PROVIDED BY TINA

==Impact (Jim Miller, Submitted to Ann)==

*Impact within the Center

*Impact within NIH-Funded Research

*National and International Impact

==DBPs==
===Atrial Fibrillation, Rob MacLeod===

===Huntington's Disease, Hans Johnson===

===Head and Neck Cancer, Greg Sharp===

Submitted.

===Traumatic Brain Injury, Jack Van Horn===

Submitted.

==CS Core==

===Algorithms (Ross)===

*Statistical Models of Anatomy and Pathology, Polina Golland

*Geometric Correspondence, Guido Gerig

*User Interactive Tools for Segmentation, Allen Tannenbaum

*Longitudinal and Time Series Analysis, Martin Styner

===NA-MIC Kit (Will)===

==ARRA SUPPLEMENTS: Quality Assurance==

==Publications==
1. Wang Y., Gupta A., Liu Z., Zhang H., Escolar M.L., Gilmore J.H., Gouttard S., Fillard P., Maltbie E., Gerig G., Styner M. DTI Registration in Atlas Based Fiber Analysis of Infantile Krabbe Disease. Neuroimage. 2011 Apr 15;55(4):1577-86. PMID: 21256236. PMCID: PMC3062693.

2. Pohl K.M., Konukoglu E., Novellas S., Ayache N., Fedorov A., Talos I-F., Golby A., Wells III W.M., Kikinis R., Black P.M. A New Metric for Detecting Change in Slowly Evolving Brain Tumors: Validation in Meningioma Patients. Neurosurgery. 2011 Mar;68(1 Suppl Operative):225-33. PMID: 21206318.

3. Leritz E.C., Salat D.H., Williams V.J., Schnyer D.M., Rudolph J.L., Lipsitz L., Fischl B., McGlinchey R.E., Milberg W.P. Thickness of the Human Cerebral Cortex is Associated with Metrics of Cerebrovascular Health in a Normative Sample of Community Dwelling Older Adults. Neuroimage. 2011 Feb 14;54(4):2659-71. PMID: 21035552. PMCID: PMC3026290.

4. Salat D.H., Chen J.J., Van der Kouwe A.J.W., Greve D.N., Fischl B., Rosas H.D. Hippocampal Degeneration is Associated with Temporal and Limbic Gray Matter/White Matter Tissue Contrast in Alzheimer's Disease. Neuroimage. 2011 Feb 1;54(3):1795-802. PMID: 20965261. PMCID: PMC3021138.

5. Wang X., Grimson W.E.L., Westin C-F. Tractography Segmentation using a Hierarchical Dirichlet Processes Mixture Model. Neuroimage. 2011 Jan 1;54(1):290-302. PMID: 20678578. PMCID: PMC2962770.

6. Oguro S., Tuncali K., Elhawary H., Morrison P.R., Hata N., Silverman S.G. Image Registration of Pre-procedural MRI and Intra-procedural CT Images to Aid CT-guided Percutaneous Cryoablation of Renal Tumors. Int J Comput Assist Radiol Surg. 2011 Jan;6(1):111-7. PMID: 20499194. PMCID: PMC3050046.

7. Mohan V., Sundaramoorthi G., Tannenbaum A. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery. IEEE Trans Med Imaging. 2010 Dec;29(12):1945-58. PMID: 21118754.

8. Langs G., Golland P., Tie Y., Rigolo L., Golby A.J. Functional Geometry Alignment and Localization of Brain Areas. Proceedings of the 24th Annual Conference on Neural Information Processing Systems 2010; 1:1225-1233.

9. Shenton M.E., Whitford T.J., Kubicki M. Structural Neuroimaging in Schizophrenia: From Methods to Insights to Treatments. Dialogues Clin Neurosci. 2010;12(3):317-32. PMID: 20954428.

10. Nho K., Shen L., Kim S., Risacher S.L., West J.D., Foroud T., Jack C.R., Weiner M.W., Saykin A.J. Automatic Prediction of Conversion from Mild Cognitive Impairment to Probable Alzheimer's Disease using Structural Magnetic Resonance Imaging. AMIA Annu Symp Proc. 2010 Nov 13;2010:542-6. PMID: 21347037. PMCID: PMC3041374.

11. Kikinis Z., Fallon J.H., Niznikiewicz M., Nestor P., Davidson C., Bobrow L., Pelavin P.E., Fischl B., Yendiki A., McCarley R.W., Kikinis R., Kubicki M., Shenton M.E. Gray Matter Volume Reduction in Rostral Middle Frontal Gyrus in Patients with Chronic Schizophrenia. Schizophr Res. 2010 Nov;123(2-3):153-9. PMID: 20822884. PMCID: PMC2975427.

12. Kremen W.S., O'Brien R.C., Panizzon M.S., Prom-Wormley E., Eaves L.J., Eisen S.A., Eyler L.T., Hauger R.L., Fennema-Notestine C., Fischl B., Grant M.D., Hellhammer D.H., Jak A.J., Jacobson K.C., Jernigan T.L., Lupien S.J., Lyons M.J., Mendoza S.P., Neale M.C., Seidman L.J., Thermenos H.W., Tsuang M.T., Dale A.M., Franz C.E. Salivary Cortisol and Prefrontal Cortical Thickness in Middle-aged Men: A Twin Study. Neuroimage. 2010 Nov 15;53(3):1093-102. PMID: 20156572.

13. Elhawary H., Oguro S., Tuncali K., Morrison P.R., Tatli S., Shyn P.B., Silverman S.G., Hata N. Multimodality Non-rigid Image Registration for Planning, Targeting and Monitoring during CT-guided Percutaneous Liver Tumor Cryoablation. Acad Radiol. 2010 Nov;17(11):1334-44. PMID: 20817574. PMCID: PMC2952665.

14. Gerber S., Bremer P-T., Pascucci V., Whitaker R. Visual Exploration of High Dimensional Scalar Functions. IEEE Trans Vis Comput Graph. 2010 Nov-Dec;16(6):1271-80. PMID: 20975167.

15. Ghosh S.S., Kakunoori S., Augustinack J., Nieto-Castanon A., Kovelman I., Gaab N., Christodoulou J.A., Triantafyllou C., Gabrieli J.D.E., Fischl B. Evaluating the Validity of Volume-based and Surface-based Brain Image Registration for Developmental Cognitive Neuroscience Studies in Children 4 to 11 Years of Age. Neuroimage. 2010 Oct 15;53(1):85-93. PMID: 20621657. PMCID: PMC2914629.

16. Destrieux C., Fischl B., Dale A., Halgren E. Automatic Parcellation of Human Cortical Gyri and Sulci using Standard Anatomical Nomenclature. Neuroimage. 2010 Oct 15;53(1):1-15. PMID: 20547229. PMCID: PMC2937159.

17. Riklin-Raviv T., Van Leemput K., Menze B.H., Wells III W.M., Golland P. Segmentation of Image Ensembles via Latent Atlases. Med Image Anal. 2010 Oct;14(5):654-65. PMID: 20580305. PMCID: PMC2932709.

18. Sabuncu M.R., Yeo B.T.T., Van Leemput K., Fischl B., Golland P. A Generative Model for Image Segmentation Based on Label Fusion. IEEE Trans Med Imaging. 2010 Oct;29(10):1714-29. PMID: 20562040.

19. Gerber S., Tasdizen T., Thomas Fletcher P., Joshi S., Whitaker R. Manifold Modeling for Brain Population Analysis. Med Image Anal. 2010 Oct;14(5):643-53. PMID: 20579930. PMCID: PMC3020141.

20. Gao Y., Sandhu R., Fichtinger G., Tannenbaum A.R. A Coupled Global Registration and Segmentation Framework with Application to Magnetic Resonance Prostate Imagery. IEEE Trans Med Imaging. 2010 Oct;29(10):1781-94. PMID: 20529727. PMCID: PMC2988404.

==EAB REPORT (Lorensen)==

*Bill expects to complete this by Friday, April, 8, 2011.

2011 Scientific Progress Report

2011-04-15T15:51:49Z

Medcomparts: /* Highlights (Will Schroeder) */

=Report Outline=

==Introduction==

==Highlights (Will Schroeder)==

DOCUMENT MISSING

*Advanced algorithms

*NA-MIC Kit

*Table of Downloads

*Dissemination: SECTION PROVIDED BY TINA

==Impact (Jim Miller, Submitted to Ann)==

*Impact within the Center

*Impact within NIH-Funded Research

*National and International Impact

==DBPs==
===Atrial Fibrillation, Rob MacLeod===

===Huntington's Disease, Hans Johnson===

===Head and Neck Cancer, Greg Sharp===

Submitted.

===Traumatic Brain Injury, Jack Van Horn===

Submitted.

==CS Core==

===Algorithms (Ross)===

*Statistical Models of Anatomy and Pathology, Polina Golland

*Geometric Correspondence, Guido Gerig

*User Interactive Tools for Segmentation, Allen Tannenbaum

*Longitudinal and Time Series Analysis, Martin Styner

===NA-MIC Kit (Will)===

==ARRA SUPPLEMENTS: Quality Assurance==

==Publications==
1. Wang Y., Gupta A., Liu Z., Zhang H., Escolar M.L., Gilmore J.H., Gouttard S., Fillard P., Maltbie E., Gerig G., Styner M. DTI Registration in Atlas Based Fiber Analysis of Infantile Krabbe Disease. Neuroimage. 2011 Apr 15;55(4):1577-86. PMID: 21256236. PMCID: PMC3062693.

2. Pohl K.M., Konukoglu E., Novellas S., Ayache N., Fedorov A., Talos I-F., Golby A., Wells III W.M., Kikinis R., Black P.M. A New Metric for Detecting Change in Slowly Evolving Brain Tumors: Validation in Meningioma Patients. Neurosurgery. 2011 Mar;68(1 Suppl Operative):225-33. PMID: 21206318.

3. Leritz E.C., Salat D.H., Williams V.J., Schnyer D.M., Rudolph J.L., Lipsitz L., Fischl B., McGlinchey R.E., Milberg W.P. Thickness of the Human Cerebral Cortex is Associated with Metrics of Cerebrovascular Health in a Normative Sample of Community Dwelling Older Adults. Neuroimage. 2011 Feb 14;54(4):2659-71. PMID: 21035552. PMCID: PMC3026290.

4. Salat D.H., Chen J.J., Van der Kouwe A.J.W., Greve D.N., Fischl B., Rosas H.D. Hippocampal Degeneration is Associated with Temporal and Limbic Gray Matter/White Matter Tissue Contrast in Alzheimer's Disease. Neuroimage. 2011 Feb 1;54(3):1795-802. PMID: 20965261. PMCID: PMC3021138.

5. Wang X., Grimson W.E.L., Westin C-F. Tractography Segmentation using a Hierarchical Dirichlet Processes Mixture Model. Neuroimage. 2011 Jan 1;54(1):290-302. PMID: 20678578. PMCID: PMC2962770.

6. Oguro S., Tuncali K., Elhawary H., Morrison P.R., Hata N., Silverman S.G. Image Registration of Pre-procedural MRI and Intra-procedural CT Images to Aid CT-guided Percutaneous Cryoablation of Renal Tumors. Int J Comput Assist Radiol Surg. 2011 Jan;6(1):111-7. PMID: 20499194. PMCID: PMC3050046.

7. Mohan V., Sundaramoorthi G., Tannenbaum A. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery. IEEE Trans Med Imaging. 2010 Dec;29(12):1945-58. PMID: 21118754.

8. Langs G., Golland P., Tie Y., Rigolo L., Golby A.J. Functional Geometry Alignment and Localization of Brain Areas. Proceedings of the 24th Annual Conference on Neural Information Processing Systems 2010; 1:1225-1233.

9. Shenton M.E., Whitford T.J., Kubicki M. Structural Neuroimaging in Schizophrenia: From Methods to Insights to Treatments. Dialogues Clin Neurosci. 2010;12(3):317-32. PMID: 20954428.

10. Nho K., Shen L., Kim S., Risacher S.L., West J.D., Foroud T., Jack C.R., Weiner M.W., Saykin A.J. Automatic Prediction of Conversion from Mild Cognitive Impairment to Probable Alzheimer's Disease using Structural Magnetic Resonance Imaging. AMIA Annu Symp Proc. 2010 Nov 13;2010:542-6. PMID: 21347037. PMCID: PMC3041374.

11. Kikinis Z., Fallon J.H., Niznikiewicz M., Nestor P., Davidson C., Bobrow L., Pelavin P.E., Fischl B., Yendiki A., McCarley R.W., Kikinis R., Kubicki M., Shenton M.E. Gray Matter Volume Reduction in Rostral Middle Frontal Gyrus in Patients with Chronic Schizophrenia. Schizophr Res. 2010 Nov;123(2-3):153-9. PMID: 20822884. PMCID: PMC2975427.

12. Kremen W.S., O'Brien R.C., Panizzon M.S., Prom-Wormley E., Eaves L.J., Eisen S.A., Eyler L.T., Hauger R.L., Fennema-Notestine C., Fischl B., Grant M.D., Hellhammer D.H., Jak A.J., Jacobson K.C., Jernigan T.L., Lupien S.J., Lyons M.J., Mendoza S.P., Neale M.C., Seidman L.J., Thermenos H.W., Tsuang M.T., Dale A.M., Franz C.E. Salivary Cortisol and Prefrontal Cortical Thickness in Middle-aged Men: A Twin Study. Neuroimage. 2010 Nov 15;53(3):1093-102. PMID: 20156572.

13. Elhawary H., Oguro S., Tuncali K., Morrison P.R., Tatli S., Shyn P.B., Silverman S.G., Hata N. Multimodality Non-rigid Image Registration for Planning, Targeting and Monitoring during CT-guided Percutaneous Liver Tumor Cryoablation. Acad Radiol. 2010 Nov;17(11):1334-44. PMID: 20817574. PMCID: PMC2952665.

14. Gerber S., Bremer P-T., Pascucci V., Whitaker R. Visual Exploration of High Dimensional Scalar Functions. IEEE Trans Vis Comput Graph. 2010 Nov-Dec;16(6):1271-80. PMID: 20975167.

15. Ghosh S.S., Kakunoori S., Augustinack J., Nieto-Castanon A., Kovelman I., Gaab N., Christodoulou J.A., Triantafyllou C., Gabrieli J.D.E., Fischl B. Evaluating the Validity of Volume-based and Surface-based Brain Image Registration for Developmental Cognitive Neuroscience Studies in Children 4 to 11 Years of Age. Neuroimage. 2010 Oct 15;53(1):85-93. PMID: 20621657. PMCID: PMC2914629.

16. Destrieux C., Fischl B., Dale A., Halgren E. Automatic Parcellation of Human Cortical Gyri and Sulci using Standard Anatomical Nomenclature. Neuroimage. 2010 Oct 15;53(1):1-15. PMID: 20547229. PMCID: PMC2937159.

17. Riklin-Raviv T., Van Leemput K., Menze B.H., Wells III W.M., Golland P. Segmentation of Image Ensembles via Latent Atlases. Med Image Anal. 2010 Oct;14(5):654-65. PMID: 20580305. PMCID: PMC2932709.

18. Sabuncu M.R., Yeo B.T.T., Van Leemput K., Fischl B., Golland P. A Generative Model for Image Segmentation Based on Label Fusion. IEEE Trans Med Imaging. 2010 Oct;29(10):1714-29. PMID: 20562040.

19. Gerber S., Tasdizen T., Thomas Fletcher P., Joshi S., Whitaker R. Manifold Modeling for Brain Population Analysis. Med Image Anal. 2010 Oct;14(5):643-53. PMID: 20579930. PMCID: PMC3020141.

20. Gao Y., Sandhu R., Fichtinger G., Tannenbaum A.R. A Coupled Global Registration and Segmentation Framework with Application to Magnetic Resonance Prostate Imagery. IEEE Trans Med Imaging. 2010 Oct;29(10):1781-94. PMID: 20529727. PMCID: PMC2988404.

==EAB REPORT (Lorensen)==

*Bill expects to complete this by Friday, April, 8, 2011.

2011 Scientific Progress Report

2011-04-15T15:51:33Z

Medcomparts: /* Highlights (Will Schroeder) */

=Report Outline=

==Introduction==

==Highlights (Will Schroeder)==

DOCUMENT MISSING

*Advanced algorithms

*NA-MIC Kit

*Table of Downloads

*Dissemination: PROVIDED BY TINA

==Impact (Jim Miller, Submitted to Ann)==

*Impact within the Center

*Impact within NIH-Funded Research

*National and International Impact

==DBPs==
===Atrial Fibrillation, Rob MacLeod===

===Huntington's Disease, Hans Johnson===

===Head and Neck Cancer, Greg Sharp===

Submitted.

===Traumatic Brain Injury, Jack Van Horn===

Submitted.

==CS Core==

===Algorithms (Ross)===

*Statistical Models of Anatomy and Pathology, Polina Golland

*Geometric Correspondence, Guido Gerig

*User Interactive Tools for Segmentation, Allen Tannenbaum

*Longitudinal and Time Series Analysis, Martin Styner

===NA-MIC Kit (Will)===

==ARRA SUPPLEMENTS: Quality Assurance==

==Publications==
1. Wang Y., Gupta A., Liu Z., Zhang H., Escolar M.L., Gilmore J.H., Gouttard S., Fillard P., Maltbie E., Gerig G., Styner M. DTI Registration in Atlas Based Fiber Analysis of Infantile Krabbe Disease. Neuroimage. 2011 Apr 15;55(4):1577-86. PMID: 21256236. PMCID: PMC3062693.

2. Pohl K.M., Konukoglu E., Novellas S., Ayache N., Fedorov A., Talos I-F., Golby A., Wells III W.M., Kikinis R., Black P.M. A New Metric for Detecting Change in Slowly Evolving Brain Tumors: Validation in Meningioma Patients. Neurosurgery. 2011 Mar;68(1 Suppl Operative):225-33. PMID: 21206318.

3. Leritz E.C., Salat D.H., Williams V.J., Schnyer D.M., Rudolph J.L., Lipsitz L., Fischl B., McGlinchey R.E., Milberg W.P. Thickness of the Human Cerebral Cortex is Associated with Metrics of Cerebrovascular Health in a Normative Sample of Community Dwelling Older Adults. Neuroimage. 2011 Feb 14;54(4):2659-71. PMID: 21035552. PMCID: PMC3026290.

4. Salat D.H., Chen J.J., Van der Kouwe A.J.W., Greve D.N., Fischl B., Rosas H.D. Hippocampal Degeneration is Associated with Temporal and Limbic Gray Matter/White Matter Tissue Contrast in Alzheimer's Disease. Neuroimage. 2011 Feb 1;54(3):1795-802. PMID: 20965261. PMCID: PMC3021138.

5. Wang X., Grimson W.E.L., Westin C-F. Tractography Segmentation using a Hierarchical Dirichlet Processes Mixture Model. Neuroimage. 2011 Jan 1;54(1):290-302. PMID: 20678578. PMCID: PMC2962770.

6. Oguro S., Tuncali K., Elhawary H., Morrison P.R., Hata N., Silverman S.G. Image Registration of Pre-procedural MRI and Intra-procedural CT Images to Aid CT-guided Percutaneous Cryoablation of Renal Tumors. Int J Comput Assist Radiol Surg. 2011 Jan;6(1):111-7. PMID: 20499194. PMCID: PMC3050046.

7. Mohan V., Sundaramoorthi G., Tannenbaum A. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery. IEEE Trans Med Imaging. 2010 Dec;29(12):1945-58. PMID: 21118754.

8. Langs G., Golland P., Tie Y., Rigolo L., Golby A.J. Functional Geometry Alignment and Localization of Brain Areas. Proceedings of the 24th Annual Conference on Neural Information Processing Systems 2010; 1:1225-1233.

9. Shenton M.E., Whitford T.J., Kubicki M. Structural Neuroimaging in Schizophrenia: From Methods to Insights to Treatments. Dialogues Clin Neurosci. 2010;12(3):317-32. PMID: 20954428.

10. Nho K., Shen L., Kim S., Risacher S.L., West J.D., Foroud T., Jack C.R., Weiner M.W., Saykin A.J. Automatic Prediction of Conversion from Mild Cognitive Impairment to Probable Alzheimer's Disease using Structural Magnetic Resonance Imaging. AMIA Annu Symp Proc. 2010 Nov 13;2010:542-6. PMID: 21347037. PMCID: PMC3041374.

11. Kikinis Z., Fallon J.H., Niznikiewicz M., Nestor P., Davidson C., Bobrow L., Pelavin P.E., Fischl B., Yendiki A., McCarley R.W., Kikinis R., Kubicki M., Shenton M.E. Gray Matter Volume Reduction in Rostral Middle Frontal Gyrus in Patients with Chronic Schizophrenia. Schizophr Res. 2010 Nov;123(2-3):153-9. PMID: 20822884. PMCID: PMC2975427.

12. Kremen W.S., O'Brien R.C., Panizzon M.S., Prom-Wormley E., Eaves L.J., Eisen S.A., Eyler L.T., Hauger R.L., Fennema-Notestine C., Fischl B., Grant M.D., Hellhammer D.H., Jak A.J., Jacobson K.C., Jernigan T.L., Lupien S.J., Lyons M.J., Mendoza S.P., Neale M.C., Seidman L.J., Thermenos H.W., Tsuang M.T., Dale A.M., Franz C.E. Salivary Cortisol and Prefrontal Cortical Thickness in Middle-aged Men: A Twin Study. Neuroimage. 2010 Nov 15;53(3):1093-102. PMID: 20156572.

13. Elhawary H., Oguro S., Tuncali K., Morrison P.R., Tatli S., Shyn P.B., Silverman S.G., Hata N. Multimodality Non-rigid Image Registration for Planning, Targeting and Monitoring during CT-guided Percutaneous Liver Tumor Cryoablation. Acad Radiol. 2010 Nov;17(11):1334-44. PMID: 20817574. PMCID: PMC2952665.

14. Gerber S., Bremer P-T., Pascucci V., Whitaker R. Visual Exploration of High Dimensional Scalar Functions. IEEE Trans Vis Comput Graph. 2010 Nov-Dec;16(6):1271-80. PMID: 20975167.

15. Ghosh S.S., Kakunoori S., Augustinack J., Nieto-Castanon A., Kovelman I., Gaab N., Christodoulou J.A., Triantafyllou C., Gabrieli J.D.E., Fischl B. Evaluating the Validity of Volume-based and Surface-based Brain Image Registration for Developmental Cognitive Neuroscience Studies in Children 4 to 11 Years of Age. Neuroimage. 2010 Oct 15;53(1):85-93. PMID: 20621657. PMCID: PMC2914629.

16. Destrieux C., Fischl B., Dale A., Halgren E. Automatic Parcellation of Human Cortical Gyri and Sulci using Standard Anatomical Nomenclature. Neuroimage. 2010 Oct 15;53(1):1-15. PMID: 20547229. PMCID: PMC2937159.

17. Riklin-Raviv T., Van Leemput K., Menze B.H., Wells III W.M., Golland P. Segmentation of Image Ensembles via Latent Atlases. Med Image Anal. 2010 Oct;14(5):654-65. PMID: 20580305. PMCID: PMC2932709.

18. Sabuncu M.R., Yeo B.T.T., Van Leemput K., Fischl B., Golland P. A Generative Model for Image Segmentation Based on Label Fusion. IEEE Trans Med Imaging. 2010 Oct;29(10):1714-29. PMID: 20562040.

19. Gerber S., Tasdizen T., Thomas Fletcher P., Joshi S., Whitaker R. Manifold Modeling for Brain Population Analysis. Med Image Anal. 2010 Oct;14(5):643-53. PMID: 20579930. PMCID: PMC3020141.

20. Gao Y., Sandhu R., Fichtinger G., Tannenbaum A.R. A Coupled Global Registration and Segmentation Framework with Application to Magnetic Resonance Prostate Imagery. IEEE Trans Med Imaging. 2010 Oct;29(10):1781-94. PMID: 20529727. PMCID: PMC2988404.

==EAB REPORT (Lorensen)==

*Bill expects to complete this by Friday, April, 8, 2011.

2011 Scientific Progress Report

2011-04-15T15:51:15Z

Medcomparts: /* Introduction */

=Report Outline=

==Introduction==

==Highlights (Will Schroeder)==

MISSING

*Advanced algorithms

*NA-MIC Kit

*Table of Downloads

*Dissemination: PROVIDED BY TINA

==Impact (Jim Miller, Submitted to Ann)==

*Impact within the Center

*Impact within NIH-Funded Research

*National and International Impact

==DBPs==
===Atrial Fibrillation, Rob MacLeod===

===Huntington's Disease, Hans Johnson===

===Head and Neck Cancer, Greg Sharp===

Submitted.

===Traumatic Brain Injury, Jack Van Horn===

Submitted.

==CS Core==

===Algorithms (Ross)===

*Statistical Models of Anatomy and Pathology, Polina Golland

*Geometric Correspondence, Guido Gerig

*User Interactive Tools for Segmentation, Allen Tannenbaum

*Longitudinal and Time Series Analysis, Martin Styner

===NA-MIC Kit (Will)===

==ARRA SUPPLEMENTS: Quality Assurance==

==Publications==
1. Wang Y., Gupta A., Liu Z., Zhang H., Escolar M.L., Gilmore J.H., Gouttard S., Fillard P., Maltbie E., Gerig G., Styner M. DTI Registration in Atlas Based Fiber Analysis of Infantile Krabbe Disease. Neuroimage. 2011 Apr 15;55(4):1577-86. PMID: 21256236. PMCID: PMC3062693.

2. Pohl K.M., Konukoglu E., Novellas S., Ayache N., Fedorov A., Talos I-F., Golby A., Wells III W.M., Kikinis R., Black P.M. A New Metric for Detecting Change in Slowly Evolving Brain Tumors: Validation in Meningioma Patients. Neurosurgery. 2011 Mar;68(1 Suppl Operative):225-33. PMID: 21206318.

3. Leritz E.C., Salat D.H., Williams V.J., Schnyer D.M., Rudolph J.L., Lipsitz L., Fischl B., McGlinchey R.E., Milberg W.P. Thickness of the Human Cerebral Cortex is Associated with Metrics of Cerebrovascular Health in a Normative Sample of Community Dwelling Older Adults. Neuroimage. 2011 Feb 14;54(4):2659-71. PMID: 21035552. PMCID: PMC3026290.

4. Salat D.H., Chen J.J., Van der Kouwe A.J.W., Greve D.N., Fischl B., Rosas H.D. Hippocampal Degeneration is Associated with Temporal and Limbic Gray Matter/White Matter Tissue Contrast in Alzheimer's Disease. Neuroimage. 2011 Feb 1;54(3):1795-802. PMID: 20965261. PMCID: PMC3021138.

5. Wang X., Grimson W.E.L., Westin C-F. Tractography Segmentation using a Hierarchical Dirichlet Processes Mixture Model. Neuroimage. 2011 Jan 1;54(1):290-302. PMID: 20678578. PMCID: PMC2962770.

6. Oguro S., Tuncali K., Elhawary H., Morrison P.R., Hata N., Silverman S.G. Image Registration of Pre-procedural MRI and Intra-procedural CT Images to Aid CT-guided Percutaneous Cryoablation of Renal Tumors. Int J Comput Assist Radiol Surg. 2011 Jan;6(1):111-7. PMID: 20499194. PMCID: PMC3050046.

7. Mohan V., Sundaramoorthi G., Tannenbaum A. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery. IEEE Trans Med Imaging. 2010 Dec;29(12):1945-58. PMID: 21118754.

8. Langs G., Golland P., Tie Y., Rigolo L., Golby A.J. Functional Geometry Alignment and Localization of Brain Areas. Proceedings of the 24th Annual Conference on Neural Information Processing Systems 2010; 1:1225-1233.

9. Shenton M.E., Whitford T.J., Kubicki M. Structural Neuroimaging in Schizophrenia: From Methods to Insights to Treatments. Dialogues Clin Neurosci. 2010;12(3):317-32. PMID: 20954428.

10. Nho K., Shen L., Kim S., Risacher S.L., West J.D., Foroud T., Jack C.R., Weiner M.W., Saykin A.J. Automatic Prediction of Conversion from Mild Cognitive Impairment to Probable Alzheimer's Disease using Structural Magnetic Resonance Imaging. AMIA Annu Symp Proc. 2010 Nov 13;2010:542-6. PMID: 21347037. PMCID: PMC3041374.

11. Kikinis Z., Fallon J.H., Niznikiewicz M., Nestor P., Davidson C., Bobrow L., Pelavin P.E., Fischl B., Yendiki A., McCarley R.W., Kikinis R., Kubicki M., Shenton M.E. Gray Matter Volume Reduction in Rostral Middle Frontal Gyrus in Patients with Chronic Schizophrenia. Schizophr Res. 2010 Nov;123(2-3):153-9. PMID: 20822884. PMCID: PMC2975427.

12. Kremen W.S., O'Brien R.C., Panizzon M.S., Prom-Wormley E., Eaves L.J., Eisen S.A., Eyler L.T., Hauger R.L., Fennema-Notestine C., Fischl B., Grant M.D., Hellhammer D.H., Jak A.J., Jacobson K.C., Jernigan T.L., Lupien S.J., Lyons M.J., Mendoza S.P., Neale M.C., Seidman L.J., Thermenos H.W., Tsuang M.T., Dale A.M., Franz C.E. Salivary Cortisol and Prefrontal Cortical Thickness in Middle-aged Men: A Twin Study. Neuroimage. 2010 Nov 15;53(3):1093-102. PMID: 20156572.

13. Elhawary H., Oguro S., Tuncali K., Morrison P.R., Tatli S., Shyn P.B., Silverman S.G., Hata N. Multimodality Non-rigid Image Registration for Planning, Targeting and Monitoring during CT-guided Percutaneous Liver Tumor Cryoablation. Acad Radiol. 2010 Nov;17(11):1334-44. PMID: 20817574. PMCID: PMC2952665.

14. Gerber S., Bremer P-T., Pascucci V., Whitaker R. Visual Exploration of High Dimensional Scalar Functions. IEEE Trans Vis Comput Graph. 2010 Nov-Dec;16(6):1271-80. PMID: 20975167.

15. Ghosh S.S., Kakunoori S., Augustinack J., Nieto-Castanon A., Kovelman I., Gaab N., Christodoulou J.A., Triantafyllou C., Gabrieli J.D.E., Fischl B. Evaluating the Validity of Volume-based and Surface-based Brain Image Registration for Developmental Cognitive Neuroscience Studies in Children 4 to 11 Years of Age. Neuroimage. 2010 Oct 15;53(1):85-93. PMID: 20621657. PMCID: PMC2914629.

16. Destrieux C., Fischl B., Dale A., Halgren E. Automatic Parcellation of Human Cortical Gyri and Sulci using Standard Anatomical Nomenclature. Neuroimage. 2010 Oct 15;53(1):1-15. PMID: 20547229. PMCID: PMC2937159.

17. Riklin-Raviv T., Van Leemput K., Menze B.H., Wells III W.M., Golland P. Segmentation of Image Ensembles via Latent Atlases. Med Image Anal. 2010 Oct;14(5):654-65. PMID: 20580305. PMCID: PMC2932709.

18. Sabuncu M.R., Yeo B.T.T., Van Leemput K., Fischl B., Golland P. A Generative Model for Image Segmentation Based on Label Fusion. IEEE Trans Med Imaging. 2010 Oct;29(10):1714-29. PMID: 20562040.

19. Gerber S., Tasdizen T., Thomas Fletcher P., Joshi S., Whitaker R. Manifold Modeling for Brain Population Analysis. Med Image Anal. 2010 Oct;14(5):643-53. PMID: 20579930. PMCID: PMC3020141.

20. Gao Y., Sandhu R., Fichtinger G., Tannenbaum A.R. A Coupled Global Registration and Segmentation Framework with Application to Magnetic Resonance Prostate Imagery. IEEE Trans Med Imaging. 2010 Oct;29(10):1781-94. PMID: 20529727. PMCID: PMC2988404.

==EAB REPORT (Lorensen)==

*Bill expects to complete this by Friday, April, 8, 2011.

2011 Scientific Progress Report

2011-04-15T15:51:01Z

Medcomparts: /* Highlights (Will Schroeder) */

=Report Outline=

==Introduction==

==Highlights (Will Schroeder)== MISSING

*Advanced algorithms

*NA-MIC Kit

*Table of Downloads

*Dissemination: PROVIDED BY TINA

==Impact (Jim Miller, Submitted to Ann)==

*Impact within the Center

*Impact within NIH-Funded Research

*National and International Impact

==DBPs==
===Atrial Fibrillation, Rob MacLeod===

===Huntington's Disease, Hans Johnson===

===Head and Neck Cancer, Greg Sharp===

Submitted.

===Traumatic Brain Injury, Jack Van Horn===

Submitted.

==CS Core==

===Algorithms (Ross)===

*Statistical Models of Anatomy and Pathology, Polina Golland

*Geometric Correspondence, Guido Gerig

*User Interactive Tools for Segmentation, Allen Tannenbaum

*Longitudinal and Time Series Analysis, Martin Styner

===NA-MIC Kit (Will)===

==ARRA SUPPLEMENTS: Quality Assurance==

==Publications==
1. Wang Y., Gupta A., Liu Z., Zhang H., Escolar M.L., Gilmore J.H., Gouttard S., Fillard P., Maltbie E., Gerig G., Styner M. DTI Registration in Atlas Based Fiber Analysis of Infantile Krabbe Disease. Neuroimage. 2011 Apr 15;55(4):1577-86. PMID: 21256236. PMCID: PMC3062693.

2. Pohl K.M., Konukoglu E., Novellas S., Ayache N., Fedorov A., Talos I-F., Golby A., Wells III W.M., Kikinis R., Black P.M. A New Metric for Detecting Change in Slowly Evolving Brain Tumors: Validation in Meningioma Patients. Neurosurgery. 2011 Mar;68(1 Suppl Operative):225-33. PMID: 21206318.

3. Leritz E.C., Salat D.H., Williams V.J., Schnyer D.M., Rudolph J.L., Lipsitz L., Fischl B., McGlinchey R.E., Milberg W.P. Thickness of the Human Cerebral Cortex is Associated with Metrics of Cerebrovascular Health in a Normative Sample of Community Dwelling Older Adults. Neuroimage. 2011 Feb 14;54(4):2659-71. PMID: 21035552. PMCID: PMC3026290.

4. Salat D.H., Chen J.J., Van der Kouwe A.J.W., Greve D.N., Fischl B., Rosas H.D. Hippocampal Degeneration is Associated with Temporal and Limbic Gray Matter/White Matter Tissue Contrast in Alzheimer's Disease. Neuroimage. 2011 Feb 1;54(3):1795-802. PMID: 20965261. PMCID: PMC3021138.

5. Wang X., Grimson W.E.L., Westin C-F. Tractography Segmentation using a Hierarchical Dirichlet Processes Mixture Model. Neuroimage. 2011 Jan 1;54(1):290-302. PMID: 20678578. PMCID: PMC2962770.

6. Oguro S., Tuncali K., Elhawary H., Morrison P.R., Hata N., Silverman S.G. Image Registration of Pre-procedural MRI and Intra-procedural CT Images to Aid CT-guided Percutaneous Cryoablation of Renal Tumors. Int J Comput Assist Radiol Surg. 2011 Jan;6(1):111-7. PMID: 20499194. PMCID: PMC3050046.

7. Mohan V., Sundaramoorthi G., Tannenbaum A. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery. IEEE Trans Med Imaging. 2010 Dec;29(12):1945-58. PMID: 21118754.

8. Langs G., Golland P., Tie Y., Rigolo L., Golby A.J. Functional Geometry Alignment and Localization of Brain Areas. Proceedings of the 24th Annual Conference on Neural Information Processing Systems 2010; 1:1225-1233.

9. Shenton M.E., Whitford T.J., Kubicki M. Structural Neuroimaging in Schizophrenia: From Methods to Insights to Treatments. Dialogues Clin Neurosci. 2010;12(3):317-32. PMID: 20954428.

10. Nho K., Shen L., Kim S., Risacher S.L., West J.D., Foroud T., Jack C.R., Weiner M.W., Saykin A.J. Automatic Prediction of Conversion from Mild Cognitive Impairment to Probable Alzheimer's Disease using Structural Magnetic Resonance Imaging. AMIA Annu Symp Proc. 2010 Nov 13;2010:542-6. PMID: 21347037. PMCID: PMC3041374.

11. Kikinis Z., Fallon J.H., Niznikiewicz M., Nestor P., Davidson C., Bobrow L., Pelavin P.E., Fischl B., Yendiki A., McCarley R.W., Kikinis R., Kubicki M., Shenton M.E. Gray Matter Volume Reduction in Rostral Middle Frontal Gyrus in Patients with Chronic Schizophrenia. Schizophr Res. 2010 Nov;123(2-3):153-9. PMID: 20822884. PMCID: PMC2975427.

12. Kremen W.S., O'Brien R.C., Panizzon M.S., Prom-Wormley E., Eaves L.J., Eisen S.A., Eyler L.T., Hauger R.L., Fennema-Notestine C., Fischl B., Grant M.D., Hellhammer D.H., Jak A.J., Jacobson K.C., Jernigan T.L., Lupien S.J., Lyons M.J., Mendoza S.P., Neale M.C., Seidman L.J., Thermenos H.W., Tsuang M.T., Dale A.M., Franz C.E. Salivary Cortisol and Prefrontal Cortical Thickness in Middle-aged Men: A Twin Study. Neuroimage. 2010 Nov 15;53(3):1093-102. PMID: 20156572.

13. Elhawary H., Oguro S., Tuncali K., Morrison P.R., Tatli S., Shyn P.B., Silverman S.G., Hata N. Multimodality Non-rigid Image Registration for Planning, Targeting and Monitoring during CT-guided Percutaneous Liver Tumor Cryoablation. Acad Radiol. 2010 Nov;17(11):1334-44. PMID: 20817574. PMCID: PMC2952665.

14. Gerber S., Bremer P-T., Pascucci V., Whitaker R. Visual Exploration of High Dimensional Scalar Functions. IEEE Trans Vis Comput Graph. 2010 Nov-Dec;16(6):1271-80. PMID: 20975167.

15. Ghosh S.S., Kakunoori S., Augustinack J., Nieto-Castanon A., Kovelman I., Gaab N., Christodoulou J.A., Triantafyllou C., Gabrieli J.D.E., Fischl B. Evaluating the Validity of Volume-based and Surface-based Brain Image Registration for Developmental Cognitive Neuroscience Studies in Children 4 to 11 Years of Age. Neuroimage. 2010 Oct 15;53(1):85-93. PMID: 20621657. PMCID: PMC2914629.

16. Destrieux C., Fischl B., Dale A., Halgren E. Automatic Parcellation of Human Cortical Gyri and Sulci using Standard Anatomical Nomenclature. Neuroimage. 2010 Oct 15;53(1):1-15. PMID: 20547229. PMCID: PMC2937159.

17. Riklin-Raviv T., Van Leemput K., Menze B.H., Wells III W.M., Golland P. Segmentation of Image Ensembles via Latent Atlases. Med Image Anal. 2010 Oct;14(5):654-65. PMID: 20580305. PMCID: PMC2932709.

18. Sabuncu M.R., Yeo B.T.T., Van Leemput K., Fischl B., Golland P. A Generative Model for Image Segmentation Based on Label Fusion. IEEE Trans Med Imaging. 2010 Oct;29(10):1714-29. PMID: 20562040.

19. Gerber S., Tasdizen T., Thomas Fletcher P., Joshi S., Whitaker R. Manifold Modeling for Brain Population Analysis. Med Image Anal. 2010 Oct;14(5):643-53. PMID: 20579930. PMCID: PMC3020141.

20. Gao Y., Sandhu R., Fichtinger G., Tannenbaum A.R. A Coupled Global Registration and Segmentation Framework with Application to Magnetic Resonance Prostate Imagery. IEEE Trans Med Imaging. 2010 Oct;29(10):1781-94. PMID: 20529727. PMCID: PMC2988404.

==EAB REPORT (Lorensen)==

*Bill expects to complete this by Friday, April, 8, 2011.

2011 Scientific Progress Report

2011-04-08T17:17:12Z

Medcomparts: /* ARRA SUPPLEMENTS: Quality Assurance */

=Report Outline=

==Introduction==

==Highlights (Will Schroeder)==

*Advanced algorithms

*NA-MIC Kit

*Table of Downloads

*Outreach and Technology Transfer

==Impact (Jim Miller)==

*Impact within the Center

*Impact within NIH-Funded Research

*National and International Impact

==DBPs==
===Atrial Fibrillation, Rob MacLeod===

===Huntington's Disease, Hans Johnson===

===Head and Neck Cancer, Greg Sharp===

Submitted.

===Traumatic Brain Injury, Jack Van Horn===

Submitted.

==CS Core==

===Algorithms (Ross)===

*Statistical Models of Anatomy and Pathology, Polina Golland

*Geometric Correspondence, Guido Gerig

*User Interactive Tools for Segmentation, Allen Tannenbaum

*Longitudinal and Time Series Analysis, Martin Styner

===NA-MIC Kit (Will)===

==ARRA SUPPLEMENTS: Quality Assurance==

==Publications==
1. Wang Y., Gupta A., Liu Z., Zhang H., Escolar M.L., Gilmore J.H., Gouttard S., Fillard P., Maltbie E., Gerig G., Styner M. DTI Registration in Atlas Based Fiber Analysis of Infantile Krabbe Disease. Neuroimage. 2011 Apr 15;55(4):1577-86. PMID: 21256236. PMCID: PMC3062693.

2. Pohl K.M., Konukoglu E., Novellas S., Ayache N., Fedorov A., Talos I-F., Golby A., Wells III W.M., Kikinis R., Black P.M. A New Metric for Detecting Change in Slowly Evolving Brain Tumors: Validation in Meningioma Patients. Neurosurgery. 2011 Mar;68(1 Suppl Operative):225-33. PMID: 21206318.

3. Leritz E.C., Salat D.H., Williams V.J., Schnyer D.M., Rudolph J.L., Lipsitz L., Fischl B., McGlinchey R.E., Milberg W.P. Thickness of the Human Cerebral Cortex is Associated with Metrics of Cerebrovascular Health in a Normative Sample of Community Dwelling Older Adults. Neuroimage. 2011 Feb 14;54(4):2659-71. PMID: 21035552. PMCID: PMC3026290.

4. Salat D.H., Chen J.J., Van der Kouwe A.J.W., Greve D.N., Fischl B., Rosas H.D. Hippocampal Degeneration is Associated with Temporal and Limbic Gray Matter/White Matter Tissue Contrast in Alzheimer's Disease. Neuroimage. 2011 Feb 1;54(3):1795-802. PMID: 20965261. PMCID: PMC3021138.

5. Wang X., Grimson W.E.L., Westin C-F. Tractography Segmentation using a Hierarchical Dirichlet Processes Mixture Model. Neuroimage. 2011 Jan 1;54(1):290-302. PMID: 20678578. PMCID: PMC2962770.

6. Oguro S., Tuncali K., Elhawary H., Morrison P.R., Hata N., Silverman S.G. Image Registration of Pre-procedural MRI and Intra-procedural CT Images to Aid CT-guided Percutaneous Cryoablation of Renal Tumors. Int J Comput Assist Radiol Surg. 2011 Jan;6(1):111-7. PMID: 20499194. PMCID: PMC3050046.

7. Mohan V., Sundaramoorthi G., Tannenbaum A. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery. IEEE Trans Med Imaging. 2010 Dec;29(12):1945-58. PMID: 21118754.

8. Langs G., Golland P., Tie Y., Rigolo L., Golby A.J. Functional Geometry Alignment and Localization of Brain Areas. Proceedings of the 24th Annual Conference on Neural Information Processing Systems 2010; 1:1225-1233.

9. Shenton M.E., Whitford T.J., Kubicki M. Structural Neuroimaging in Schizophrenia: From Methods to Insights to Treatments. Dialogues Clin Neurosci. 2010;12(3):317-32. PMID: 20954428.

10. Nho K., Shen L., Kim S., Risacher S.L., West J.D., Foroud T., Jack C.R., Weiner M.W., Saykin A.J. Automatic Prediction of Conversion from Mild Cognitive Impairment to Probable Alzheimer's Disease using Structural Magnetic Resonance Imaging. AMIA Annu Symp Proc. 2010 Nov 13;2010:542-6. PMID: 21347037. PMCID: PMC3041374.

11. Kikinis Z., Fallon J.H., Niznikiewicz M., Nestor P., Davidson C., Bobrow L., Pelavin P.E., Fischl B., Yendiki A., McCarley R.W., Kikinis R., Kubicki M., Shenton M.E. Gray Matter Volume Reduction in Rostral Middle Frontal Gyrus in Patients with Chronic Schizophrenia. Schizophr Res. 2010 Nov;123(2-3):153-9. PMID: 20822884. PMCID: PMC2975427.

12. Kremen W.S., O'Brien R.C., Panizzon M.S., Prom-Wormley E., Eaves L.J., Eisen S.A., Eyler L.T., Hauger R.L., Fennema-Notestine C., Fischl B., Grant M.D., Hellhammer D.H., Jak A.J., Jacobson K.C., Jernigan T.L., Lupien S.J., Lyons M.J., Mendoza S.P., Neale M.C., Seidman L.J., Thermenos H.W., Tsuang M.T., Dale A.M., Franz C.E. Salivary Cortisol and Prefrontal Cortical Thickness in Middle-aged Men: A Twin Study. Neuroimage. 2010 Nov 15;53(3):1093-102. PMID: 20156572.

13. Elhawary H., Oguro S., Tuncali K., Morrison P.R., Tatli S., Shyn P.B., Silverman S.G., Hata N. Multimodality Non-rigid Image Registration for Planning, Targeting and Monitoring during CT-guided Percutaneous Liver Tumor Cryoablation. Acad Radiol. 2010 Nov;17(11):1334-44. PMID: 20817574. PMCID: PMC2952665.

14. Gerber S., Bremer P-T., Pascucci V., Whitaker R. Visual Exploration of High Dimensional Scalar Functions. IEEE Trans Vis Comput Graph. 2010 Nov-Dec;16(6):1271-80. PMID: 20975167.

15. Ghosh S.S., Kakunoori S., Augustinack J., Nieto-Castanon A., Kovelman I., Gaab N., Christodoulou J.A., Triantafyllou C., Gabrieli J.D.E., Fischl B. Evaluating the Validity of Volume-based and Surface-based Brain Image Registration for Developmental Cognitive Neuroscience Studies in Children 4 to 11 Years of Age. Neuroimage. 2010 Oct 15;53(1):85-93. PMID: 20621657. PMCID: PMC2914629.

16. Destrieux C., Fischl B., Dale A., Halgren E. Automatic Parcellation of Human Cortical Gyri and Sulci using Standard Anatomical Nomenclature. Neuroimage. 2010 Oct 15;53(1):1-15. PMID: 20547229. PMCID: PMC2937159.

17. Riklin-Raviv T., Van Leemput K., Menze B.H., Wells III W.M., Golland P. Segmentation of Image Ensembles via Latent Atlases. Med Image Anal. 2010 Oct;14(5):654-65. PMID: 20580305. PMCID: PMC2932709.

18. Sabuncu M.R., Yeo B.T.T., Van Leemput K., Fischl B., Golland P. A Generative Model for Image Segmentation Based on Label Fusion. IEEE Trans Med Imaging. 2010 Oct;29(10):1714-29. PMID: 20562040.

19. Gerber S., Tasdizen T., Thomas Fletcher P., Joshi S., Whitaker R. Manifold Modeling for Brain Population Analysis. Med Image Anal. 2010 Oct;14(5):643-53. PMID: 20579930. PMCID: PMC3020141.

20. Gao Y., Sandhu R., Fichtinger G., Tannenbaum A.R. A Coupled Global Registration and Segmentation Framework with Application to Magnetic Resonance Prostate Imagery. IEEE Trans Med Imaging. 2010 Oct;29(10):1781-94. PMID: 20529727. PMCID: PMC2988404.

==EAB REPORT (Lorensen)==

*Bill expects to complete this by Friday, April, 8, 2011.

2011 Scientific Progress Report

2011-04-08T17:10:56Z

Medcomparts: /* ARRA SUPPLEMENTS */

=Report Outline=

==Introduction==

==Highlights (Will Schroeder)==

*Advanced algorithms

*NA-MIC Kit

*Table of Downloads

*Outreach and Technology Transfer

==Impact (Jim Miller)==

*Impact within the Center

*Impact within NIH-Funded Research

*National and International Impact

==DBPs==
===Atrial Fibrillation, Rob MacLeod===

===Huntington's Disease, Hans Johnson===

===Head and Neck Cancer, Greg Sharp===

Submitted.

===Traumatic Brain Injury, Jack Van Horn===

Submitted.

==CS Core==

===Algorithms (Ross)===

*Statistical Models of Anatomy and Pathology, Polina Golland

*Geometric Correspondence, Guido Gerig

*User Interactive Tools for Segmentation, Allen Tannenbaum

*Longitudinal and Time Series Analysis, Martin Styner

===NA-MIC Kit (Will)===

==ARRA SUPPLEMENTS: Quality Assurance==
[http://www.na-mic.org/Wiki/index.php/CTSC:ARRA.030111]

==Publications==
1. Wang Y., Gupta A., Liu Z., Zhang H., Escolar M.L., Gilmore J.H., Gouttard S., Fillard P., Maltbie E., Gerig G., Styner M. DTI Registration in Atlas Based Fiber Analysis of Infantile Krabbe Disease. Neuroimage. 2011 Apr 15;55(4):1577-86. PMID: 21256236. PMCID: PMC3062693.

2. Pohl K.M., Konukoglu E., Novellas S., Ayache N., Fedorov A., Talos I-F., Golby A., Wells III W.M., Kikinis R., Black P.M. A New Metric for Detecting Change in Slowly Evolving Brain Tumors: Validation in Meningioma Patients. Neurosurgery. 2011 Mar;68(1 Suppl Operative):225-33. PMID: 21206318.

3. Leritz E.C., Salat D.H., Williams V.J., Schnyer D.M., Rudolph J.L., Lipsitz L., Fischl B., McGlinchey R.E., Milberg W.P. Thickness of the Human Cerebral Cortex is Associated with Metrics of Cerebrovascular Health in a Normative Sample of Community Dwelling Older Adults. Neuroimage. 2011 Feb 14;54(4):2659-71. PMID: 21035552. PMCID: PMC3026290.

4. Salat D.H., Chen J.J., Van der Kouwe A.J.W., Greve D.N., Fischl B., Rosas H.D. Hippocampal Degeneration is Associated with Temporal and Limbic Gray Matter/White Matter Tissue Contrast in Alzheimer's Disease. Neuroimage. 2011 Feb 1;54(3):1795-802. PMID: 20965261. PMCID: PMC3021138.

5. Wang X., Grimson W.E.L., Westin C-F. Tractography Segmentation using a Hierarchical Dirichlet Processes Mixture Model. Neuroimage. 2011 Jan 1;54(1):290-302. PMID: 20678578. PMCID: PMC2962770.

6. Oguro S., Tuncali K., Elhawary H., Morrison P.R., Hata N., Silverman S.G. Image Registration of Pre-procedural MRI and Intra-procedural CT Images to Aid CT-guided Percutaneous Cryoablation of Renal Tumors. Int J Comput Assist Radiol Surg. 2011 Jan;6(1):111-7. PMID: 20499194. PMCID: PMC3050046.

7. Mohan V., Sundaramoorthi G., Tannenbaum A. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery. IEEE Trans Med Imaging. 2010 Dec;29(12):1945-58. PMID: 21118754.

8. Langs G., Golland P., Tie Y., Rigolo L., Golby A.J. Functional Geometry Alignment and Localization of Brain Areas. Proceedings of the 24th Annual Conference on Neural Information Processing Systems 2010; 1:1225-1233.

9. Shenton M.E., Whitford T.J., Kubicki M. Structural Neuroimaging in Schizophrenia: From Methods to Insights to Treatments. Dialogues Clin Neurosci. 2010;12(3):317-32. PMID: 20954428.

10. Nho K., Shen L., Kim S., Risacher S.L., West J.D., Foroud T., Jack C.R., Weiner M.W., Saykin A.J. Automatic Prediction of Conversion from Mild Cognitive Impairment to Probable Alzheimer's Disease using Structural Magnetic Resonance Imaging. AMIA Annu Symp Proc. 2010 Nov 13;2010:542-6. PMID: 21347037. PMCID: PMC3041374.

11. Kikinis Z., Fallon J.H., Niznikiewicz M., Nestor P., Davidson C., Bobrow L., Pelavin P.E., Fischl B., Yendiki A., McCarley R.W., Kikinis R., Kubicki M., Shenton M.E. Gray Matter Volume Reduction in Rostral Middle Frontal Gyrus in Patients with Chronic Schizophrenia. Schizophr Res. 2010 Nov;123(2-3):153-9. PMID: 20822884. PMCID: PMC2975427.

12. Kremen W.S., O'Brien R.C., Panizzon M.S., Prom-Wormley E., Eaves L.J., Eisen S.A., Eyler L.T., Hauger R.L., Fennema-Notestine C., Fischl B., Grant M.D., Hellhammer D.H., Jak A.J., Jacobson K.C., Jernigan T.L., Lupien S.J., Lyons M.J., Mendoza S.P., Neale M.C., Seidman L.J., Thermenos H.W., Tsuang M.T., Dale A.M., Franz C.E. Salivary Cortisol and Prefrontal Cortical Thickness in Middle-aged Men: A Twin Study. Neuroimage. 2010 Nov 15;53(3):1093-102. PMID: 20156572.

13. Elhawary H., Oguro S., Tuncali K., Morrison P.R., Tatli S., Shyn P.B., Silverman S.G., Hata N. Multimodality Non-rigid Image Registration for Planning, Targeting and Monitoring during CT-guided Percutaneous Liver Tumor Cryoablation. Acad Radiol. 2010 Nov;17(11):1334-44. PMID: 20817574. PMCID: PMC2952665.

14. Gerber S., Bremer P-T., Pascucci V., Whitaker R. Visual Exploration of High Dimensional Scalar Functions. IEEE Trans Vis Comput Graph. 2010 Nov-Dec;16(6):1271-80. PMID: 20975167.

15. Ghosh S.S., Kakunoori S., Augustinack J., Nieto-Castanon A., Kovelman I., Gaab N., Christodoulou J.A., Triantafyllou C., Gabrieli J.D.E., Fischl B. Evaluating the Validity of Volume-based and Surface-based Brain Image Registration for Developmental Cognitive Neuroscience Studies in Children 4 to 11 Years of Age. Neuroimage. 2010 Oct 15;53(1):85-93. PMID: 20621657. PMCID: PMC2914629.

16. Destrieux C., Fischl B., Dale A., Halgren E. Automatic Parcellation of Human Cortical Gyri and Sulci using Standard Anatomical Nomenclature. Neuroimage. 2010 Oct 15;53(1):1-15. PMID: 20547229. PMCID: PMC2937159.

17. Riklin-Raviv T., Van Leemput K., Menze B.H., Wells III W.M., Golland P. Segmentation of Image Ensembles via Latent Atlases. Med Image Anal. 2010 Oct;14(5):654-65. PMID: 20580305. PMCID: PMC2932709.

18. Sabuncu M.R., Yeo B.T.T., Van Leemput K., Fischl B., Golland P. A Generative Model for Image Segmentation Based on Label Fusion. IEEE Trans Med Imaging. 2010 Oct;29(10):1714-29. PMID: 20562040.

19. Gerber S., Tasdizen T., Thomas Fletcher P., Joshi S., Whitaker R. Manifold Modeling for Brain Population Analysis. Med Image Anal. 2010 Oct;14(5):643-53. PMID: 20579930. PMCID: PMC3020141.

20. Gao Y., Sandhu R., Fichtinger G., Tannenbaum A.R. A Coupled Global Registration and Segmentation Framework with Application to Magnetic Resonance Prostate Imagery. IEEE Trans Med Imaging. 2010 Oct;29(10):1781-94. PMID: 20529727. PMCID: PMC2988404.

==EAB REPORT (Lorensen)==

*Bill expects to complete this by Friday, April, 8, 2011.

2011 Scientific Progress Report

2011-04-08T17:08:59Z

Medcomparts: /* ARRA SUPPLEMENTS */

=Report Outline=

==Introduction==

==Highlights (Will Schroeder)==

*Advanced algorithms

*NA-MIC Kit

*Table of Downloads

*Outreach and Technology Transfer

==Impact (Jim Miller)==

*Impact within the Center

*Impact within NIH-Funded Research

*National and International Impact

==DBPs==
===Atrial Fibrillation, Rob MacLeod===

===Huntington's Disease, Hans Johnson===

===Head and Neck Cancer, Greg Sharp===

Submitted.

===Traumatic Brain Injury, Jack Van Horn===

Submitted.

==CS Core==

===Algorithms (Ross)===

*Statistical Models of Anatomy and Pathology, Polina Golland

*Geometric Correspondence, Guido Gerig

*User Interactive Tools for Segmentation, Allen Tannenbaum

*Longitudinal and Time Series Analysis, Martin Styner

===NA-MIC Kit (Will)===

==ARRA SUPPLEMENTS==
[http://www.na-mic.org/Wiki/index.php/CTSC:ARRA.030111]

==Publications==
1. Wang Y., Gupta A., Liu Z., Zhang H., Escolar M.L., Gilmore J.H., Gouttard S., Fillard P., Maltbie E., Gerig G., Styner M. DTI Registration in Atlas Based Fiber Analysis of Infantile Krabbe Disease. Neuroimage. 2011 Apr 15;55(4):1577-86. PMID: 21256236. PMCID: PMC3062693.

2. Pohl K.M., Konukoglu E., Novellas S., Ayache N., Fedorov A., Talos I-F., Golby A., Wells III W.M., Kikinis R., Black P.M. A New Metric for Detecting Change in Slowly Evolving Brain Tumors: Validation in Meningioma Patients. Neurosurgery. 2011 Mar;68(1 Suppl Operative):225-33. PMID: 21206318.

3. Leritz E.C., Salat D.H., Williams V.J., Schnyer D.M., Rudolph J.L., Lipsitz L., Fischl B., McGlinchey R.E., Milberg W.P. Thickness of the Human Cerebral Cortex is Associated with Metrics of Cerebrovascular Health in a Normative Sample of Community Dwelling Older Adults. Neuroimage. 2011 Feb 14;54(4):2659-71. PMID: 21035552. PMCID: PMC3026290.

4. Salat D.H., Chen J.J., Van der Kouwe A.J.W., Greve D.N., Fischl B., Rosas H.D. Hippocampal Degeneration is Associated with Temporal and Limbic Gray Matter/White Matter Tissue Contrast in Alzheimer's Disease. Neuroimage. 2011 Feb 1;54(3):1795-802. PMID: 20965261. PMCID: PMC3021138.

5. Wang X., Grimson W.E.L., Westin C-F. Tractography Segmentation using a Hierarchical Dirichlet Processes Mixture Model. Neuroimage. 2011 Jan 1;54(1):290-302. PMID: 20678578. PMCID: PMC2962770.

6. Oguro S., Tuncali K., Elhawary H., Morrison P.R., Hata N., Silverman S.G. Image Registration of Pre-procedural MRI and Intra-procedural CT Images to Aid CT-guided Percutaneous Cryoablation of Renal Tumors. Int J Comput Assist Radiol Surg. 2011 Jan;6(1):111-7. PMID: 20499194. PMCID: PMC3050046.

7. Mohan V., Sundaramoorthi G., Tannenbaum A. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery. IEEE Trans Med Imaging. 2010 Dec;29(12):1945-58. PMID: 21118754.

8. Langs G., Golland P., Tie Y., Rigolo L., Golby A.J. Functional Geometry Alignment and Localization of Brain Areas. Proceedings of the 24th Annual Conference on Neural Information Processing Systems 2010; 1:1225-1233.

9. Shenton M.E., Whitford T.J., Kubicki M. Structural Neuroimaging in Schizophrenia: From Methods to Insights to Treatments. Dialogues Clin Neurosci. 2010;12(3):317-32. PMID: 20954428.

10. Nho K., Shen L., Kim S., Risacher S.L., West J.D., Foroud T., Jack C.R., Weiner M.W., Saykin A.J. Automatic Prediction of Conversion from Mild Cognitive Impairment to Probable Alzheimer's Disease using Structural Magnetic Resonance Imaging. AMIA Annu Symp Proc. 2010 Nov 13;2010:542-6. PMID: 21347037. PMCID: PMC3041374.

11. Kikinis Z., Fallon J.H., Niznikiewicz M., Nestor P., Davidson C., Bobrow L., Pelavin P.E., Fischl B., Yendiki A., McCarley R.W., Kikinis R., Kubicki M., Shenton M.E. Gray Matter Volume Reduction in Rostral Middle Frontal Gyrus in Patients with Chronic Schizophrenia. Schizophr Res. 2010 Nov;123(2-3):153-9. PMID: 20822884. PMCID: PMC2975427.

12. Kremen W.S., O'Brien R.C., Panizzon M.S., Prom-Wormley E., Eaves L.J., Eisen S.A., Eyler L.T., Hauger R.L., Fennema-Notestine C., Fischl B., Grant M.D., Hellhammer D.H., Jak A.J., Jacobson K.C., Jernigan T.L., Lupien S.J., Lyons M.J., Mendoza S.P., Neale M.C., Seidman L.J., Thermenos H.W., Tsuang M.T., Dale A.M., Franz C.E. Salivary Cortisol and Prefrontal Cortical Thickness in Middle-aged Men: A Twin Study. Neuroimage. 2010 Nov 15;53(3):1093-102. PMID: 20156572.

13. Elhawary H., Oguro S., Tuncali K., Morrison P.R., Tatli S., Shyn P.B., Silverman S.G., Hata N. Multimodality Non-rigid Image Registration for Planning, Targeting and Monitoring during CT-guided Percutaneous Liver Tumor Cryoablation. Acad Radiol. 2010 Nov;17(11):1334-44. PMID: 20817574. PMCID: PMC2952665.

14. Gerber S., Bremer P-T., Pascucci V., Whitaker R. Visual Exploration of High Dimensional Scalar Functions. IEEE Trans Vis Comput Graph. 2010 Nov-Dec;16(6):1271-80. PMID: 20975167.

15. Ghosh S.S., Kakunoori S., Augustinack J., Nieto-Castanon A., Kovelman I., Gaab N., Christodoulou J.A., Triantafyllou C., Gabrieli J.D.E., Fischl B. Evaluating the Validity of Volume-based and Surface-based Brain Image Registration for Developmental Cognitive Neuroscience Studies in Children 4 to 11 Years of Age. Neuroimage. 2010 Oct 15;53(1):85-93. PMID: 20621657. PMCID: PMC2914629.

16. Destrieux C., Fischl B., Dale A., Halgren E. Automatic Parcellation of Human Cortical Gyri and Sulci using Standard Anatomical Nomenclature. Neuroimage. 2010 Oct 15;53(1):1-15. PMID: 20547229. PMCID: PMC2937159.

17. Riklin-Raviv T., Van Leemput K., Menze B.H., Wells III W.M., Golland P. Segmentation of Image Ensembles via Latent Atlases. Med Image Anal. 2010 Oct;14(5):654-65. PMID: 20580305. PMCID: PMC2932709.

18. Sabuncu M.R., Yeo B.T.T., Van Leemput K., Fischl B., Golland P. A Generative Model for Image Segmentation Based on Label Fusion. IEEE Trans Med Imaging. 2010 Oct;29(10):1714-29. PMID: 20562040.

19. Gerber S., Tasdizen T., Thomas Fletcher P., Joshi S., Whitaker R. Manifold Modeling for Brain Population Analysis. Med Image Anal. 2010 Oct;14(5):643-53. PMID: 20579930. PMCID: PMC3020141.

20. Gao Y., Sandhu R., Fichtinger G., Tannenbaum A.R. A Coupled Global Registration and Segmentation Framework with Application to Magnetic Resonance Prostate Imagery. IEEE Trans Med Imaging. 2010 Oct;29(10):1781-94. PMID: 20529727. PMCID: PMC2988404.

==EAB REPORT (Lorensen)==

*Bill expects to complete this by Friday, April, 8, 2011.

2011 Scientific Progress Report

2011-04-08T17:04:35Z

Medcomparts: /* Highlights (Will) */

=Report Outline=

==Introduction==

==Highlights (Will Schroeder)==

*Advanced algorithms

*NA-MIC Kit

*Table of Downloads

*Outreach and Technology Transfer

==Impact (Jim Miller)==

*Impact within the Center

*Impact within NIH-Funded Research

*National and International Impact

==DBPs==
===Atrial Fibrillation, Rob MacLeod===

===Huntington's Disease, Hans Johnson===

===Head and Neck Cancer, Greg Sharp===

Submitted.

===Traumatic Brain Injury, Jack Van Horn===

Submitted.

==CS Core==

===Algorithms (Ross)===

*Statistical Models of Anatomy and Pathology, Polina Golland

*Geometric Correspondence, Guido Gerig

*User Interactive Tools for Segmentation, Allen Tannenbaum

*Longitudinal and Time Series Analysis, Martin Styner

===NA-MIC Kit (Will)===

==ARRA SUPPLEMENTS==

==Publications==
1. Wang Y., Gupta A., Liu Z., Zhang H., Escolar M.L., Gilmore J.H., Gouttard S., Fillard P., Maltbie E., Gerig G., Styner M. DTI Registration in Atlas Based Fiber Analysis of Infantile Krabbe Disease. Neuroimage. 2011 Apr 15;55(4):1577-86. PMID: 21256236. PMCID: PMC3062693.

2. Pohl K.M., Konukoglu E., Novellas S., Ayache N., Fedorov A., Talos I-F., Golby A., Wells III W.M., Kikinis R., Black P.M. A New Metric for Detecting Change in Slowly Evolving Brain Tumors: Validation in Meningioma Patients. Neurosurgery. 2011 Mar;68(1 Suppl Operative):225-33. PMID: 21206318.

3. Leritz E.C., Salat D.H., Williams V.J., Schnyer D.M., Rudolph J.L., Lipsitz L., Fischl B., McGlinchey R.E., Milberg W.P. Thickness of the Human Cerebral Cortex is Associated with Metrics of Cerebrovascular Health in a Normative Sample of Community Dwelling Older Adults. Neuroimage. 2011 Feb 14;54(4):2659-71. PMID: 21035552. PMCID: PMC3026290.

4. Salat D.H., Chen J.J., Van der Kouwe A.J.W., Greve D.N., Fischl B., Rosas H.D. Hippocampal Degeneration is Associated with Temporal and Limbic Gray Matter/White Matter Tissue Contrast in Alzheimer's Disease. Neuroimage. 2011 Feb 1;54(3):1795-802. PMID: 20965261. PMCID: PMC3021138.

5. Wang X., Grimson W.E.L., Westin C-F. Tractography Segmentation using a Hierarchical Dirichlet Processes Mixture Model. Neuroimage. 2011 Jan 1;54(1):290-302. PMID: 20678578. PMCID: PMC2962770.

6. Oguro S., Tuncali K., Elhawary H., Morrison P.R., Hata N., Silverman S.G. Image Registration of Pre-procedural MRI and Intra-procedural CT Images to Aid CT-guided Percutaneous Cryoablation of Renal Tumors. Int J Comput Assist Radiol Surg. 2011 Jan;6(1):111-7. PMID: 20499194. PMCID: PMC3050046.

7. Mohan V., Sundaramoorthi G., Tannenbaum A. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery. IEEE Trans Med Imaging. 2010 Dec;29(12):1945-58. PMID: 21118754.

8. Langs G., Golland P., Tie Y., Rigolo L., Golby A.J. Functional Geometry Alignment and Localization of Brain Areas. Proceedings of the 24th Annual Conference on Neural Information Processing Systems 2010; 1:1225-1233.

9. Shenton M.E., Whitford T.J., Kubicki M. Structural Neuroimaging in Schizophrenia: From Methods to Insights to Treatments. Dialogues Clin Neurosci. 2010;12(3):317-32. PMID: 20954428.

10. Nho K., Shen L., Kim S., Risacher S.L., West J.D., Foroud T., Jack C.R., Weiner M.W., Saykin A.J. Automatic Prediction of Conversion from Mild Cognitive Impairment to Probable Alzheimer's Disease using Structural Magnetic Resonance Imaging. AMIA Annu Symp Proc. 2010 Nov 13;2010:542-6. PMID: 21347037. PMCID: PMC3041374.

11. Kikinis Z., Fallon J.H., Niznikiewicz M., Nestor P., Davidson C., Bobrow L., Pelavin P.E., Fischl B., Yendiki A., McCarley R.W., Kikinis R., Kubicki M., Shenton M.E. Gray Matter Volume Reduction in Rostral Middle Frontal Gyrus in Patients with Chronic Schizophrenia. Schizophr Res. 2010 Nov;123(2-3):153-9. PMID: 20822884. PMCID: PMC2975427.

12. Kremen W.S., O'Brien R.C., Panizzon M.S., Prom-Wormley E., Eaves L.J., Eisen S.A., Eyler L.T., Hauger R.L., Fennema-Notestine C., Fischl B., Grant M.D., Hellhammer D.H., Jak A.J., Jacobson K.C., Jernigan T.L., Lupien S.J., Lyons M.J., Mendoza S.P., Neale M.C., Seidman L.J., Thermenos H.W., Tsuang M.T., Dale A.M., Franz C.E. Salivary Cortisol and Prefrontal Cortical Thickness in Middle-aged Men: A Twin Study. Neuroimage. 2010 Nov 15;53(3):1093-102. PMID: 20156572.

13. Elhawary H., Oguro S., Tuncali K., Morrison P.R., Tatli S., Shyn P.B., Silverman S.G., Hata N. Multimodality Non-rigid Image Registration for Planning, Targeting and Monitoring during CT-guided Percutaneous Liver Tumor Cryoablation. Acad Radiol. 2010 Nov;17(11):1334-44. PMID: 20817574. PMCID: PMC2952665.

14. Gerber S., Bremer P-T., Pascucci V., Whitaker R. Visual Exploration of High Dimensional Scalar Functions. IEEE Trans Vis Comput Graph. 2010 Nov-Dec;16(6):1271-80. PMID: 20975167.

15. Ghosh S.S., Kakunoori S., Augustinack J., Nieto-Castanon A., Kovelman I., Gaab N., Christodoulou J.A., Triantafyllou C., Gabrieli J.D.E., Fischl B. Evaluating the Validity of Volume-based and Surface-based Brain Image Registration for Developmental Cognitive Neuroscience Studies in Children 4 to 11 Years of Age. Neuroimage. 2010 Oct 15;53(1):85-93. PMID: 20621657. PMCID: PMC2914629.

16. Destrieux C., Fischl B., Dale A., Halgren E. Automatic Parcellation of Human Cortical Gyri and Sulci using Standard Anatomical Nomenclature. Neuroimage. 2010 Oct 15;53(1):1-15. PMID: 20547229. PMCID: PMC2937159.

17. Riklin-Raviv T., Van Leemput K., Menze B.H., Wells III W.M., Golland P. Segmentation of Image Ensembles via Latent Atlases. Med Image Anal. 2010 Oct;14(5):654-65. PMID: 20580305. PMCID: PMC2932709.

18. Sabuncu M.R., Yeo B.T.T., Van Leemput K., Fischl B., Golland P. A Generative Model for Image Segmentation Based on Label Fusion. IEEE Trans Med Imaging. 2010 Oct;29(10):1714-29. PMID: 20562040.

19. Gerber S., Tasdizen T., Thomas Fletcher P., Joshi S., Whitaker R. Manifold Modeling for Brain Population Analysis. Med Image Anal. 2010 Oct;14(5):643-53. PMID: 20579930. PMCID: PMC3020141.

20. Gao Y., Sandhu R., Fichtinger G., Tannenbaum A.R. A Coupled Global Registration and Segmentation Framework with Application to Magnetic Resonance Prostate Imagery. IEEE Trans Med Imaging. 2010 Oct;29(10):1781-94. PMID: 20529727. PMCID: PMC2988404.

==EAB REPORT (Lorensen)==

*Bill expects to complete this by Friday, April, 8, 2011.

2011 Scientific Progress Report

2011-04-08T17:00:51Z

Medcomparts: /* Traumatic Brain Injury, Jack Van Horn */

=Report Outline=

==Introduction==

==Highlights (Will)==

*Advanced algorithms

*NA-MIC Kit

*Table of Downloads

*Outreach and Technology Transfer

==Impact (Jim Miller)==

*Impact within the Center

*Impact within NIH-Funded Research

*National and International Impact

==DBPs==
===Atrial Fibrillation, Rob MacLeod===

===Huntington's Disease, Hans Johnson===

===Head and Neck Cancer, Greg Sharp===

Submitted.

===Traumatic Brain Injury, Jack Van Horn===

Submitted.

==CS Core==

===Algorithms (Ross)===

*Statistical Models of Anatomy and Pathology, Polina Golland

*Geometric Correspondence, Guido Gerig

*User Interactive Tools for Segmentation, Allen Tannenbaum

*Longitudinal and Time Series Analysis, Martin Styner

===NA-MIC Kit (Will)===

==ARRA SUPPLEMENTS==

==Publications==
1. Wang Y., Gupta A., Liu Z., Zhang H., Escolar M.L., Gilmore J.H., Gouttard S., Fillard P., Maltbie E., Gerig G., Styner M. DTI Registration in Atlas Based Fiber Analysis of Infantile Krabbe Disease. Neuroimage. 2011 Apr 15;55(4):1577-86. PMID: 21256236. PMCID: PMC3062693.

2. Pohl K.M., Konukoglu E., Novellas S., Ayache N., Fedorov A., Talos I-F., Golby A., Wells III W.M., Kikinis R., Black P.M. A New Metric for Detecting Change in Slowly Evolving Brain Tumors: Validation in Meningioma Patients. Neurosurgery. 2011 Mar;68(1 Suppl Operative):225-33. PMID: 21206318.

3. Leritz E.C., Salat D.H., Williams V.J., Schnyer D.M., Rudolph J.L., Lipsitz L., Fischl B., McGlinchey R.E., Milberg W.P. Thickness of the Human Cerebral Cortex is Associated with Metrics of Cerebrovascular Health in a Normative Sample of Community Dwelling Older Adults. Neuroimage. 2011 Feb 14;54(4):2659-71. PMID: 21035552. PMCID: PMC3026290.

4. Salat D.H., Chen J.J., Van der Kouwe A.J.W., Greve D.N., Fischl B., Rosas H.D. Hippocampal Degeneration is Associated with Temporal and Limbic Gray Matter/White Matter Tissue Contrast in Alzheimer's Disease. Neuroimage. 2011 Feb 1;54(3):1795-802. PMID: 20965261. PMCID: PMC3021138.

5. Wang X., Grimson W.E.L., Westin C-F. Tractography Segmentation using a Hierarchical Dirichlet Processes Mixture Model. Neuroimage. 2011 Jan 1;54(1):290-302. PMID: 20678578. PMCID: PMC2962770.

6. Oguro S., Tuncali K., Elhawary H., Morrison P.R., Hata N., Silverman S.G. Image Registration of Pre-procedural MRI and Intra-procedural CT Images to Aid CT-guided Percutaneous Cryoablation of Renal Tumors. Int J Comput Assist Radiol Surg. 2011 Jan;6(1):111-7. PMID: 20499194. PMCID: PMC3050046.

7. Mohan V., Sundaramoorthi G., Tannenbaum A. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery. IEEE Trans Med Imaging. 2010 Dec;29(12):1945-58. PMID: 21118754.

8. Langs G., Golland P., Tie Y., Rigolo L., Golby A.J. Functional Geometry Alignment and Localization of Brain Areas. Proceedings of the 24th Annual Conference on Neural Information Processing Systems 2010; 1:1225-1233.

9. Shenton M.E., Whitford T.J., Kubicki M. Structural Neuroimaging in Schizophrenia: From Methods to Insights to Treatments. Dialogues Clin Neurosci. 2010;12(3):317-32. PMID: 20954428.

10. Nho K., Shen L., Kim S., Risacher S.L., West J.D., Foroud T., Jack C.R., Weiner M.W., Saykin A.J. Automatic Prediction of Conversion from Mild Cognitive Impairment to Probable Alzheimer's Disease using Structural Magnetic Resonance Imaging. AMIA Annu Symp Proc. 2010 Nov 13;2010:542-6. PMID: 21347037. PMCID: PMC3041374.

11. Kikinis Z., Fallon J.H., Niznikiewicz M., Nestor P., Davidson C., Bobrow L., Pelavin P.E., Fischl B., Yendiki A., McCarley R.W., Kikinis R., Kubicki M., Shenton M.E. Gray Matter Volume Reduction in Rostral Middle Frontal Gyrus in Patients with Chronic Schizophrenia. Schizophr Res. 2010 Nov;123(2-3):153-9. PMID: 20822884. PMCID: PMC2975427.

12. Kremen W.S., O'Brien R.C., Panizzon M.S., Prom-Wormley E., Eaves L.J., Eisen S.A., Eyler L.T., Hauger R.L., Fennema-Notestine C., Fischl B., Grant M.D., Hellhammer D.H., Jak A.J., Jacobson K.C., Jernigan T.L., Lupien S.J., Lyons M.J., Mendoza S.P., Neale M.C., Seidman L.J., Thermenos H.W., Tsuang M.T., Dale A.M., Franz C.E. Salivary Cortisol and Prefrontal Cortical Thickness in Middle-aged Men: A Twin Study. Neuroimage. 2010 Nov 15;53(3):1093-102. PMID: 20156572.

13. Elhawary H., Oguro S., Tuncali K., Morrison P.R., Tatli S., Shyn P.B., Silverman S.G., Hata N. Multimodality Non-rigid Image Registration for Planning, Targeting and Monitoring during CT-guided Percutaneous Liver Tumor Cryoablation. Acad Radiol. 2010 Nov;17(11):1334-44. PMID: 20817574. PMCID: PMC2952665.

14. Gerber S., Bremer P-T., Pascucci V., Whitaker R. Visual Exploration of High Dimensional Scalar Functions. IEEE Trans Vis Comput Graph. 2010 Nov-Dec;16(6):1271-80. PMID: 20975167.

15. Ghosh S.S., Kakunoori S., Augustinack J., Nieto-Castanon A., Kovelman I., Gaab N., Christodoulou J.A., Triantafyllou C., Gabrieli J.D.E., Fischl B. Evaluating the Validity of Volume-based and Surface-based Brain Image Registration for Developmental Cognitive Neuroscience Studies in Children 4 to 11 Years of Age. Neuroimage. 2010 Oct 15;53(1):85-93. PMID: 20621657. PMCID: PMC2914629.

16. Destrieux C., Fischl B., Dale A., Halgren E. Automatic Parcellation of Human Cortical Gyri and Sulci using Standard Anatomical Nomenclature. Neuroimage. 2010 Oct 15;53(1):1-15. PMID: 20547229. PMCID: PMC2937159.

17. Riklin-Raviv T., Van Leemput K., Menze B.H., Wells III W.M., Golland P. Segmentation of Image Ensembles via Latent Atlases. Med Image Anal. 2010 Oct;14(5):654-65. PMID: 20580305. PMCID: PMC2932709.

18. Sabuncu M.R., Yeo B.T.T., Van Leemput K., Fischl B., Golland P. A Generative Model for Image Segmentation Based on Label Fusion. IEEE Trans Med Imaging. 2010 Oct;29(10):1714-29. PMID: 20562040.

19. Gerber S., Tasdizen T., Thomas Fletcher P., Joshi S., Whitaker R. Manifold Modeling for Brain Population Analysis. Med Image Anal. 2010 Oct;14(5):643-53. PMID: 20579930. PMCID: PMC3020141.

20. Gao Y., Sandhu R., Fichtinger G., Tannenbaum A.R. A Coupled Global Registration and Segmentation Framework with Application to Magnetic Resonance Prostate Imagery. IEEE Trans Med Imaging. 2010 Oct;29(10):1781-94. PMID: 20529727. PMCID: PMC2988404.

==EAB REPORT (Lorensen)==

*Bill expects to complete this by Friday, April, 8, 2011.

2011 Scientific Progress Report

2011-04-08T17:00:33Z

Medcomparts: /* Head and Neck Cancer, Greg Sharp */

=Report Outline=

==Introduction==

==Highlights (Will)==

*Advanced algorithms

*NA-MIC Kit

*Table of Downloads

*Outreach and Technology Transfer

==Impact (Jim Miller)==

*Impact within the Center

*Impact within NIH-Funded Research

*National and International Impact

==DBPs==
===Atrial Fibrillation, Rob MacLeod===

===Huntington's Disease, Hans Johnson===

===Head and Neck Cancer, Greg Sharp===

Submitted.

===Traumatic Brain Injury, Jack Van Horn===

==CS Core==

===Algorithms (Ross)===

*Statistical Models of Anatomy and Pathology, Polina Golland

*Geometric Correspondence, Guido Gerig

*User Interactive Tools for Segmentation, Allen Tannenbaum

*Longitudinal and Time Series Analysis, Martin Styner

===NA-MIC Kit (Will)===

==ARRA SUPPLEMENTS==

==Publications==
1. Wang Y., Gupta A., Liu Z., Zhang H., Escolar M.L., Gilmore J.H., Gouttard S., Fillard P., Maltbie E., Gerig G., Styner M. DTI Registration in Atlas Based Fiber Analysis of Infantile Krabbe Disease. Neuroimage. 2011 Apr 15;55(4):1577-86. PMID: 21256236. PMCID: PMC3062693.

2. Pohl K.M., Konukoglu E., Novellas S., Ayache N., Fedorov A., Talos I-F., Golby A., Wells III W.M., Kikinis R., Black P.M. A New Metric for Detecting Change in Slowly Evolving Brain Tumors: Validation in Meningioma Patients. Neurosurgery. 2011 Mar;68(1 Suppl Operative):225-33. PMID: 21206318.

3. Leritz E.C., Salat D.H., Williams V.J., Schnyer D.M., Rudolph J.L., Lipsitz L., Fischl B., McGlinchey R.E., Milberg W.P. Thickness of the Human Cerebral Cortex is Associated with Metrics of Cerebrovascular Health in a Normative Sample of Community Dwelling Older Adults. Neuroimage. 2011 Feb 14;54(4):2659-71. PMID: 21035552. PMCID: PMC3026290.

4. Salat D.H., Chen J.J., Van der Kouwe A.J.W., Greve D.N., Fischl B., Rosas H.D. Hippocampal Degeneration is Associated with Temporal and Limbic Gray Matter/White Matter Tissue Contrast in Alzheimer's Disease. Neuroimage. 2011 Feb 1;54(3):1795-802. PMID: 20965261. PMCID: PMC3021138.

5. Wang X., Grimson W.E.L., Westin C-F. Tractography Segmentation using a Hierarchical Dirichlet Processes Mixture Model. Neuroimage. 2011 Jan 1;54(1):290-302. PMID: 20678578. PMCID: PMC2962770.

6. Oguro S., Tuncali K., Elhawary H., Morrison P.R., Hata N., Silverman S.G. Image Registration of Pre-procedural MRI and Intra-procedural CT Images to Aid CT-guided Percutaneous Cryoablation of Renal Tumors. Int J Comput Assist Radiol Surg. 2011 Jan;6(1):111-7. PMID: 20499194. PMCID: PMC3050046.

7. Mohan V., Sundaramoorthi G., Tannenbaum A. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery. IEEE Trans Med Imaging. 2010 Dec;29(12):1945-58. PMID: 21118754.

8. Langs G., Golland P., Tie Y., Rigolo L., Golby A.J. Functional Geometry Alignment and Localization of Brain Areas. Proceedings of the 24th Annual Conference on Neural Information Processing Systems 2010; 1:1225-1233.

9. Shenton M.E., Whitford T.J., Kubicki M. Structural Neuroimaging in Schizophrenia: From Methods to Insights to Treatments. Dialogues Clin Neurosci. 2010;12(3):317-32. PMID: 20954428.

10. Nho K., Shen L., Kim S., Risacher S.L., West J.D., Foroud T., Jack C.R., Weiner M.W., Saykin A.J. Automatic Prediction of Conversion from Mild Cognitive Impairment to Probable Alzheimer's Disease using Structural Magnetic Resonance Imaging. AMIA Annu Symp Proc. 2010 Nov 13;2010:542-6. PMID: 21347037. PMCID: PMC3041374.

11. Kikinis Z., Fallon J.H., Niznikiewicz M., Nestor P., Davidson C., Bobrow L., Pelavin P.E., Fischl B., Yendiki A., McCarley R.W., Kikinis R., Kubicki M., Shenton M.E. Gray Matter Volume Reduction in Rostral Middle Frontal Gyrus in Patients with Chronic Schizophrenia. Schizophr Res. 2010 Nov;123(2-3):153-9. PMID: 20822884. PMCID: PMC2975427.

12. Kremen W.S., O'Brien R.C., Panizzon M.S., Prom-Wormley E., Eaves L.J., Eisen S.A., Eyler L.T., Hauger R.L., Fennema-Notestine C., Fischl B., Grant M.D., Hellhammer D.H., Jak A.J., Jacobson K.C., Jernigan T.L., Lupien S.J., Lyons M.J., Mendoza S.P., Neale M.C., Seidman L.J., Thermenos H.W., Tsuang M.T., Dale A.M., Franz C.E. Salivary Cortisol and Prefrontal Cortical Thickness in Middle-aged Men: A Twin Study. Neuroimage. 2010 Nov 15;53(3):1093-102. PMID: 20156572.

13. Elhawary H., Oguro S., Tuncali K., Morrison P.R., Tatli S., Shyn P.B., Silverman S.G., Hata N. Multimodality Non-rigid Image Registration for Planning, Targeting and Monitoring during CT-guided Percutaneous Liver Tumor Cryoablation. Acad Radiol. 2010 Nov;17(11):1334-44. PMID: 20817574. PMCID: PMC2952665.

14. Gerber S., Bremer P-T., Pascucci V., Whitaker R. Visual Exploration of High Dimensional Scalar Functions. IEEE Trans Vis Comput Graph. 2010 Nov-Dec;16(6):1271-80. PMID: 20975167.

15. Ghosh S.S., Kakunoori S., Augustinack J., Nieto-Castanon A., Kovelman I., Gaab N., Christodoulou J.A., Triantafyllou C., Gabrieli J.D.E., Fischl B. Evaluating the Validity of Volume-based and Surface-based Brain Image Registration for Developmental Cognitive Neuroscience Studies in Children 4 to 11 Years of Age. Neuroimage. 2010 Oct 15;53(1):85-93. PMID: 20621657. PMCID: PMC2914629.

16. Destrieux C., Fischl B., Dale A., Halgren E. Automatic Parcellation of Human Cortical Gyri and Sulci using Standard Anatomical Nomenclature. Neuroimage. 2010 Oct 15;53(1):1-15. PMID: 20547229. PMCID: PMC2937159.

17. Riklin-Raviv T., Van Leemput K., Menze B.H., Wells III W.M., Golland P. Segmentation of Image Ensembles via Latent Atlases. Med Image Anal. 2010 Oct;14(5):654-65. PMID: 20580305. PMCID: PMC2932709.

18. Sabuncu M.R., Yeo B.T.T., Van Leemput K., Fischl B., Golland P. A Generative Model for Image Segmentation Based on Label Fusion. IEEE Trans Med Imaging. 2010 Oct;29(10):1714-29. PMID: 20562040.

19. Gerber S., Tasdizen T., Thomas Fletcher P., Joshi S., Whitaker R. Manifold Modeling for Brain Population Analysis. Med Image Anal. 2010 Oct;14(5):643-53. PMID: 20579930. PMCID: PMC3020141.

20. Gao Y., Sandhu R., Fichtinger G., Tannenbaum A.R. A Coupled Global Registration and Segmentation Framework with Application to Magnetic Resonance Prostate Imagery. IEEE Trans Med Imaging. 2010 Oct;29(10):1781-94. PMID: 20529727. PMCID: PMC2988404.

==EAB REPORT (Lorensen)==

*Bill expects to complete this by Friday, April, 8, 2011.

2011 Scientific Progress Report

2011-04-08T17:00:11Z

Medcomparts: /* EAB REPORT */

=Report Outline=

==Introduction==

==Highlights (Will)==

*Advanced algorithms

*NA-MIC Kit

*Table of Downloads

*Outreach and Technology Transfer

==Impact (Jim Miller)==

*Impact within the Center

*Impact within NIH-Funded Research

*National and International Impact

==DBPs==
===Atrial Fibrillation, Rob MacLeod===

===Huntington's Disease, Hans Johnson===

===Head and Neck Cancer, Greg Sharp===

===Traumatic Brain Injury, Jack Van Horn===

==CS Core==

===Algorithms (Ross)===

*Statistical Models of Anatomy and Pathology, Polina Golland

*Geometric Correspondence, Guido Gerig

*User Interactive Tools for Segmentation, Allen Tannenbaum

*Longitudinal and Time Series Analysis, Martin Styner

===NA-MIC Kit (Will)===

==ARRA SUPPLEMENTS==

==Publications==
1. Wang Y., Gupta A., Liu Z., Zhang H., Escolar M.L., Gilmore J.H., Gouttard S., Fillard P., Maltbie E., Gerig G., Styner M. DTI Registration in Atlas Based Fiber Analysis of Infantile Krabbe Disease. Neuroimage. 2011 Apr 15;55(4):1577-86. PMID: 21256236. PMCID: PMC3062693.

2. Pohl K.M., Konukoglu E., Novellas S., Ayache N., Fedorov A., Talos I-F., Golby A., Wells III W.M., Kikinis R., Black P.M. A New Metric for Detecting Change in Slowly Evolving Brain Tumors: Validation in Meningioma Patients. Neurosurgery. 2011 Mar;68(1 Suppl Operative):225-33. PMID: 21206318.

3. Leritz E.C., Salat D.H., Williams V.J., Schnyer D.M., Rudolph J.L., Lipsitz L., Fischl B., McGlinchey R.E., Milberg W.P. Thickness of the Human Cerebral Cortex is Associated with Metrics of Cerebrovascular Health in a Normative Sample of Community Dwelling Older Adults. Neuroimage. 2011 Feb 14;54(4):2659-71. PMID: 21035552. PMCID: PMC3026290.

4. Salat D.H., Chen J.J., Van der Kouwe A.J.W., Greve D.N., Fischl B., Rosas H.D. Hippocampal Degeneration is Associated with Temporal and Limbic Gray Matter/White Matter Tissue Contrast in Alzheimer's Disease. Neuroimage. 2011 Feb 1;54(3):1795-802. PMID: 20965261. PMCID: PMC3021138.

5. Wang X., Grimson W.E.L., Westin C-F. Tractography Segmentation using a Hierarchical Dirichlet Processes Mixture Model. Neuroimage. 2011 Jan 1;54(1):290-302. PMID: 20678578. PMCID: PMC2962770.

6. Oguro S., Tuncali K., Elhawary H., Morrison P.R., Hata N., Silverman S.G. Image Registration of Pre-procedural MRI and Intra-procedural CT Images to Aid CT-guided Percutaneous Cryoablation of Renal Tumors. Int J Comput Assist Radiol Surg. 2011 Jan;6(1):111-7. PMID: 20499194. PMCID: PMC3050046.

7. Mohan V., Sundaramoorthi G., Tannenbaum A. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery. IEEE Trans Med Imaging. 2010 Dec;29(12):1945-58. PMID: 21118754.

8. Langs G., Golland P., Tie Y., Rigolo L., Golby A.J. Functional Geometry Alignment and Localization of Brain Areas. Proceedings of the 24th Annual Conference on Neural Information Processing Systems 2010; 1:1225-1233.

9. Shenton M.E., Whitford T.J., Kubicki M. Structural Neuroimaging in Schizophrenia: From Methods to Insights to Treatments. Dialogues Clin Neurosci. 2010;12(3):317-32. PMID: 20954428.

10. Nho K., Shen L., Kim S., Risacher S.L., West J.D., Foroud T., Jack C.R., Weiner M.W., Saykin A.J. Automatic Prediction of Conversion from Mild Cognitive Impairment to Probable Alzheimer's Disease using Structural Magnetic Resonance Imaging. AMIA Annu Symp Proc. 2010 Nov 13;2010:542-6. PMID: 21347037. PMCID: PMC3041374.

11. Kikinis Z., Fallon J.H., Niznikiewicz M., Nestor P., Davidson C., Bobrow L., Pelavin P.E., Fischl B., Yendiki A., McCarley R.W., Kikinis R., Kubicki M., Shenton M.E. Gray Matter Volume Reduction in Rostral Middle Frontal Gyrus in Patients with Chronic Schizophrenia. Schizophr Res. 2010 Nov;123(2-3):153-9. PMID: 20822884. PMCID: PMC2975427.

12. Kremen W.S., O'Brien R.C., Panizzon M.S., Prom-Wormley E., Eaves L.J., Eisen S.A., Eyler L.T., Hauger R.L., Fennema-Notestine C., Fischl B., Grant M.D., Hellhammer D.H., Jak A.J., Jacobson K.C., Jernigan T.L., Lupien S.J., Lyons M.J., Mendoza S.P., Neale M.C., Seidman L.J., Thermenos H.W., Tsuang M.T., Dale A.M., Franz C.E. Salivary Cortisol and Prefrontal Cortical Thickness in Middle-aged Men: A Twin Study. Neuroimage. 2010 Nov 15;53(3):1093-102. PMID: 20156572.

13. Elhawary H., Oguro S., Tuncali K., Morrison P.R., Tatli S., Shyn P.B., Silverman S.G., Hata N. Multimodality Non-rigid Image Registration for Planning, Targeting and Monitoring during CT-guided Percutaneous Liver Tumor Cryoablation. Acad Radiol. 2010 Nov;17(11):1334-44. PMID: 20817574. PMCID: PMC2952665.

14. Gerber S., Bremer P-T., Pascucci V., Whitaker R. Visual Exploration of High Dimensional Scalar Functions. IEEE Trans Vis Comput Graph. 2010 Nov-Dec;16(6):1271-80. PMID: 20975167.

15. Ghosh S.S., Kakunoori S., Augustinack J., Nieto-Castanon A., Kovelman I., Gaab N., Christodoulou J.A., Triantafyllou C., Gabrieli J.D.E., Fischl B. Evaluating the Validity of Volume-based and Surface-based Brain Image Registration for Developmental Cognitive Neuroscience Studies in Children 4 to 11 Years of Age. Neuroimage. 2010 Oct 15;53(1):85-93. PMID: 20621657. PMCID: PMC2914629.

16. Destrieux C., Fischl B., Dale A., Halgren E. Automatic Parcellation of Human Cortical Gyri and Sulci using Standard Anatomical Nomenclature. Neuroimage. 2010 Oct 15;53(1):1-15. PMID: 20547229. PMCID: PMC2937159.

17. Riklin-Raviv T., Van Leemput K., Menze B.H., Wells III W.M., Golland P. Segmentation of Image Ensembles via Latent Atlases. Med Image Anal. 2010 Oct;14(5):654-65. PMID: 20580305. PMCID: PMC2932709.

18. Sabuncu M.R., Yeo B.T.T., Van Leemput K., Fischl B., Golland P. A Generative Model for Image Segmentation Based on Label Fusion. IEEE Trans Med Imaging. 2010 Oct;29(10):1714-29. PMID: 20562040.

19. Gerber S., Tasdizen T., Thomas Fletcher P., Joshi S., Whitaker R. Manifold Modeling for Brain Population Analysis. Med Image Anal. 2010 Oct;14(5):643-53. PMID: 20579930. PMCID: PMC3020141.

20. Gao Y., Sandhu R., Fichtinger G., Tannenbaum A.R. A Coupled Global Registration and Segmentation Framework with Application to Magnetic Resonance Prostate Imagery. IEEE Trans Med Imaging. 2010 Oct;29(10):1781-94. PMID: 20529727. PMCID: PMC2988404.

==EAB REPORT (Lorensen)==

*Bill expects to complete this by Friday, April, 8, 2011.

2011 Scientific Progress Report

2011-04-08T16:59:14Z

Medcomparts: /* Impact (Jim Miller) */

=Report Outline=

==Introduction==

==Highlights (Will)==

*Advanced algorithms

*NA-MIC Kit

*Table of Downloads

*Outreach and Technology Transfer

==Impact (Jim Miller)==

*Impact within the Center

*Impact within NIH-Funded Research

*National and International Impact

==DBPs==
===Atrial Fibrillation, Rob MacLeod===

===Huntington's Disease, Hans Johnson===

===Head and Neck Cancer, Greg Sharp===

===Traumatic Brain Injury, Jack Van Horn===

==CS Core==

===Algorithms (Ross)===

*Statistical Models of Anatomy and Pathology, Polina Golland

*Geometric Correspondence, Guido Gerig

*User Interactive Tools for Segmentation, Allen Tannenbaum

*Longitudinal and Time Series Analysis, Martin Styner

===NA-MIC Kit (Will)===

==ARRA SUPPLEMENTS==

==Publications==
1. Wang Y., Gupta A., Liu Z., Zhang H., Escolar M.L., Gilmore J.H., Gouttard S., Fillard P., Maltbie E., Gerig G., Styner M. DTI Registration in Atlas Based Fiber Analysis of Infantile Krabbe Disease. Neuroimage. 2011 Apr 15;55(4):1577-86. PMID: 21256236. PMCID: PMC3062693.

2. Pohl K.M., Konukoglu E., Novellas S., Ayache N., Fedorov A., Talos I-F., Golby A., Wells III W.M., Kikinis R., Black P.M. A New Metric for Detecting Change in Slowly Evolving Brain Tumors: Validation in Meningioma Patients. Neurosurgery. 2011 Mar;68(1 Suppl Operative):225-33. PMID: 21206318.

3. Leritz E.C., Salat D.H., Williams V.J., Schnyer D.M., Rudolph J.L., Lipsitz L., Fischl B., McGlinchey R.E., Milberg W.P. Thickness of the Human Cerebral Cortex is Associated with Metrics of Cerebrovascular Health in a Normative Sample of Community Dwelling Older Adults. Neuroimage. 2011 Feb 14;54(4):2659-71. PMID: 21035552. PMCID: PMC3026290.

4. Salat D.H., Chen J.J., Van der Kouwe A.J.W., Greve D.N., Fischl B., Rosas H.D. Hippocampal Degeneration is Associated with Temporal and Limbic Gray Matter/White Matter Tissue Contrast in Alzheimer's Disease. Neuroimage. 2011 Feb 1;54(3):1795-802. PMID: 20965261. PMCID: PMC3021138.

5. Wang X., Grimson W.E.L., Westin C-F. Tractography Segmentation using a Hierarchical Dirichlet Processes Mixture Model. Neuroimage. 2011 Jan 1;54(1):290-302. PMID: 20678578. PMCID: PMC2962770.

6. Oguro S., Tuncali K., Elhawary H., Morrison P.R., Hata N., Silverman S.G. Image Registration of Pre-procedural MRI and Intra-procedural CT Images to Aid CT-guided Percutaneous Cryoablation of Renal Tumors. Int J Comput Assist Radiol Surg. 2011 Jan;6(1):111-7. PMID: 20499194. PMCID: PMC3050046.

7. Mohan V., Sundaramoorthi G., Tannenbaum A. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery. IEEE Trans Med Imaging. 2010 Dec;29(12):1945-58. PMID: 21118754.

8. Langs G., Golland P., Tie Y., Rigolo L., Golby A.J. Functional Geometry Alignment and Localization of Brain Areas. Proceedings of the 24th Annual Conference on Neural Information Processing Systems 2010; 1:1225-1233.

9. Shenton M.E., Whitford T.J., Kubicki M. Structural Neuroimaging in Schizophrenia: From Methods to Insights to Treatments. Dialogues Clin Neurosci. 2010;12(3):317-32. PMID: 20954428.

10. Nho K., Shen L., Kim S., Risacher S.L., West J.D., Foroud T., Jack C.R., Weiner M.W., Saykin A.J. Automatic Prediction of Conversion from Mild Cognitive Impairment to Probable Alzheimer's Disease using Structural Magnetic Resonance Imaging. AMIA Annu Symp Proc. 2010 Nov 13;2010:542-6. PMID: 21347037. PMCID: PMC3041374.

11. Kikinis Z., Fallon J.H., Niznikiewicz M., Nestor P., Davidson C., Bobrow L., Pelavin P.E., Fischl B., Yendiki A., McCarley R.W., Kikinis R., Kubicki M., Shenton M.E. Gray Matter Volume Reduction in Rostral Middle Frontal Gyrus in Patients with Chronic Schizophrenia. Schizophr Res. 2010 Nov;123(2-3):153-9. PMID: 20822884. PMCID: PMC2975427.

12. Kremen W.S., O'Brien R.C., Panizzon M.S., Prom-Wormley E., Eaves L.J., Eisen S.A., Eyler L.T., Hauger R.L., Fennema-Notestine C., Fischl B., Grant M.D., Hellhammer D.H., Jak A.J., Jacobson K.C., Jernigan T.L., Lupien S.J., Lyons M.J., Mendoza S.P., Neale M.C., Seidman L.J., Thermenos H.W., Tsuang M.T., Dale A.M., Franz C.E. Salivary Cortisol and Prefrontal Cortical Thickness in Middle-aged Men: A Twin Study. Neuroimage. 2010 Nov 15;53(3):1093-102. PMID: 20156572.

13. Elhawary H., Oguro S., Tuncali K., Morrison P.R., Tatli S., Shyn P.B., Silverman S.G., Hata N. Multimodality Non-rigid Image Registration for Planning, Targeting and Monitoring during CT-guided Percutaneous Liver Tumor Cryoablation. Acad Radiol. 2010 Nov;17(11):1334-44. PMID: 20817574. PMCID: PMC2952665.

14. Gerber S., Bremer P-T., Pascucci V., Whitaker R. Visual Exploration of High Dimensional Scalar Functions. IEEE Trans Vis Comput Graph. 2010 Nov-Dec;16(6):1271-80. PMID: 20975167.

15. Ghosh S.S., Kakunoori S., Augustinack J., Nieto-Castanon A., Kovelman I., Gaab N., Christodoulou J.A., Triantafyllou C., Gabrieli J.D.E., Fischl B. Evaluating the Validity of Volume-based and Surface-based Brain Image Registration for Developmental Cognitive Neuroscience Studies in Children 4 to 11 Years of Age. Neuroimage. 2010 Oct 15;53(1):85-93. PMID: 20621657. PMCID: PMC2914629.

16. Destrieux C., Fischl B., Dale A., Halgren E. Automatic Parcellation of Human Cortical Gyri and Sulci using Standard Anatomical Nomenclature. Neuroimage. 2010 Oct 15;53(1):1-15. PMID: 20547229. PMCID: PMC2937159.

17. Riklin-Raviv T., Van Leemput K., Menze B.H., Wells III W.M., Golland P. Segmentation of Image Ensembles via Latent Atlases. Med Image Anal. 2010 Oct;14(5):654-65. PMID: 20580305. PMCID: PMC2932709.

18. Sabuncu M.R., Yeo B.T.T., Van Leemput K., Fischl B., Golland P. A Generative Model for Image Segmentation Based on Label Fusion. IEEE Trans Med Imaging. 2010 Oct;29(10):1714-29. PMID: 20562040.

19. Gerber S., Tasdizen T., Thomas Fletcher P., Joshi S., Whitaker R. Manifold Modeling for Brain Population Analysis. Med Image Anal. 2010 Oct;14(5):643-53. PMID: 20579930. PMCID: PMC3020141.

20. Gao Y., Sandhu R., Fichtinger G., Tannenbaum A.R. A Coupled Global Registration and Segmentation Framework with Application to Magnetic Resonance Prostate Imagery. IEEE Trans Med Imaging. 2010 Oct;29(10):1781-94. PMID: 20529727. PMCID: PMC2988404.

==EAB REPORT==

2011 Scientific Progress Report

2011-04-08T16:58:48Z

Medcomparts: /* Highlights (Will) */

=Report Outline=

==Introduction==

==Highlights (Will)==

*Advanced algorithms

*NA-MIC Kit

*Table of Downloads

*Outreach and Technology Transfer

==Impact (Jim Miller)==

'Impact within the Center'

'Impact within NIH-Funded Research'

'National and International Impact'

==DBPs==
===Atrial Fibrillation, Rob MacLeod===

===Huntington's Disease, Hans Johnson===

===Head and Neck Cancer, Greg Sharp===

===Traumatic Brain Injury, Jack Van Horn===

==CS Core==

===Algorithms (Ross)===

*Statistical Models of Anatomy and Pathology, Polina Golland

*Geometric Correspondence, Guido Gerig

*User Interactive Tools for Segmentation, Allen Tannenbaum

*Longitudinal and Time Series Analysis, Martin Styner

===NA-MIC Kit (Will)===

==ARRA SUPPLEMENTS==

==Publications==
1. Wang Y., Gupta A., Liu Z., Zhang H., Escolar M.L., Gilmore J.H., Gouttard S., Fillard P., Maltbie E., Gerig G., Styner M. DTI Registration in Atlas Based Fiber Analysis of Infantile Krabbe Disease. Neuroimage. 2011 Apr 15;55(4):1577-86. PMID: 21256236. PMCID: PMC3062693.

2. Pohl K.M., Konukoglu E., Novellas S., Ayache N., Fedorov A., Talos I-F., Golby A., Wells III W.M., Kikinis R., Black P.M. A New Metric for Detecting Change in Slowly Evolving Brain Tumors: Validation in Meningioma Patients. Neurosurgery. 2011 Mar;68(1 Suppl Operative):225-33. PMID: 21206318.

3. Leritz E.C., Salat D.H., Williams V.J., Schnyer D.M., Rudolph J.L., Lipsitz L., Fischl B., McGlinchey R.E., Milberg W.P. Thickness of the Human Cerebral Cortex is Associated with Metrics of Cerebrovascular Health in a Normative Sample of Community Dwelling Older Adults. Neuroimage. 2011 Feb 14;54(4):2659-71. PMID: 21035552. PMCID: PMC3026290.

4. Salat D.H., Chen J.J., Van der Kouwe A.J.W., Greve D.N., Fischl B., Rosas H.D. Hippocampal Degeneration is Associated with Temporal and Limbic Gray Matter/White Matter Tissue Contrast in Alzheimer's Disease. Neuroimage. 2011 Feb 1;54(3):1795-802. PMID: 20965261. PMCID: PMC3021138.

5. Wang X., Grimson W.E.L., Westin C-F. Tractography Segmentation using a Hierarchical Dirichlet Processes Mixture Model. Neuroimage. 2011 Jan 1;54(1):290-302. PMID: 20678578. PMCID: PMC2962770.

6. Oguro S., Tuncali K., Elhawary H., Morrison P.R., Hata N., Silverman S.G. Image Registration of Pre-procedural MRI and Intra-procedural CT Images to Aid CT-guided Percutaneous Cryoablation of Renal Tumors. Int J Comput Assist Radiol Surg. 2011 Jan;6(1):111-7. PMID: 20499194. PMCID: PMC3050046.

7. Mohan V., Sundaramoorthi G., Tannenbaum A. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery. IEEE Trans Med Imaging. 2010 Dec;29(12):1945-58. PMID: 21118754.

8. Langs G., Golland P., Tie Y., Rigolo L., Golby A.J. Functional Geometry Alignment and Localization of Brain Areas. Proceedings of the 24th Annual Conference on Neural Information Processing Systems 2010; 1:1225-1233.

9. Shenton M.E., Whitford T.J., Kubicki M. Structural Neuroimaging in Schizophrenia: From Methods to Insights to Treatments. Dialogues Clin Neurosci. 2010;12(3):317-32. PMID: 20954428.

10. Nho K., Shen L., Kim S., Risacher S.L., West J.D., Foroud T., Jack C.R., Weiner M.W., Saykin A.J. Automatic Prediction of Conversion from Mild Cognitive Impairment to Probable Alzheimer's Disease using Structural Magnetic Resonance Imaging. AMIA Annu Symp Proc. 2010 Nov 13;2010:542-6. PMID: 21347037. PMCID: PMC3041374.

11. Kikinis Z., Fallon J.H., Niznikiewicz M., Nestor P., Davidson C., Bobrow L., Pelavin P.E., Fischl B., Yendiki A., McCarley R.W., Kikinis R., Kubicki M., Shenton M.E. Gray Matter Volume Reduction in Rostral Middle Frontal Gyrus in Patients with Chronic Schizophrenia. Schizophr Res. 2010 Nov;123(2-3):153-9. PMID: 20822884. PMCID: PMC2975427.

12. Kremen W.S., O'Brien R.C., Panizzon M.S., Prom-Wormley E., Eaves L.J., Eisen S.A., Eyler L.T., Hauger R.L., Fennema-Notestine C., Fischl B., Grant M.D., Hellhammer D.H., Jak A.J., Jacobson K.C., Jernigan T.L., Lupien S.J., Lyons M.J., Mendoza S.P., Neale M.C., Seidman L.J., Thermenos H.W., Tsuang M.T., Dale A.M., Franz C.E. Salivary Cortisol and Prefrontal Cortical Thickness in Middle-aged Men: A Twin Study. Neuroimage. 2010 Nov 15;53(3):1093-102. PMID: 20156572.

13. Elhawary H., Oguro S., Tuncali K., Morrison P.R., Tatli S., Shyn P.B., Silverman S.G., Hata N. Multimodality Non-rigid Image Registration for Planning, Targeting and Monitoring during CT-guided Percutaneous Liver Tumor Cryoablation. Acad Radiol. 2010 Nov;17(11):1334-44. PMID: 20817574. PMCID: PMC2952665.

14. Gerber S., Bremer P-T., Pascucci V., Whitaker R. Visual Exploration of High Dimensional Scalar Functions. IEEE Trans Vis Comput Graph. 2010 Nov-Dec;16(6):1271-80. PMID: 20975167.

15. Ghosh S.S., Kakunoori S., Augustinack J., Nieto-Castanon A., Kovelman I., Gaab N., Christodoulou J.A., Triantafyllou C., Gabrieli J.D.E., Fischl B. Evaluating the Validity of Volume-based and Surface-based Brain Image Registration for Developmental Cognitive Neuroscience Studies in Children 4 to 11 Years of Age. Neuroimage. 2010 Oct 15;53(1):85-93. PMID: 20621657. PMCID: PMC2914629.

16. Destrieux C., Fischl B., Dale A., Halgren E. Automatic Parcellation of Human Cortical Gyri and Sulci using Standard Anatomical Nomenclature. Neuroimage. 2010 Oct 15;53(1):1-15. PMID: 20547229. PMCID: PMC2937159.

17. Riklin-Raviv T., Van Leemput K., Menze B.H., Wells III W.M., Golland P. Segmentation of Image Ensembles via Latent Atlases. Med Image Anal. 2010 Oct;14(5):654-65. PMID: 20580305. PMCID: PMC2932709.

18. Sabuncu M.R., Yeo B.T.T., Van Leemput K., Fischl B., Golland P. A Generative Model for Image Segmentation Based on Label Fusion. IEEE Trans Med Imaging. 2010 Oct;29(10):1714-29. PMID: 20562040.

19. Gerber S., Tasdizen T., Thomas Fletcher P., Joshi S., Whitaker R. Manifold Modeling for Brain Population Analysis. Med Image Anal. 2010 Oct;14(5):643-53. PMID: 20579930. PMCID: PMC3020141.

20. Gao Y., Sandhu R., Fichtinger G., Tannenbaum A.R. A Coupled Global Registration and Segmentation Framework with Application to Magnetic Resonance Prostate Imagery. IEEE Trans Med Imaging. 2010 Oct;29(10):1781-94. PMID: 20529727. PMCID: PMC2988404.

==EAB REPORT==

2011 Scientific Progress Report

2011-04-08T16:58:06Z

Medcomparts: /* Impact (jim) */

=Report Outline=

==Introduction==

==Highlights (Will)==

'Advanced algorithms'

'NA-MIC Kit'

'Table of Downloads'

'Outreach and Technology Transfer'

==Impact (Jim Miller)==

'Impact within the Center'

'Impact within NIH-Funded Research'

'National and International Impact'

==DBPs==
===Atrial Fibrillation, Rob MacLeod===

===Huntington's Disease, Hans Johnson===

===Head and Neck Cancer, Greg Sharp===

===Traumatic Brain Injury, Jack Van Horn===

==CS Core==

===Algorithms (Ross)===

*Statistical Models of Anatomy and Pathology, Polina Golland

*Geometric Correspondence, Guido Gerig

*User Interactive Tools for Segmentation, Allen Tannenbaum

*Longitudinal and Time Series Analysis, Martin Styner

===NA-MIC Kit (Will)===

==ARRA SUPPLEMENTS==

==Publications==
1. Wang Y., Gupta A., Liu Z., Zhang H., Escolar M.L., Gilmore J.H., Gouttard S., Fillard P., Maltbie E., Gerig G., Styner M. DTI Registration in Atlas Based Fiber Analysis of Infantile Krabbe Disease. Neuroimage. 2011 Apr 15;55(4):1577-86. PMID: 21256236. PMCID: PMC3062693.

2. Pohl K.M., Konukoglu E., Novellas S., Ayache N., Fedorov A., Talos I-F., Golby A., Wells III W.M., Kikinis R., Black P.M. A New Metric for Detecting Change in Slowly Evolving Brain Tumors: Validation in Meningioma Patients. Neurosurgery. 2011 Mar;68(1 Suppl Operative):225-33. PMID: 21206318.

3. Leritz E.C., Salat D.H., Williams V.J., Schnyer D.M., Rudolph J.L., Lipsitz L., Fischl B., McGlinchey R.E., Milberg W.P. Thickness of the Human Cerebral Cortex is Associated with Metrics of Cerebrovascular Health in a Normative Sample of Community Dwelling Older Adults. Neuroimage. 2011 Feb 14;54(4):2659-71. PMID: 21035552. PMCID: PMC3026290.

4. Salat D.H., Chen J.J., Van der Kouwe A.J.W., Greve D.N., Fischl B., Rosas H.D. Hippocampal Degeneration is Associated with Temporal and Limbic Gray Matter/White Matter Tissue Contrast in Alzheimer's Disease. Neuroimage. 2011 Feb 1;54(3):1795-802. PMID: 20965261. PMCID: PMC3021138.

5. Wang X., Grimson W.E.L., Westin C-F. Tractography Segmentation using a Hierarchical Dirichlet Processes Mixture Model. Neuroimage. 2011 Jan 1;54(1):290-302. PMID: 20678578. PMCID: PMC2962770.

6. Oguro S., Tuncali K., Elhawary H., Morrison P.R., Hata N., Silverman S.G. Image Registration of Pre-procedural MRI and Intra-procedural CT Images to Aid CT-guided Percutaneous Cryoablation of Renal Tumors. Int J Comput Assist Radiol Surg. 2011 Jan;6(1):111-7. PMID: 20499194. PMCID: PMC3050046.

7. Mohan V., Sundaramoorthi G., Tannenbaum A. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery. IEEE Trans Med Imaging. 2010 Dec;29(12):1945-58. PMID: 21118754.

8. Langs G., Golland P., Tie Y., Rigolo L., Golby A.J. Functional Geometry Alignment and Localization of Brain Areas. Proceedings of the 24th Annual Conference on Neural Information Processing Systems 2010; 1:1225-1233.

9. Shenton M.E., Whitford T.J., Kubicki M. Structural Neuroimaging in Schizophrenia: From Methods to Insights to Treatments. Dialogues Clin Neurosci. 2010;12(3):317-32. PMID: 20954428.

10. Nho K., Shen L., Kim S., Risacher S.L., West J.D., Foroud T., Jack C.R., Weiner M.W., Saykin A.J. Automatic Prediction of Conversion from Mild Cognitive Impairment to Probable Alzheimer's Disease using Structural Magnetic Resonance Imaging. AMIA Annu Symp Proc. 2010 Nov 13;2010:542-6. PMID: 21347037. PMCID: PMC3041374.

11. Kikinis Z., Fallon J.H., Niznikiewicz M., Nestor P., Davidson C., Bobrow L., Pelavin P.E., Fischl B., Yendiki A., McCarley R.W., Kikinis R., Kubicki M., Shenton M.E. Gray Matter Volume Reduction in Rostral Middle Frontal Gyrus in Patients with Chronic Schizophrenia. Schizophr Res. 2010 Nov;123(2-3):153-9. PMID: 20822884. PMCID: PMC2975427.

12. Kremen W.S., O'Brien R.C., Panizzon M.S., Prom-Wormley E., Eaves L.J., Eisen S.A., Eyler L.T., Hauger R.L., Fennema-Notestine C., Fischl B., Grant M.D., Hellhammer D.H., Jak A.J., Jacobson K.C., Jernigan T.L., Lupien S.J., Lyons M.J., Mendoza S.P., Neale M.C., Seidman L.J., Thermenos H.W., Tsuang M.T., Dale A.M., Franz C.E. Salivary Cortisol and Prefrontal Cortical Thickness in Middle-aged Men: A Twin Study. Neuroimage. 2010 Nov 15;53(3):1093-102. PMID: 20156572.

13. Elhawary H., Oguro S., Tuncali K., Morrison P.R., Tatli S., Shyn P.B., Silverman S.G., Hata N. Multimodality Non-rigid Image Registration for Planning, Targeting and Monitoring during CT-guided Percutaneous Liver Tumor Cryoablation. Acad Radiol. 2010 Nov;17(11):1334-44. PMID: 20817574. PMCID: PMC2952665.

14. Gerber S., Bremer P-T., Pascucci V., Whitaker R. Visual Exploration of High Dimensional Scalar Functions. IEEE Trans Vis Comput Graph. 2010 Nov-Dec;16(6):1271-80. PMID: 20975167.

15. Ghosh S.S., Kakunoori S., Augustinack J., Nieto-Castanon A., Kovelman I., Gaab N., Christodoulou J.A., Triantafyllou C., Gabrieli J.D.E., Fischl B. Evaluating the Validity of Volume-based and Surface-based Brain Image Registration for Developmental Cognitive Neuroscience Studies in Children 4 to 11 Years of Age. Neuroimage. 2010 Oct 15;53(1):85-93. PMID: 20621657. PMCID: PMC2914629.

16. Destrieux C., Fischl B., Dale A., Halgren E. Automatic Parcellation of Human Cortical Gyri and Sulci using Standard Anatomical Nomenclature. Neuroimage. 2010 Oct 15;53(1):1-15. PMID: 20547229. PMCID: PMC2937159.

17. Riklin-Raviv T., Van Leemput K., Menze B.H., Wells III W.M., Golland P. Segmentation of Image Ensembles via Latent Atlases. Med Image Anal. 2010 Oct;14(5):654-65. PMID: 20580305. PMCID: PMC2932709.

18. Sabuncu M.R., Yeo B.T.T., Van Leemput K., Fischl B., Golland P. A Generative Model for Image Segmentation Based on Label Fusion. IEEE Trans Med Imaging. 2010 Oct;29(10):1714-29. PMID: 20562040.

19. Gerber S., Tasdizen T., Thomas Fletcher P., Joshi S., Whitaker R. Manifold Modeling for Brain Population Analysis. Med Image Anal. 2010 Oct;14(5):643-53. PMID: 20579930. PMCID: PMC3020141.

20. Gao Y., Sandhu R., Fichtinger G., Tannenbaum A.R. A Coupled Global Registration and Segmentation Framework with Application to Magnetic Resonance Prostate Imagery. IEEE Trans Med Imaging. 2010 Oct;29(10):1781-94. PMID: 20529727. PMCID: PMC2988404.

==EAB REPORT==

2011 Scientific Progress Report

2011-04-08T16:57:08Z

Medcomparts: /* Highlights (Will) */

=Report Outline=

==Introduction==

==Highlights (Will)==

'Advanced algorithms'

'NA-MIC Kit'

'Table of Downloads'

'Outreach and Technology Transfer'

==Impact (jim)==

==DBPs==
===Atrial Fibrillation, Rob MacLeod===

===Huntington's Disease, Hans Johnson===

===Head and Neck Cancer, Greg Sharp===

===Traumatic Brain Injury, Jack Van Horn===

==CS Core==

===Algorithms (Ross)===

*Statistical Models of Anatomy and Pathology, Polina Golland

*Geometric Correspondence, Guido Gerig

*User Interactive Tools for Segmentation, Allen Tannenbaum

*Longitudinal and Time Series Analysis, Martin Styner

===NA-MIC Kit (Will)===

==ARRA SUPPLEMENTS==

==Publications==
1. Wang Y., Gupta A., Liu Z., Zhang H., Escolar M.L., Gilmore J.H., Gouttard S., Fillard P., Maltbie E., Gerig G., Styner M. DTI Registration in Atlas Based Fiber Analysis of Infantile Krabbe Disease. Neuroimage. 2011 Apr 15;55(4):1577-86. PMID: 21256236. PMCID: PMC3062693.

2. Pohl K.M., Konukoglu E., Novellas S., Ayache N., Fedorov A., Talos I-F., Golby A., Wells III W.M., Kikinis R., Black P.M. A New Metric for Detecting Change in Slowly Evolving Brain Tumors: Validation in Meningioma Patients. Neurosurgery. 2011 Mar;68(1 Suppl Operative):225-33. PMID: 21206318.

3. Leritz E.C., Salat D.H., Williams V.J., Schnyer D.M., Rudolph J.L., Lipsitz L., Fischl B., McGlinchey R.E., Milberg W.P. Thickness of the Human Cerebral Cortex is Associated with Metrics of Cerebrovascular Health in a Normative Sample of Community Dwelling Older Adults. Neuroimage. 2011 Feb 14;54(4):2659-71. PMID: 21035552. PMCID: PMC3026290.

4. Salat D.H., Chen J.J., Van der Kouwe A.J.W., Greve D.N., Fischl B., Rosas H.D. Hippocampal Degeneration is Associated with Temporal and Limbic Gray Matter/White Matter Tissue Contrast in Alzheimer's Disease. Neuroimage. 2011 Feb 1;54(3):1795-802. PMID: 20965261. PMCID: PMC3021138.

5. Wang X., Grimson W.E.L., Westin C-F. Tractography Segmentation using a Hierarchical Dirichlet Processes Mixture Model. Neuroimage. 2011 Jan 1;54(1):290-302. PMID: 20678578. PMCID: PMC2962770.

6. Oguro S., Tuncali K., Elhawary H., Morrison P.R., Hata N., Silverman S.G. Image Registration of Pre-procedural MRI and Intra-procedural CT Images to Aid CT-guided Percutaneous Cryoablation of Renal Tumors. Int J Comput Assist Radiol Surg. 2011 Jan;6(1):111-7. PMID: 20499194. PMCID: PMC3050046.

7. Mohan V., Sundaramoorthi G., Tannenbaum A. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery. IEEE Trans Med Imaging. 2010 Dec;29(12):1945-58. PMID: 21118754.

8. Langs G., Golland P., Tie Y., Rigolo L., Golby A.J. Functional Geometry Alignment and Localization of Brain Areas. Proceedings of the 24th Annual Conference on Neural Information Processing Systems 2010; 1:1225-1233.

9. Shenton M.E., Whitford T.J., Kubicki M. Structural Neuroimaging in Schizophrenia: From Methods to Insights to Treatments. Dialogues Clin Neurosci. 2010;12(3):317-32. PMID: 20954428.

10. Nho K., Shen L., Kim S., Risacher S.L., West J.D., Foroud T., Jack C.R., Weiner M.W., Saykin A.J. Automatic Prediction of Conversion from Mild Cognitive Impairment to Probable Alzheimer's Disease using Structural Magnetic Resonance Imaging. AMIA Annu Symp Proc. 2010 Nov 13;2010:542-6. PMID: 21347037. PMCID: PMC3041374.

11. Kikinis Z., Fallon J.H., Niznikiewicz M., Nestor P., Davidson C., Bobrow L., Pelavin P.E., Fischl B., Yendiki A., McCarley R.W., Kikinis R., Kubicki M., Shenton M.E. Gray Matter Volume Reduction in Rostral Middle Frontal Gyrus in Patients with Chronic Schizophrenia. Schizophr Res. 2010 Nov;123(2-3):153-9. PMID: 20822884. PMCID: PMC2975427.

12. Kremen W.S., O'Brien R.C., Panizzon M.S., Prom-Wormley E., Eaves L.J., Eisen S.A., Eyler L.T., Hauger R.L., Fennema-Notestine C., Fischl B., Grant M.D., Hellhammer D.H., Jak A.J., Jacobson K.C., Jernigan T.L., Lupien S.J., Lyons M.J., Mendoza S.P., Neale M.C., Seidman L.J., Thermenos H.W., Tsuang M.T., Dale A.M., Franz C.E. Salivary Cortisol and Prefrontal Cortical Thickness in Middle-aged Men: A Twin Study. Neuroimage. 2010 Nov 15;53(3):1093-102. PMID: 20156572.

13. Elhawary H., Oguro S., Tuncali K., Morrison P.R., Tatli S., Shyn P.B., Silverman S.G., Hata N. Multimodality Non-rigid Image Registration for Planning, Targeting and Monitoring during CT-guided Percutaneous Liver Tumor Cryoablation. Acad Radiol. 2010 Nov;17(11):1334-44. PMID: 20817574. PMCID: PMC2952665.

14. Gerber S., Bremer P-T., Pascucci V., Whitaker R. Visual Exploration of High Dimensional Scalar Functions. IEEE Trans Vis Comput Graph. 2010 Nov-Dec;16(6):1271-80. PMID: 20975167.

15. Ghosh S.S., Kakunoori S., Augustinack J., Nieto-Castanon A., Kovelman I., Gaab N., Christodoulou J.A., Triantafyllou C., Gabrieli J.D.E., Fischl B. Evaluating the Validity of Volume-based and Surface-based Brain Image Registration for Developmental Cognitive Neuroscience Studies in Children 4 to 11 Years of Age. Neuroimage. 2010 Oct 15;53(1):85-93. PMID: 20621657. PMCID: PMC2914629.

16. Destrieux C., Fischl B., Dale A., Halgren E. Automatic Parcellation of Human Cortical Gyri and Sulci using Standard Anatomical Nomenclature. Neuroimage. 2010 Oct 15;53(1):1-15. PMID: 20547229. PMCID: PMC2937159.

17. Riklin-Raviv T., Van Leemput K., Menze B.H., Wells III W.M., Golland P. Segmentation of Image Ensembles via Latent Atlases. Med Image Anal. 2010 Oct;14(5):654-65. PMID: 20580305. PMCID: PMC2932709.

18. Sabuncu M.R., Yeo B.T.T., Van Leemput K., Fischl B., Golland P. A Generative Model for Image Segmentation Based on Label Fusion. IEEE Trans Med Imaging. 2010 Oct;29(10):1714-29. PMID: 20562040.

19. Gerber S., Tasdizen T., Thomas Fletcher P., Joshi S., Whitaker R. Manifold Modeling for Brain Population Analysis. Med Image Anal. 2010 Oct;14(5):643-53. PMID: 20579930. PMCID: PMC3020141.

20. Gao Y., Sandhu R., Fichtinger G., Tannenbaum A.R. A Coupled Global Registration and Segmentation Framework with Application to Magnetic Resonance Prostate Imagery. IEEE Trans Med Imaging. 2010 Oct;29(10):1781-94. PMID: 20529727. PMCID: PMC2988404.

==EAB REPORT==

2011 Scientific Progress Report

2011-04-08T16:56:33Z

Medcomparts: /* Highlights (Will) */

=Report Outline=

==Introduction==

==Highlights (Will)==

'Advanced algorithms'
'NA-MIC Kit'
'Table of Downloads'
'Outreach and Technology Transfer'

==Impact (jim)==

==DBPs==
===Atrial Fibrillation, Rob MacLeod===

===Huntington's Disease, Hans Johnson===

===Head and Neck Cancer, Greg Sharp===

===Traumatic Brain Injury, Jack Van Horn===

==CS Core==

===Algorithms (Ross)===

*Statistical Models of Anatomy and Pathology, Polina Golland

*Geometric Correspondence, Guido Gerig

*User Interactive Tools for Segmentation, Allen Tannenbaum

*Longitudinal and Time Series Analysis, Martin Styner

===NA-MIC Kit (Will)===

==ARRA SUPPLEMENTS==

==Publications==
1. Wang Y., Gupta A., Liu Z., Zhang H., Escolar M.L., Gilmore J.H., Gouttard S., Fillard P., Maltbie E., Gerig G., Styner M. DTI Registration in Atlas Based Fiber Analysis of Infantile Krabbe Disease. Neuroimage. 2011 Apr 15;55(4):1577-86. PMID: 21256236. PMCID: PMC3062693.

2. Pohl K.M., Konukoglu E., Novellas S., Ayache N., Fedorov A., Talos I-F., Golby A., Wells III W.M., Kikinis R., Black P.M. A New Metric for Detecting Change in Slowly Evolving Brain Tumors: Validation in Meningioma Patients. Neurosurgery. 2011 Mar;68(1 Suppl Operative):225-33. PMID: 21206318.

3. Leritz E.C., Salat D.H., Williams V.J., Schnyer D.M., Rudolph J.L., Lipsitz L., Fischl B., McGlinchey R.E., Milberg W.P. Thickness of the Human Cerebral Cortex is Associated with Metrics of Cerebrovascular Health in a Normative Sample of Community Dwelling Older Adults. Neuroimage. 2011 Feb 14;54(4):2659-71. PMID: 21035552. PMCID: PMC3026290.

4. Salat D.H., Chen J.J., Van der Kouwe A.J.W., Greve D.N., Fischl B., Rosas H.D. Hippocampal Degeneration is Associated with Temporal and Limbic Gray Matter/White Matter Tissue Contrast in Alzheimer's Disease. Neuroimage. 2011 Feb 1;54(3):1795-802. PMID: 20965261. PMCID: PMC3021138.

5. Wang X., Grimson W.E.L., Westin C-F. Tractography Segmentation using a Hierarchical Dirichlet Processes Mixture Model. Neuroimage. 2011 Jan 1;54(1):290-302. PMID: 20678578. PMCID: PMC2962770.

6. Oguro S., Tuncali K., Elhawary H., Morrison P.R., Hata N., Silverman S.G. Image Registration of Pre-procedural MRI and Intra-procedural CT Images to Aid CT-guided Percutaneous Cryoablation of Renal Tumors. Int J Comput Assist Radiol Surg. 2011 Jan;6(1):111-7. PMID: 20499194. PMCID: PMC3050046.

7. Mohan V., Sundaramoorthi G., Tannenbaum A. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery. IEEE Trans Med Imaging. 2010 Dec;29(12):1945-58. PMID: 21118754.

8. Langs G., Golland P., Tie Y., Rigolo L., Golby A.J. Functional Geometry Alignment and Localization of Brain Areas. Proceedings of the 24th Annual Conference on Neural Information Processing Systems 2010; 1:1225-1233.

9. Shenton M.E., Whitford T.J., Kubicki M. Structural Neuroimaging in Schizophrenia: From Methods to Insights to Treatments. Dialogues Clin Neurosci. 2010;12(3):317-32. PMID: 20954428.

10. Nho K., Shen L., Kim S., Risacher S.L., West J.D., Foroud T., Jack C.R., Weiner M.W., Saykin A.J. Automatic Prediction of Conversion from Mild Cognitive Impairment to Probable Alzheimer's Disease using Structural Magnetic Resonance Imaging. AMIA Annu Symp Proc. 2010 Nov 13;2010:542-6. PMID: 21347037. PMCID: PMC3041374.

11. Kikinis Z., Fallon J.H., Niznikiewicz M., Nestor P., Davidson C., Bobrow L., Pelavin P.E., Fischl B., Yendiki A., McCarley R.W., Kikinis R., Kubicki M., Shenton M.E. Gray Matter Volume Reduction in Rostral Middle Frontal Gyrus in Patients with Chronic Schizophrenia. Schizophr Res. 2010 Nov;123(2-3):153-9. PMID: 20822884. PMCID: PMC2975427.

12. Kremen W.S., O'Brien R.C., Panizzon M.S., Prom-Wormley E., Eaves L.J., Eisen S.A., Eyler L.T., Hauger R.L., Fennema-Notestine C., Fischl B., Grant M.D., Hellhammer D.H., Jak A.J., Jacobson K.C., Jernigan T.L., Lupien S.J., Lyons M.J., Mendoza S.P., Neale M.C., Seidman L.J., Thermenos H.W., Tsuang M.T., Dale A.M., Franz C.E. Salivary Cortisol and Prefrontal Cortical Thickness in Middle-aged Men: A Twin Study. Neuroimage. 2010 Nov 15;53(3):1093-102. PMID: 20156572.

13. Elhawary H., Oguro S., Tuncali K., Morrison P.R., Tatli S., Shyn P.B., Silverman S.G., Hata N. Multimodality Non-rigid Image Registration for Planning, Targeting and Monitoring during CT-guided Percutaneous Liver Tumor Cryoablation. Acad Radiol. 2010 Nov;17(11):1334-44. PMID: 20817574. PMCID: PMC2952665.

14. Gerber S., Bremer P-T., Pascucci V., Whitaker R. Visual Exploration of High Dimensional Scalar Functions. IEEE Trans Vis Comput Graph. 2010 Nov-Dec;16(6):1271-80. PMID: 20975167.

15. Ghosh S.S., Kakunoori S., Augustinack J., Nieto-Castanon A., Kovelman I., Gaab N., Christodoulou J.A., Triantafyllou C., Gabrieli J.D.E., Fischl B. Evaluating the Validity of Volume-based and Surface-based Brain Image Registration for Developmental Cognitive Neuroscience Studies in Children 4 to 11 Years of Age. Neuroimage. 2010 Oct 15;53(1):85-93. PMID: 20621657. PMCID: PMC2914629.

16. Destrieux C., Fischl B., Dale A., Halgren E. Automatic Parcellation of Human Cortical Gyri and Sulci using Standard Anatomical Nomenclature. Neuroimage. 2010 Oct 15;53(1):1-15. PMID: 20547229. PMCID: PMC2937159.

17. Riklin-Raviv T., Van Leemput K., Menze B.H., Wells III W.M., Golland P. Segmentation of Image Ensembles via Latent Atlases. Med Image Anal. 2010 Oct;14(5):654-65. PMID: 20580305. PMCID: PMC2932709.

18. Sabuncu M.R., Yeo B.T.T., Van Leemput K., Fischl B., Golland P. A Generative Model for Image Segmentation Based on Label Fusion. IEEE Trans Med Imaging. 2010 Oct;29(10):1714-29. PMID: 20562040.

19. Gerber S., Tasdizen T., Thomas Fletcher P., Joshi S., Whitaker R. Manifold Modeling for Brain Population Analysis. Med Image Anal. 2010 Oct;14(5):643-53. PMID: 20579930. PMCID: PMC3020141.

20. Gao Y., Sandhu R., Fichtinger G., Tannenbaum A.R. A Coupled Global Registration and Segmentation Framework with Application to Magnetic Resonance Prostate Imagery. IEEE Trans Med Imaging. 2010 Oct;29(10):1781-94. PMID: 20529727. PMCID: PMC2988404.

==EAB REPORT==

2011 Scientific Progress Report

2011-04-08T16:52:34Z

Medcomparts: /* Publications */

=Report Outline=

==Introduction==

==Highlights (Will)==

==Impact (jim)==

==DBPs==
===Atrial Fibrillation, Rob MacLeod===

===Huntington's Disease, Hans Johnson===

===Head and Neck Cancer, Greg Sharp===

===Traumatic Brain Injury, Jack Van Horn===

==CS Core==

===Algorithms (Ross)===

*Statistical Models of Anatomy and Pathology, Polina Golland

*Geometric Correspondence, Guido Gerig

*User Interactive Tools for Segmentation, Allen Tannenbaum

*Longitudinal and Time Series Analysis, Martin Styner

===NA-MIC Kit (Will)===

==ARRA SUPPLEMENTS==

==Publications==
1. Wang Y., Gupta A., Liu Z., Zhang H., Escolar M.L., Gilmore J.H., Gouttard S., Fillard P., Maltbie E., Gerig G., Styner M. DTI Registration in Atlas Based Fiber Analysis of Infantile Krabbe Disease. Neuroimage. 2011 Apr 15;55(4):1577-86. PMID: 21256236. PMCID: PMC3062693.

2. Pohl K.M., Konukoglu E., Novellas S., Ayache N., Fedorov A., Talos I-F., Golby A., Wells III W.M., Kikinis R., Black P.M. A New Metric for Detecting Change in Slowly Evolving Brain Tumors: Validation in Meningioma Patients. Neurosurgery. 2011 Mar;68(1 Suppl Operative):225-33. PMID: 21206318.

3. Leritz E.C., Salat D.H., Williams V.J., Schnyer D.M., Rudolph J.L., Lipsitz L., Fischl B., McGlinchey R.E., Milberg W.P. Thickness of the Human Cerebral Cortex is Associated with Metrics of Cerebrovascular Health in a Normative Sample of Community Dwelling Older Adults. Neuroimage. 2011 Feb 14;54(4):2659-71. PMID: 21035552. PMCID: PMC3026290.

4. Salat D.H., Chen J.J., Van der Kouwe A.J.W., Greve D.N., Fischl B., Rosas H.D. Hippocampal Degeneration is Associated with Temporal and Limbic Gray Matter/White Matter Tissue Contrast in Alzheimer's Disease. Neuroimage. 2011 Feb 1;54(3):1795-802. PMID: 20965261. PMCID: PMC3021138.

5. Wang X., Grimson W.E.L., Westin C-F. Tractography Segmentation using a Hierarchical Dirichlet Processes Mixture Model. Neuroimage. 2011 Jan 1;54(1):290-302. PMID: 20678578. PMCID: PMC2962770.

6. Oguro S., Tuncali K., Elhawary H., Morrison P.R., Hata N., Silverman S.G. Image Registration of Pre-procedural MRI and Intra-procedural CT Images to Aid CT-guided Percutaneous Cryoablation of Renal Tumors. Int J Comput Assist Radiol Surg. 2011 Jan;6(1):111-7. PMID: 20499194. PMCID: PMC3050046.

7. Mohan V., Sundaramoorthi G., Tannenbaum A. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery. IEEE Trans Med Imaging. 2010 Dec;29(12):1945-58. PMID: 21118754.

8. Langs G., Golland P., Tie Y., Rigolo L., Golby A.J. Functional Geometry Alignment and Localization of Brain Areas. Proceedings of the 24th Annual Conference on Neural Information Processing Systems 2010; 1:1225-1233.

9. Shenton M.E., Whitford T.J., Kubicki M. Structural Neuroimaging in Schizophrenia: From Methods to Insights to Treatments. Dialogues Clin Neurosci. 2010;12(3):317-32. PMID: 20954428.

10. Nho K., Shen L., Kim S., Risacher S.L., West J.D., Foroud T., Jack C.R., Weiner M.W., Saykin A.J. Automatic Prediction of Conversion from Mild Cognitive Impairment to Probable Alzheimer's Disease using Structural Magnetic Resonance Imaging. AMIA Annu Symp Proc. 2010 Nov 13;2010:542-6. PMID: 21347037. PMCID: PMC3041374.

11. Kikinis Z., Fallon J.H., Niznikiewicz M., Nestor P., Davidson C., Bobrow L., Pelavin P.E., Fischl B., Yendiki A., McCarley R.W., Kikinis R., Kubicki M., Shenton M.E. Gray Matter Volume Reduction in Rostral Middle Frontal Gyrus in Patients with Chronic Schizophrenia. Schizophr Res. 2010 Nov;123(2-3):153-9. PMID: 20822884. PMCID: PMC2975427.

12. Kremen W.S., O'Brien R.C., Panizzon M.S., Prom-Wormley E., Eaves L.J., Eisen S.A., Eyler L.T., Hauger R.L., Fennema-Notestine C., Fischl B., Grant M.D., Hellhammer D.H., Jak A.J., Jacobson K.C., Jernigan T.L., Lupien S.J., Lyons M.J., Mendoza S.P., Neale M.C., Seidman L.J., Thermenos H.W., Tsuang M.T., Dale A.M., Franz C.E. Salivary Cortisol and Prefrontal Cortical Thickness in Middle-aged Men: A Twin Study. Neuroimage. 2010 Nov 15;53(3):1093-102. PMID: 20156572.

13. Elhawary H., Oguro S., Tuncali K., Morrison P.R., Tatli S., Shyn P.B., Silverman S.G., Hata N. Multimodality Non-rigid Image Registration for Planning, Targeting and Monitoring during CT-guided Percutaneous Liver Tumor Cryoablation. Acad Radiol. 2010 Nov;17(11):1334-44. PMID: 20817574. PMCID: PMC2952665.

14. Gerber S., Bremer P-T., Pascucci V., Whitaker R. Visual Exploration of High Dimensional Scalar Functions. IEEE Trans Vis Comput Graph. 2010 Nov-Dec;16(6):1271-80. PMID: 20975167.

15. Ghosh S.S., Kakunoori S., Augustinack J., Nieto-Castanon A., Kovelman I., Gaab N., Christodoulou J.A., Triantafyllou C., Gabrieli J.D.E., Fischl B. Evaluating the Validity of Volume-based and Surface-based Brain Image Registration for Developmental Cognitive Neuroscience Studies in Children 4 to 11 Years of Age. Neuroimage. 2010 Oct 15;53(1):85-93. PMID: 20621657. PMCID: PMC2914629.

16. Destrieux C., Fischl B., Dale A., Halgren E. Automatic Parcellation of Human Cortical Gyri and Sulci using Standard Anatomical Nomenclature. Neuroimage. 2010 Oct 15;53(1):1-15. PMID: 20547229. PMCID: PMC2937159.

17. Riklin-Raviv T., Van Leemput K., Menze B.H., Wells III W.M., Golland P. Segmentation of Image Ensembles via Latent Atlases. Med Image Anal. 2010 Oct;14(5):654-65. PMID: 20580305. PMCID: PMC2932709.

18. Sabuncu M.R., Yeo B.T.T., Van Leemput K., Fischl B., Golland P. A Generative Model for Image Segmentation Based on Label Fusion. IEEE Trans Med Imaging. 2010 Oct;29(10):1714-29. PMID: 20562040.

19. Gerber S., Tasdizen T., Thomas Fletcher P., Joshi S., Whitaker R. Manifold Modeling for Brain Population Analysis. Med Image Anal. 2010 Oct;14(5):643-53. PMID: 20579930. PMCID: PMC3020141.

20. Gao Y., Sandhu R., Fichtinger G., Tannenbaum A.R. A Coupled Global Registration and Segmentation Framework with Application to Magnetic Resonance Prostate Imagery. IEEE Trans Med Imaging. 2010 Oct;29(10):1781-94. PMID: 20529727. PMCID: PMC2988404.

==EAB REPORT==

2011 Progress Report

2011-02-21T16:26:26Z

Medcomparts: /* =EAB REPORT */

Back to [[NAMIC_Annual_Reports|NAMIC_Annual_Reports]]

==Administrative Paperwork Timeline==

*3/01: LOI goes to subs (Sanjay/Rachana)
*3/29: Signed Subcontractor's Documents due back to BWH
*4/05: Review by Radiology Administration (Trey/Susan)
*4/14: Review by BWH RA
*4/28: Signed Docs due back to Katie M for page numbering (Katie)
*4/29: Ship it to NIH

==Scientific Report Timeline==
*3/1: wiki page setup for progress report
*4/16: all sections to be completed by owners
*4/16: check NIH compliance of all publications in Pubmed (Katie)
*4/20: create final report (Ann)
*4/20: create full publications list (Katie)
*4/16: final review (Tina, Ron)
*4/23: final pdf created and submitted to Rachana (Katie)

=Report Outline=

==OVERVIEW==

==ROADMAP PROJECTS==

===Atrial Fibrillation, Rob MacLeod===

===Huntington's Disease, Hans Johnson===

===Head and Neck Cancer, Greg Sharp===

===Traumatic Brain Injury, Jack Van Horn===

===??Image Guided Therapy, Nobuhiko Hata===

==COMPUTER SCIENCE CORE==

===Algorithms===

*Overview, Ross Whitaker

*Statistical Models of Anatomy and Pathology, Polina Golland

*Geometric Correspondence, Guido Gerig

*User Interactive Tools for Segmentation, Allen Tannenbaum

*Longitudinal and Time Series Analysis, Martin Styner

===Engineering===

*Overview, Will Schroeder

*Architecture, Will Schroeder

*End User Platform Slicer 4, Steve Pieper

*Computational Platform, CLI, Jim Miller

*Data Management Platform, Steven Aylward

*Software Process, Steven Alyward

==OUTREACH==

*Service, Will Schroeder

*Training, Randy Gollub

*Dissemination, Tina Kapur and Steve Pieper

==ARRA SUPPLEMENTS==

==EAB REPORT==

2011 Progress Report

2011-02-21T16:25:45Z

Medcomparts: /* Report Outline */

2011 Progress Report

2011-02-21T16:25:26Z

Medcomparts: /* Outreach */

Back to [[NAMIC_Annual_Reports|NAMIC_Annual_Reports]]

==Administrative Paperwork Timeline==

*3/01: LOI goes to subs (Sanjay/Rachana)
*3/29: Signed Subcontractor's Documents due back to BWH
*4/05: Review by Radiology Administration (Trey/Susan)
*4/14: Review by BWH RA
*4/28: Signed Docs due back to Katie M for page numbering (Katie)
*4/29: Ship it to NIH

==Scientific Report Timeline==
*3/1: wiki page setup for progress report
*4/16: all sections to be completed by owners
*4/16: check NIH compliance of all publications in Pubmed (Katie)
*4/20: create final report (Ann)
*4/20: create full publications list (Katie)
*4/16: final review (Tina, Ron)
*4/23: final pdf created and submitted to Rachana (Katie)

=Report Outline=

==Overview==

==ROADMAP PROJECTS==

===Atrial Fibrillation, Rob MacLeod===

===Huntington's Disease, Hans Johnson===

===Head and Neck Cancer, Greg Sharp===

===Traumatic Brain Injury, Jack Van Horn===

===??Image Guided Therapy, Nobuhiko Hata===

==COMPUTER SCIENCE CORE==

===Algorithms===

*Overview, Ross Whitaker

*Statistical Models of Anatomy and Pathology, Polina Golland

*Geometric Correspondence, Guido Gerig

*User Interactive Tools for Segmentation, Allen Tannenbaum

*Longitudinal and Time Series Analysis, Martin Styner

===Engineering===

*Overview, Will Schroeder

*Architecture, Will Schroeder

*End User Platform Slicer 4, Steve Pieper

*Computational Platform, CLI, Jim Miller

*Data Management Platform, Steven Aylward

*Software Process, Steven Alyward

==OUTREACH==

*Service, Will Schroeder

*Training, Randy Gollub

*Dissemination, Tina Kapur and Steve Pieper

===EAB REPORT==

2011 Progress Report

2011-02-21T16:25:13Z

Medcomparts: /* Outreach */

Back to [[NAMIC_Annual_Reports|NAMIC_Annual_Reports]]

==Administrative Paperwork Timeline==

*3/01: LOI goes to subs (Sanjay/Rachana)
*3/29: Signed Subcontractor's Documents due back to BWH
*4/05: Review by Radiology Administration (Trey/Susan)
*4/14: Review by BWH RA
*4/28: Signed Docs due back to Katie M for page numbering (Katie)
*4/29: Ship it to NIH

==Scientific Report Timeline==
*3/1: wiki page setup for progress report
*4/16: all sections to be completed by owners
*4/16: check NIH compliance of all publications in Pubmed (Katie)
*4/20: create final report (Ann)
*4/20: create full publications list (Katie)
*4/16: final review (Tina, Ron)
*4/23: final pdf created and submitted to Rachana (Katie)

=Report Outline=

==Overview==

==ROADMAP PROJECTS==

===Atrial Fibrillation, Rob MacLeod===

===Huntington's Disease, Hans Johnson===

===Head and Neck Cancer, Greg Sharp===

===Traumatic Brain Injury, Jack Van Horn===

===??Image Guided Therapy, Nobuhiko Hata===

==COMPUTER SCIENCE CORE==

===Algorithms===

*Overview, Ross Whitaker

*Statistical Models of Anatomy and Pathology, Polina Golland

*Geometric Correspondence, Guido Gerig

*User Interactive Tools for Segmentation, Allen Tannenbaum

*Longitudinal and Time Series Analysis, Martin Styner

===Engineering===

*Overview, Will Schroeder

*Architecture, Will Schroeder

*End User Platform Slicer 4, Steve Pieper

*Computational Platform, CLI, Jim Miller

*Data Management Platform, Steven Aylward

*Software Process, Steven Alyward

==Outreach==

*Service, Will Schroeder

*Training, Randy Gollub

*Dissemination, Tina Kapur and Steve Pieper

===EAB REPORT==

2011 Progress Report

2011-02-21T16:24:44Z

Medcomparts: /* Algorithms */

Back to [[NAMIC_Annual_Reports|NAMIC_Annual_Reports]]

==Administrative Paperwork Timeline==

*3/01: LOI goes to subs (Sanjay/Rachana)
*3/29: Signed Subcontractor's Documents due back to BWH
*4/05: Review by Radiology Administration (Trey/Susan)
*4/14: Review by BWH RA
*4/28: Signed Docs due back to Katie M for page numbering (Katie)
*4/29: Ship it to NIH

==Scientific Report Timeline==
*3/1: wiki page setup for progress report
*4/16: all sections to be completed by owners
*4/16: check NIH compliance of all publications in Pubmed (Katie)
*4/20: create final report (Ann)
*4/20: create full publications list (Katie)
*4/16: final review (Tina, Ron)
*4/23: final pdf created and submitted to Rachana (Katie)

=Report Outline=

==Overview==

==ROADMAP PROJECTS==

===Atrial Fibrillation, Rob MacLeod===

===Huntington's Disease, Hans Johnson===

===Head and Neck Cancer, Greg Sharp===

===Traumatic Brain Injury, Jack Van Horn===

===??Image Guided Therapy, Nobuhiko Hata===

==COMPUTER SCIENCE CORE==

===Algorithms===

*Overview, Ross Whitaker

*Statistical Models of Anatomy and Pathology, Polina Golland

*Geometric Correspondence, Guido Gerig

*User Interactive Tools for Segmentation, Allen Tannenbaum

*Longitudinal and Time Series Analysis, Martin Styner

===Engineering===

*Overview, Will Schroeder

*Architecture, Will Schroeder

*End User Platform Slicer 4, Steve Pieper

*Computational Platform, CLI, Jim Miller

*Data Management Platform, Steven Aylward

*Software Process, Steven Alyward

===Outreach===

*Service, Will Schroeder

*Training, Randy Gollub

*Dissemination, Tina Kapur and Steve Pieper

===EAB REPORT==

2011 Progress Report

2011-02-21T16:23:17Z

Medcomparts: /* Scientific Report Timeline */

Back to [[NAMIC_Annual_Reports|NAMIC_Annual_Reports]]

==Administrative Paperwork Timeline==

*3/01: LOI goes to subs (Sanjay/Rachana)
*3/29: Signed Subcontractor's Documents due back to BWH
*4/05: Review by Radiology Administration (Trey/Susan)
*4/14: Review by BWH RA
*4/28: Signed Docs due back to Katie M for page numbering (Katie)
*4/29: Ship it to NIH

==Scientific Report Timeline==
*3/1: wiki page setup for progress report
*4/16: all sections to be completed by owners
*4/16: check NIH compliance of all publications in Pubmed (Katie)
*4/20: create final report (Ann)
*4/20: create full publications list (Katie)
*4/16: final review (Tina, Ron)
*4/23: final pdf created and submitted to Rachana (Katie)

=Report Outline=

==Overview==

==ROADMAP PROJECTS==

===Atrial Fibrillation, Rob MacLeod===

===Huntington's Disease, Hans Johnson===

===Head and Neck Cancer, Greg Sharp===

===Traumatic Brain Injury, Jack Van Horn===

===??Image Guided Therapy, Nobuhiko Hata===

==COMPUTER SCIENCE CORE==

===Algorithms===

*Overview, Ross Whitaker

*Statistical Models of Anatomy and Pathology,

*Geometric Correspondence, Guido Gerig

*User Interactive Tools for Segmentation, Allen Tannenbaum

*Longitudinal and Time Series Analysis, Steven Aylward

===Engineering===

*Overview, Will Schroeder

*Architecture, Will Schroeder

*End User Platform Slicer 4, Steve Pieper

*Computational Platform, CLI, Jim Miller

*Data Management Platform, Steven Aylward

*Software Process, Steven Alyward

===Outreach===

*Service, Will Schroeder

*Training, Randy Gollub

*Dissemination, Tina Kapur and Steve Pieper

===EAB REPORT==

DBP2:QueensFinal:2010

2011-02-01T13:25:50Z

Medcomparts: /* Overview */

[[DBP2:Main|back to DBP2 Main]]
__NOTOC__

=Overview=

A 3D Slicer based end-to-end application was developed for MRI-guided prostate biopsy (implemented as a [http://www.slicer.org 3D Slicer 3.6] module, [http://www.slicer.org/slicerWiki/index.php/Modules:ProstateNav-Documentation-3.6 ProstateNav]). The application supports multiple targeting devices (transrectal robot [[{{fullurl:{{FULLPAGENAME}}}}#ref_5 5], [{{fullurl:{{FULLPAGENAME}}}}#ref_15 15]], transperineal template, and transperineal robot [[{{fullurl:{{FULLPAGENAME}}}}#ref_17 17], [{{fullurl:{{FULLPAGENAME}}}}#ref_18 18]]) and multiple needle types (for biopsy and seed placement). Automatic and semi-automatic registration of the targeting device to the planning image coordinate system was implemented using 4 line fiducials or 3 orthogonal Z-shaped fiducials [[{{fullurl:{{FULLPAGENAME}}}}#ref_15 15], [{{fullurl:{{FULLPAGENAME}}}}#ref_16 16], [{{fullurl:{{FULLPAGENAME}}}}#ref_18 18]]. During the planning phase, the clinician defines several point targets for biopsy and seed placement. The 3D Slicer platform provides flexible visualization of several diagnostic image types (T2, contrast-enhanced, etc.). In the targeting phase, the software computes targeting parameters for each site that permit the targeting devices to accurately position the needles. The software offers a means to quantitatively assess the accuracy of needle placement. Patient motion during the intervention (dislocation and deformation between the planning and verification images) can be easily visualized, thereby reducing the chance of incorrect needle placement during lengthy procedures. The software is being tested in phantom experiments at three clinical sites: Brigham and Women's Hospital, NIH National Cancer Institute, and Johns Hopkins Hospital. The software also is being evaluated on patients. The targeting accuracy of the system was evaluated using 3D Slicer and the NA-MIC Kit [[{{fullurl:{{FULLPAGENAME}}}}#ref_2 2], [{{fullurl:{{FULLPAGENAME}}}}#ref_5 5], [{{fullurl:{{FULLPAGENAME}}}}#ref_6 6], [{{fullurl:{{FULLPAGENAME}}}}#ref_7 7]].
[[{{fullurl:{{FULLPAGENAME}}}}#ref_15 15], [{{fullurl:{{FULLPAGENAME}}}}#ref_18 18], [{{fullurl:{{FULLPAGENAME}}}}#ref_16 16]]

[[Image:ProstateNav361Calibration.png|thumb|320px|ProstateNav - Calibration of the transrectal robotic device]]
[[Image:ProstateNav361Planning.png|thumb|320px|ProstateNav - Planning of biopsy and seed targets]]
[[Image:ProstateNav361Targeting.png|thumb|320px|ProstateNav - Targeting parameters displayed in the operating room]]
[[Image:ProstateNav361Verification.png|thumb|320px|ProstateNav - Verification of the needle insertion, the needle is well aligned with planned trajectory]]

Preliminary prostate segmentation and registration algorithms were developed. The initial results were encouraging and have been published in journals and presented at conferences. Current algorithm implementations are stored in the NA-MIC sandbox repository. After optimization and tuning they will be to be added to the clinical application [[{{fullurl:{{FULLPAGENAME}}}}#ref_1 1], [{{fullurl:{{FULLPAGENAME}}}}#ref_8 8], [{{fullurl:{{FULLPAGENAME}}}}#ref_11 11], [{{fullurl:{{FULLPAGENAME}}}}#ref_19 19]]. A fast patient motion detection algorithm, based on slice-to-volume registration, also was developed using the NA-MIC Kit [[{{fullurl:{{FULLPAGENAME}}}}#ref_3 3], [{{fullurl:{{FULLPAGENAME}}}}#ref_4 4], [{{fullurl:{{FULLPAGENAME}}}}#ref_9 9], [{{fullurl:{{FULLPAGENAME}}}}#ref_14 14]].
[[Image:ProstateSegShapeBased.png|thumb|320px|Prostate segmentation using a statistical shape based method [[{{fullurl:{{FULLPAGENAME}}}}#ref_8 8]].]]

Applicability of 3D Slicer as a generic computer-assisted intervention platform was evaluated. Several architectural and usability features were identified and implemented that enable effective use of 3D Slicer in the operating room [[{{fullurl:{{FULLPAGENAME}}}}#ref_10 10], [{{fullurl:{{FULLPAGENAME}}}}#ref_12 12], [{{fullurl:{{FULLPAGENAME}}}}#ref_13 13], [{{fullurl:{{FULLPAGENAME}}}}#ref_15 15], [{{fullurl:{{FULLPAGENAME}}}}#ref_16 16], [{{fullurl:{{FULLPAGENAME}}}}#ref_17 17], [{{fullurl:{{FULLPAGENAME}}}}#ref_18 18]].

Utilization and further enhancement of the end results are planned in the scope of a new collaboration between [[Collaboration:OCAIRO | NA-MIC/OCAIRO collaboration]] and through other research grants (applications pending).

=Software=
*[http://www.slicer.org/slicerWiki/index.php/Modules:ProstateNav-Documentation-3.6 ProstateNav]: MRI-guided prostate biopsy module with multiple device support
** Implements a complete MRI-guided biopsy workflow: calibration, planning, targeting, verification
** Supports multiple devices: transrectal robot, transperineal template, transperineal robot
** Provides 3D visualization of patient images, targets, devices in the control and scanner room
** Latest stable version is available in [http://www.slicer.org/pages/Special:SlicerDownloads Slicer 3.6.1 release]. Latest development version source code is available in the [http://svn.na-mic.org/NAMICSandBox/trunk/IGTLoadableModules/ProstateNav/ NA-MIC Sandbox]
** Tutorial: [[Media:DBP2JohnsHopkinsTransRectalProstateBiopsy.pdf | presentation]], [[Media:TransRectalProstateBiopsyTutorialDataset.zip‎| dataset]]

=Listing and short description of the sample data=
* [http://insight-journal.org/midas/community/view/25 Clinical data sets of MRI-guided prostate biopsies]
**63 anonymized prostate MRI sequences of 5 patients (Pt*), acquired at NIH-NCI, PI-s: Camphausen, Kausal and Pinto.
**Imaging sessions: (date).Diag = diagnostic; (date).B(id), (date).LR(id), (date).HR(id) = trans-rectal prostate biopsy
**Image types: Needle Ax = needle insertion confirmation image; SAG 3POINT PLAN = calibration image
* [[Media:TransRectalProstateBiopsyTutorialDataset.zip‎| Tutorial data set]]
**One clinical dataset of robot-assisted trans-rectal prostate biopsy, contains calibration, planning, and verification images

=Related pages=
*[http://www.slicer.org/pages/Special:SlicerDownloads Slicer 3.6 download]
*[http://www.slicer.org/slicerWiki/index.php/Documentation-3.6 Slicer 3.6 documentation]
*[http://www.slicer.org/slicerWiki/index.php/Modules:ProstateNav-Documentation-3.6 ProstateNav documentation]
*[http://www.brighamandwomens.org/radiology/ Department of Radiology, Brigham and Women's Hospital and Harvard Medical School]
*[http://snr.spl.harvard.edu/ Surgical Navigation and Robotics Laboratory, Image-Guided Therapy Program, Brigham and Women's Hospital and Harvard Medical School]
*[http://ccr.cancer.gov/labs/lab.asp?labid=52 Radiation Oncology Branch, National Cancer Institute]
*[http://www.hopkinsradiology.org/ Department of Radiology, Johns Hopkins Hospital]
*[http://perk.cs.queensu.ca Laboratory for Percutaneous Surgery, Queen's University]

=References=

#<cite id="ref_1"></cite>Gao Y., Sandhu R., Fichtinger G., Tannenbaum A.R. [http://www.na-mic.org/publications/item/view/1920 A Coupled Global Registration and Segmentation Framework with Application to Magnetic Resonance Prostate Imagery.] IEEE Trans Med Imaging. 2010 Oct;29(10):1781-94. PMID: 20529727.
#<cite id="ref_2"></cite>Xu H., Lasso A., Vikal S., Guion P., Krieger A., Kaushal A., Whitcomb L.L., Fichtinger G. [http://www.na-mic.org/publications/item/view/1919 MRI-guided Robotic Prostate Biopsy: A Clinical Accuracy Validation.] Med Image Comput Comput Assist Interv. 2010;13(Pt 3):383-91. PMID: 20879423.
#<cite id="ref_3"></cite>Tadayyon, H., A. Lasso, S. Gill, A. Kaushal, P. Guion, and G. Fichtinger. "Target Motion Compensation in MRI-guided Prostate Biopsy with Static Images", EMBC2010 - 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Buenos Aires, Argentina, pp. 5416-5419, 2010.
#<cite id="ref_4"></cite>Lasso, A., S. Avni, and G. Fichtinger. "Targeting Error Simulator for Image-guided Prostate Needle Placement", EMBC2010 - 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Buenos Aires, Argentina, pp. 5424-5427, 2010.
#<cite id="ref_5"></cite>Xu, H., A. Lasso, S. Vikal, P. Guion, A. Krieger, A. Kaushal, L. Whitcomb, and G. Fichtinger. "MRI-Guided Transrectal Robotic Prostate Biopsy Validation", The American Association of Physicists in Medicine (AAPM) Annual Meeting 2010, July 18-22, vol. 37, 3128 (2010), Philadelphia, Pennsylvania, pp. 3128-3129, 2010.
#<cite id="ref_6"></cite>Xu, H., A. Lasso, S. Vikal, P. Guion, A. Krieger, A. Kaushal, L. Whitcomb, and G. Fichtinger. "Clinical Accuracy of Robot-Assisted Prostate Biopsy in Closed MRI Scanner", The Hamlyn Symposium on Medical Robotics, The Royal Society, London, UK, 25 May 2010.
#<cite id="ref_7"></cite>Xu, H., A. Lasso, S. Vikal, P. Guion, A. Krieger, A. Kaushal, L. L. Whitcomb, and G. Fichtinger. "Accuracy validation for MRI-guided robotic prostate biopsy", Medical Imaging 2010: Visualization, Image-Guided Procedures, and Modeling, San Diego, California, USA, SPIE, pp. 762517-762517-8, 2010.
#<cite id="ref_8"></cite>Gao, Y., A. Tannenbaum. "Shape based MRI prostate image segmentation using local information driven directional distance Bayesian method", Medical Imaging 2010: Visualization, Image-Guided Procedures, and Modeling, San Diego, California, USA, SPIE, pp. 762308-1 - 762308-9, 2010.
#<cite id="ref_9"></cite>Tadayyon, H., G. Fichtinger, A. Lasso, S. Vikal, and S. Gill. "MRI-Guided prostate motion tracking by means of multislice-to-volume registration", Proc. SPIE,(2010); Vol. 7625, 76252V.
#<cite id="ref_10"></cite>Lasso, A., J. Tokuda, S. Vikal, C. M. Tempany, N. Hata, and G. Fichtinger. "A generic computer assisted intervention plug-in module for 3D Slicer with multiple device support." Int Conf Med Image Comput Comput Assist Interv. 2009;
#<cite id="ref_11"></cite>Vikal S., Haker S., Tempany C.M., Fichtinger G. [http://www.na-mic.org/publications/item/view/1597 Prostate Contouring in MRI Guided Biopsy.] Proceedings of SPIE Medical Imaging, Image Processing 2009; 7259.
#<cite id="ref_12"></cite>Boisvert, J., D. Gobbi, S. Vikal, R. Rohling, G. Fichtinger, and P. Abolmaesumi. "An open-source solution for interactive acquisition, processing and transfer of interventional ultrasound images." Workshop on Systems and Architectures for Computer Assisted Interventions, held in conjunction with the 11th International Conference on Medical Image Computing and Computer Assisted Intervention, 2008.
#<cite id="ref_13"></cite>Fischer G.S., Krieger A., Iordachita I., Csoma C., Whitcomb L., Fichtinger G. [http://www.na-mic.org/publications/item/view/1595 MRI Compatibility of Robot Actuation Techniques - A Comparative Study.] Int Conf Med Image Comput Comput Assist Interv. 2008;11(Pt 2):509-517. PMID: 18982643.
#<cite id="ref_14"></cite>Gill S., Abolmaesumi P., Vikal S., Mousavi P., Fichtinger G. [http://www.spl.harvard.edu/publications/item/view/1598 Intraoperative Prostate Tracking with Slice-to-Volume Registration in MRI.] Proceedings of the 20th International Conference of the Society for Medical Innovation and Technology 2008; 154-158.
#<cite id="ref_15"></cite>Krieger, A., P. Guion, C. Csoma, I. Iordachita, A. Singh, A. Kaushal, C. Menard, G. Fichtinger, and L. Whitcomb. "Design and Preliminary Clinical Studies of an MRI-Guided Transrectal Prostate Intervention System." International Society of Magnetic Resonance in Medicine (ISMRM), 2008.
#<cite id="ref_16"></cite>Mewes P., Tokuda J., DiMaio S.P., Fischer G., Csoma C., Gobbi D., Tempany C.M., Fichtinger G., Hata N. [http://www.na-mic.org/publications/publications/item/view/1600 Integrated System for Robot-Assisted in Prostate Biopsy in Closed MRI Scanner.] Proceedings of the IEEE International Conference on Robotics and Automation 2008; 2959-2962.
#<cite id="ref_17"></cite>Tokuda, J., S. DiMaio, G. Fischer, C. Csoma, D. Gobbi, G. Fichtinger, N. Hata, and C. Tempany. "Real-time MR Imaging Controlled by Transperineal Needle Placement Device for MRI-guided Prostate Biopsy", 16th Scientific Meeting and Exhibition of International Society of Magnetic Resonance in Medicine, 2008.
#<cite id="ref_18"></cite>Tokuda J., Fischer G.S., Csoma C., DiMaio S.P., Gobbi D.G., Fichtinger G., Tempany C.M., Hata N. [http://www.na-mic.org/publications/publications/item/view/1477 Software Strategy for Robotic Transperineal Prostate Therapy in Closed-Bore MRI.] Int Conf Med Image Comput Comput Assist Interv. 2008;11(Pt 2):701-709. PMID: 18982666. PMCID: PMC2692941.
#<cite id="ref_19"></cite>Vikal, S., S. Haker, C. Tempany, and G. Fichtinger. [http://www.na-mic.org/publications/item/view/1597 Prostate contouring in MRI guided biopsy.]Proceedings of SPIE Medical Imaging, Image Processing 2009; 7259.

DBP2:UNCFinal:2010

2011-02-01T13:17:27Z

Medcomparts: /* Overview */

[[DBP2:Main|back to DBP2 Main]]
__NOTOC__
=Overview=
[[Image:BrainDevelopment.jpg|thumb|177px|Brain development]]

The analysis of neuroimaging data from pediatric populations presents several challenges. Normal variations in brain shape exist from infancy to adulthood as well as normal developmental changes related to tissue maturation. Measuring cortical thickness is one important way to analyze such developmental tissue changes. As a DBP, our group is interested in the longitudinal study of early brain development as reflected in variations in cortical thickness in autistic children and controls.
*We first developed a novel framework to perform individual regional cortical thickness analysis [[{{fullurl:{{FULLPAGENAME}}}}#ref_3 3], [{{fullurl:{{FULLPAGENAME}}}}#ref_4 4]]. This framework relies first on atlas-based automatic tissue segmentation via an expectation maximization scheme to compute probabilistic and hard tissue segmentations. Second, the skull is stripped by using a post-processed hard tissue segmentation label map as a mask. Third, a B-spline registration, initialized by centers of mass, rigid then affine registration, is computed to register a T1-weighted atlas to the corrected T1w skull-stripped image. A multi-threaded coarse-to-fine registration scheme using mattes mutual information metric is considered. The full transformation is then applied to a lobar parcellation map defined in the atlas space, dividing the brain into 20+ lobes. By using both the tissue segmentation label map and the registered parcellation map, sparse and asymmetric cortical thickness measurements are finally computed for each lobe.
**This framework has been incorporated in our ARCTIC tool, an open source C++ based high-level module disseminated as part of the 3D Slicer toolkit. ARCTIC’s setup allows for efficient batch processing and grid computing via BatchMake (Kitware, Inc.).
**We performed a longitudinal MRI study to investigate early growth trajectories in brain volume and cortical thickness. Cerebral gray and white matter volumes and cortical thickness in children with autism spectrum disorder and controls were examined. Subjects were seen at approximately 2 years of age (autism = 59, controls = 38) and then were rescanned approximately 24 months later at age 4-5 years (autism = 38, controls = 21). Results and conclusions should be published soon (paper submitted to AGP, Archives of General Psychiatry).

*We also developed a novel framework that permits group-wise automatic mesh-based analysis of cortical thickness [[{{fullurl:{{FULLPAGENAME}}}}#ref_1 1], [{{fullurl:{{FULLPAGENAME}}}}#ref_2 2], [{{fullurl:{{FULLPAGENAME}}}}#ref_5 5], [{{fullurl:{{FULLPAGENAME}}}}#ref_6 6]]. Our analysis framework consists of a pipeline of C++ based automated 3D Slicer compatible modules. The approach is divided into four parts. First an individual pre-processing pipeline is applied on each subject to create genus-zero inflated white matter cortical surfaces with cortical thickness measurements. The second part performs an entropy-based group-wise shape correspondence on these meshes using a particle system, which establishes a trade-off between an even sampling of the cortical surfaces and the similarity of corresponding points across the population using sulcal depth information and spatial proximity. A novel automatic initial particle sampling is performed using a matched 98-lobe parcellation map prior to a particle-splitting phase. Third, corresponding re-sampled surfaces are computed with interpolated cortical thickness measurements, which are finally analyzed via a statistical vertex-wise analysis module.
**This framework has been tested on a small pediatric dataset and incorporated in an open source C++ based high-level module called GAMBIT. GAMBIT’s setup allows efficient batch processing, grid computing and quality control.

=Software=
[[Image:ArcticLogo.png|thumb|150px|ARCTIC Logo|right]]
*[http://www.nitrc.org/projects/gambit ARCTIC] (Automatic Regional Cortical ThICkness)
**Description:
***ARCTIC is an end-to-end application allowing individual lobar analysis of cortical thickness
***Pipeline: tissue segmentation, regional atlas deformable registration, cortical thickness measurements, volume information stored in spreadsheets
***Visualization: white matter and gray matter mesh creation
***Quality control: optimal QC via 3D Slicer MRML scenes
**Download:
***Source code, executables and tutorial are available on [http://www.nitrc.org/projects/gambit NITRC]
***Latest stable version is directly available as an extension in [http://www.slicer.org/pages/Special:SlicerDownloads Slicer 3.6.1 release] and soon in Slicer 3.6.2
**Documentation:
***[http://www.nitrc.org/plugins/mwiki/index.php/arctic:MainPage NITRC wiki page]
***[http://www.slicer.org/slicerWiki/index.php/Modules:ARCTIC-Documentation-3.6 Online documentation within Slicer 3.6]
**Tutorials:
***ARCTIC tutorials: [[Media:ARCTIC-Slicer3-Tutorial.ppt|‏ [ppt]]][[Media:ARCTIC-Slicer3-Tutorial.pdf|‏ [pdf]]]
****1st Prize: NAMIC tutorial contest AHM 2009
****2nd Prize: NAMIC tutorial contest summer project week 2009

<center>
{|class=wikitable
|[[Image:T1Image.jpg|150px|T1-weighted skull-stripped image]]
|[[Image:Parcellation.jpg|150px|Parcellation image]]
|[[Image:WMThickness.jpg|150px|Cortical thickness on WM surface]]
|[[Image:ThicknessInformation.jpg|150px|Cortical thickness information]]
|-
|T1-weighted skull-stripped image
|Parcellation image
|Cortical thickness on WM surface
|Cortical thickness information
|}
</center>

[[Image:GAMBITLogo.png|thumb|150px|GAMBIT Logo|right]]
*[http://www.nitrc.org/projects/gambit GAMBIT] (Group-wise Automatic Mesh Based analysis of cortIcal Thickness)
**Description:
***GAMBIT is an end-to-end application allowing allowing group-wise automatic mesh-based analysis of cortical thickness as well as other surface area measurements
***Pipeline: individual preprocessing pipeline, group-wise particle-based correspondence on inflated genus-zero white matter surfaces, group-wise statistical analysis
***Visualization: inflated and folded white matter surfaces in correspondence, with cortical thickness and sulcal depth as overlays
***Quality control: optimal QC via 3D Slicer MRML scenes
**Download:
***Source code, executables and tutorial are available on [http://www.nitrc.org/projects/gambit NITRC]
***Latest stable version is available soon as an extension in Slicer 3.6.2
**Documentation:
***[http://www.nitrc.org/plugins/mwiki/index.php/gambit:MainPage NITRC wiki page]
**Tutorials:
***GAMBIT tutorial presentations: [http://wiki.na-mic.org/Wiki/index.php/File:GAMBIT_TutorialContestSummer2010.ppt [ppt]] [http://wiki.na-mic.org/Wiki/index.php/File:GAMBIT_TutorialContestSummer2010.pdf [pdf]]

<center>
{|class=wikitable
|[[Image:GAMBIT QC CorticalThickness.png|500px|Cortical thickness overlayed on inflated cortical surfaces]]
|valign="center"|[[Image:GAMBIT_QC_SulcalDepth.png|500px|Sucal depth overlayed on inflated cortical surfaces]]
|-
|Cortical thickness overlayed on inflated cortical surfaces
|Sucal depth overlayed on inflated cortical surfaces
|}
</center>

=Listing and short description of the sample data=
[[Image:MeshBasedCortThick_T1Image.jpg|thumb|125px|T1-weighted image]]
*Pediatric Brain MRI data available on MIDAS
**[http://insight-journal.org/midas/community/view/24 Data of 2 autistic children and 2 normal controls] (male, female) scanned at 2 years with follow up at 4 years from a 1.5T Siemens scanner. Files include structural data, tissue segmentation label map and subcortical structures segmentation.
[[Image:MeshBasedCortThick_BrainROIAtlas_AllROIMesh.jpg|thumb|125px|T1-weigthed atlas with subcortical structures]]
*Brain Atlases available on MIDAS
** Average T1-weighted images (with/without skull) are provided with tissue segmentation probability maps (white matter, gray matter, csf, rest), subcortical structures probability maps (amygdala, caudate, hippocampus, pallidus, putamen) and lobar parcellation maps
**[http://www.insight-journal.org/midas/item/view/2277 Pediatric atlas]
**[http://www.insight-journal.org/midas/item/view/2328 Adult atlas]
**[http://www.insight-journal.org/midas/item/view/2330 Elderly atlas]
**[http://www.insight-journal.org/midas/item/view/2283 Primate atlas]
*Tutorial datasets:
**[http://www.nitrc.org/frs/download.php/545/ARCTIC_Tutorial_example_1.0.zip Download ARCTIC tutorial dataset]
**[http://www.nitrc.org/frs/download.php/2185/GAMBIT_Tutorial_example_1.0.zip Download GAMBIT tutorial dataset]

=Related pages=
*[http://www.slicer.org/pages/Special:SlicerDownloads Slicer 3.6 download]
*[http://www.slicer.org/slicerWiki/index.php/Documentation-3.6 Slicer 3.6 documentation]
*[http://www.niral.unc.edu/ Neuro Image Research and Analysis Laboratory, UNC Chapel Hill]
*[http://www.cidd.unc.edu/ UNC Carolina Institute for Developmental Disabilities]

=References=
#<cite id="ref_1"></cite>C. Vachet, H.C. Hazlett, M. Niethammer, I. Oguz, J.Cates, R. Whitaker, J. Piven, M. Styner, Group-wise Automatic Mesh-Based Analysis of Cortical Thickness, accepted to SPIE 2011
#<cite id="ref_2"></cite>Oguz I., Niethammer M., Cates J., Whitaker R., Fletcher T., Vachet C., Styner M. [http://www.na-mic.org/publications/item/view/1671 Cortical Correspondence with Probabilistic Fiber Connectivity.] Inf Process Med Imaging. 2009;21:651-63. PMID: 19694301. PMCID: PMC2751643.
#<cite id="ref_3"></cite>H.C. Hazlett, C. Vachet, C. Mathieu, M. Styner, J. Piven, Use of the Slicer3 Toolkit to Produce Regional Cortical Thickness Measurement of Pediatric MRI Data, presented at the 8th Annual International Meeting for Autism Research (IMFAR) Chicago, IL 2009.
#<cite id="ref_4"></cite>C. Mathieu, C. Vachet, H.C. Hazlett, G. Geric, J. Piven, and M. Styner, ARCTIC – Automatic Regional Cortical ThICkness Tool, UNC Radiology Research Day 2009 abstract.
#<cite id="ref_5"></cite>Oguz, I., Cates, J., Fletcher, T., Whitaker, R., Cool, D., Aylward, S., Styner, M., Cortical correspondence using entropy-based particle systems and local features, IEEE Symposium on Biomedical Imaging ISBI 2008. 1637-1640.
#<cite id="ref_6"></cite>J. Cates J., Fletcher P.T., Styner M., Hazlett H.C., Whitaker R. [http://www.na-mic.org/publications/item/view/1473 Particle-Based Shape Analysis of Multi-object Complexes.] Int Conf Med Image Comput Comput Assist Interv. 2008;11(Pt 1):477-485. PMID: 18979781. PMCID: PMC2753605.

DBP2:UNCFinal:2010

2011-02-01T13:12:44Z

Medcomparts: /* Overview */

[[DBP2:Main|back to DBP2 Main]]
__NOTOC__
=Overview=
[[Image:BrainDevelopment.jpg|thumb|177px|Brain development]]

The analysis of neuroimaging data from pediatric populations presents several challenges. Normal variations in brain shape exist from infancy to adulthood as well as normal developmental changes related to tissue maturation. Measuring cortical thickness is one important way to analyze such developmental tissue changes. As a DBP, our group is interested in the longitudinal study of early brain development as reflected in variations in cortical thickness in autistic children and controls.
*We first developed a novel framework to perform individual regional cortical thickness analysis [[{{fullurl:{{FULLPAGENAME}}}}#ref_3 3], [{{fullurl:{{FULLPAGENAME}}}}#ref_4 4]]. This framework entails first an atlas-based automatic tissue segmentation via an expectation maximization scheme in order to compute probabilistic and hard tissue segmentations. Secondly, the skull is stripped using a post-processed hard tissue segmentation label map as a mask. Thirdly, a B-spline registration, initialized by centers of mass, rigid then affine registration, is computed to register a T1-weighted atlas to the corrected T1w skull-stripped image. A multi-threaded coarse-to-fine registration scheme using mattes mutual information metric is considered. The full transformation is then applied to a lobar parcellation map defined in the atlas space, dividing the brain into 20+ lobes. Using both the tissue segmentation label map and the registered parcellation map, sparse and asymmetric cortical thickness measurements are finally computed for each lobe.
**This framework has been incorporated in our ARCTIC tool, an open source C++ based high-level module disseminated as part of the 3D Slicer toolkit. ARCTIC’s setup allows for efficient batch processing and grid computing via BatchMake (Kitware Inc).
**We performed a longitudinal MRI study investigating early growth trajectories in brain volume and cortical thickness. Cerebral gray and white matter volumes and cortical thickness in children with autism spectrum disorder and controls were examined. Subjects were seen at approximately 2 years of age (autism = 59, controls = 38) and were rescanned approximately 24 months later at age 4-5 years (autism = 38, controls = 21). Results and conclusions should be published soon (paper submitted to AGP, Archives of General Psychiatry).

*We also developed a novel framework that allows group-wise automatic mesh-based analysis of cortical thickness [[{{fullurl:{{FULLPAGENAME}}}}#ref_1 1], [{{fullurl:{{FULLPAGENAME}}}}#ref_2 2], [{{fullurl:{{FULLPAGENAME}}}}#ref_5 5], [{{fullurl:{{FULLPAGENAME}}}}#ref_6 6]]. Our analysis framework consists of a pipeline of C++ based automated 3D Slicer compatible modules. The approach is divided into four parts. First an individual pre-processing pipeline is applied on each subject to create genus-zero inflated white matter cortical surfaces with cortical thickness measurements. The second part performs an entropy-based group-wise shape correspondence on these meshes using a particle system, which establishes a trade-off between an even sampling of the cortical surfaces and the similarity of corresponding points across the population using sulcal depth information and spatial proximity. A novel automatic initial particle sampling is performed using a matched 98-lobe parcellation map prior to a particle-splitting phase. Third, corresponding re-sampled surfaces are computed with interpolated cortical thickness measurements, which are finally analyzed via a statistical vertex-wise analysis module.
**This framework has been tested on a small pediatric dataset and incorporated in an open source C++ based high-level module called GAMBIT. GAMBIT’s setup allows efficient batch processing, grid computing and quality control.

=Software=
[[Image:ArcticLogo.png|thumb|150px|ARCTIC Logo|right]]
*[http://www.nitrc.org/projects/gambit ARCTIC] (Automatic Regional Cortical ThICkness)
**Description:
***ARCTIC is an end-to-end application allowing individual lobar analysis of cortical thickness
***Pipeline: tissue segmentation, regional atlas deformable registration, cortical thickness measurements, volume information stored in spreadsheets
***Visualization: white matter and gray matter mesh creation
***Quality control: optimal QC via 3D Slicer MRML scenes
**Download:
***Source code, executables and tutorial are available on [http://www.nitrc.org/projects/gambit NITRC]
***Latest stable version is directly available as an extension in [http://www.slicer.org/pages/Special:SlicerDownloads Slicer 3.6.1 release] and soon in Slicer 3.6.2
**Documentation:
***[http://www.nitrc.org/plugins/mwiki/index.php/arctic:MainPage NITRC wiki page]
***[http://www.slicer.org/slicerWiki/index.php/Modules:ARCTIC-Documentation-3.6 Online documentation within Slicer 3.6]
**Tutorials:
***ARCTIC tutorials: [[Media:ARCTIC-Slicer3-Tutorial.ppt|‏ [ppt]]][[Media:ARCTIC-Slicer3-Tutorial.pdf|‏ [pdf]]]
****1st Prize: NAMIC tutorial contest AHM 2009
****2nd Prize: NAMIC tutorial contest summer project week 2009

<center>
{|class=wikitable
|[[Image:T1Image.jpg|150px|T1-weighted skull-stripped image]]
|[[Image:Parcellation.jpg|150px|Parcellation image]]
|[[Image:WMThickness.jpg|150px|Cortical thickness on WM surface]]
|[[Image:ThicknessInformation.jpg|150px|Cortical thickness information]]
|-
|T1-weighted skull-stripped image
|Parcellation image
|Cortical thickness on WM surface
|Cortical thickness information
|}
</center>

[[Image:GAMBITLogo.png|thumb|150px|GAMBIT Logo|right]]
*[http://www.nitrc.org/projects/gambit GAMBIT] (Group-wise Automatic Mesh Based analysis of cortIcal Thickness)
**Description:
***GAMBIT is an end-to-end application allowing allowing group-wise automatic mesh-based analysis of cortical thickness as well as other surface area measurements
***Pipeline: individual preprocessing pipeline, group-wise particle-based correspondence on inflated genus-zero white matter surfaces, group-wise statistical analysis
***Visualization: inflated and folded white matter surfaces in correspondence, with cortical thickness and sulcal depth as overlays
***Quality control: optimal QC via 3D Slicer MRML scenes
**Download:
***Source code, executables and tutorial are available on [http://www.nitrc.org/projects/gambit NITRC]
***Latest stable version is available soon as an extension in Slicer 3.6.2
**Documentation:
***[http://www.nitrc.org/plugins/mwiki/index.php/gambit:MainPage NITRC wiki page]
**Tutorials:
***GAMBIT tutorial presentations: [http://wiki.na-mic.org/Wiki/index.php/File:GAMBIT_TutorialContestSummer2010.ppt [ppt]] [http://wiki.na-mic.org/Wiki/index.php/File:GAMBIT_TutorialContestSummer2010.pdf [pdf]]

<center>
{|class=wikitable
|[[Image:GAMBIT QC CorticalThickness.png|500px|Cortical thickness overlayed on inflated cortical surfaces]]
|valign="center"|[[Image:GAMBIT_QC_SulcalDepth.png|500px|Sucal depth overlayed on inflated cortical surfaces]]
|-
|Cortical thickness overlayed on inflated cortical surfaces
|Sucal depth overlayed on inflated cortical surfaces
|}
</center>

=Listing and short description of the sample data=
[[Image:MeshBasedCortThick_T1Image.jpg|thumb|125px|T1-weighted image]]
*Pediatric Brain MRI data available on MIDAS
**[http://insight-journal.org/midas/community/view/24 Data of 2 autistic children and 2 normal controls] (male, female) scanned at 2 years with follow up at 4 years from a 1.5T Siemens scanner. Files include structural data, tissue segmentation label map and subcortical structures segmentation.
[[Image:MeshBasedCortThick_BrainROIAtlas_AllROIMesh.jpg|thumb|125px|T1-weigthed atlas with subcortical structures]]
*Brain Atlases available on MIDAS
** Average T1-weighted images (with/without skull) are provided with tissue segmentation probability maps (white matter, gray matter, csf, rest), subcortical structures probability maps (amygdala, caudate, hippocampus, pallidus, putamen) and lobar parcellation maps
**[http://www.insight-journal.org/midas/item/view/2277 Pediatric atlas]
**[http://www.insight-journal.org/midas/item/view/2328 Adult atlas]
**[http://www.insight-journal.org/midas/item/view/2330 Elderly atlas]
**[http://www.insight-journal.org/midas/item/view/2283 Primate atlas]
*Tutorial datasets:
**[http://www.nitrc.org/frs/download.php/545/ARCTIC_Tutorial_example_1.0.zip Download ARCTIC tutorial dataset]
**[http://www.nitrc.org/frs/download.php/2185/GAMBIT_Tutorial_example_1.0.zip Download GAMBIT tutorial dataset]

=Related pages=
*[http://www.slicer.org/pages/Special:SlicerDownloads Slicer 3.6 download]
*[http://www.slicer.org/slicerWiki/index.php/Documentation-3.6 Slicer 3.6 documentation]
*[http://www.niral.unc.edu/ Neuro Image Research and Analysis Laboratory, UNC Chapel Hill]
*[http://www.cidd.unc.edu/ UNC Carolina Institute for Developmental Disabilities]

=References=
#<cite id="ref_1"></cite>C. Vachet, H.C. Hazlett, M. Niethammer, I. Oguz, J.Cates, R. Whitaker, J. Piven, M. Styner, Group-wise Automatic Mesh-Based Analysis of Cortical Thickness, accepted to SPIE 2011
#<cite id="ref_2"></cite>Oguz I., Niethammer M., Cates J., Whitaker R., Fletcher T., Vachet C., Styner M. [http://www.na-mic.org/publications/item/view/1671 Cortical Correspondence with Probabilistic Fiber Connectivity.] Inf Process Med Imaging. 2009;21:651-63. PMID: 19694301. PMCID: PMC2751643.
#<cite id="ref_3"></cite>H.C. Hazlett, C. Vachet, C. Mathieu, M. Styner, J. Piven, Use of the Slicer3 Toolkit to Produce Regional Cortical Thickness Measurement of Pediatric MRI Data, presented at the 8th Annual International Meeting for Autism Research (IMFAR) Chicago, IL 2009.
#<cite id="ref_4"></cite>C. Mathieu, C. Vachet, H.C. Hazlett, G. Geric, J. Piven, and M. Styner, ARCTIC – Automatic Regional Cortical ThICkness Tool, UNC Radiology Research Day 2009 abstract.
#<cite id="ref_5"></cite>Oguz, I., Cates, J., Fletcher, T., Whitaker, R., Cool, D., Aylward, S., Styner, M., Cortical correspondence using entropy-based particle systems and local features, IEEE Symposium on Biomedical Imaging ISBI 2008. 1637-1640.
#<cite id="ref_6"></cite>J. Cates J., Fletcher P.T., Styner M., Hazlett H.C., Whitaker R. [http://www.na-mic.org/publications/item/view/1473 Particle-Based Shape Analysis of Multi-object Complexes.] Int Conf Med Image Comput Comput Assist Interv. 2008;11(Pt 1):477-485. PMID: 18979781. PMCID: PMC2753605.

DBP2:UNCFinal:2010

2011-02-01T13:11:50Z

Medcomparts: /* Overview */

[[DBP2:Main|back to DBP2 Main]]
__NOTOC__
=Overview=
[[Image:BrainDevelopment.jpg|thumb|177px|Brain development]]

The analysis of neuroimaging data from pediatric populations presents several challenges. Normal variations in brain shape exist from infancy to adulthood as well as normal developmental changes related to tissue maturation. Measuring cortical thickness is one important way to analyze these developmental tissue changes. As a DBP, our group is interested in the longitudinal study of early brain development reflected in variations in cortical thickness in autistic children and controls.
*We first developed a novel framework to perform individual regional cortical thickness analysis [[{{fullurl:{{FULLPAGENAME}}}}#ref_3 3], [{{fullurl:{{FULLPAGENAME}}}}#ref_4 4]]. This framework entails first an atlas-based automatic tissue segmentation via an expectation maximization scheme in order to compute probabilistic and hard tissue segmentations. Secondly, the skull is stripped using a post-processed hard tissue segmentation label map as a mask. Thirdly, a B-spline registration, initialized by centers of mass, rigid then affine registration, is computed to register a T1-weighted atlas to the corrected T1w skull-stripped image. A multi-threaded coarse-to-fine registration scheme using mattes mutual information metric is considered. The full transformation is then applied to a lobar parcellation map defined in the atlas space, dividing the brain into 20+ lobes. Using both the tissue segmentation label map and the registered parcellation map, sparse and asymmetric cortical thickness measurements are finally computed for each lobe.
**This framework has been incorporated in our ARCTIC tool, an open source C++ based high-level module disseminated as part of the 3D Slicer toolkit. ARCTIC’s setup allows for efficient batch processing and grid computing via BatchMake (Kitware Inc).
**We performed a longitudinal MRI study investigating early growth trajectories in brain volume and cortical thickness. Cerebral gray and white matter volumes and cortical thickness in children with autism spectrum disorder and controls were examined. Subjects were seen at approximately 2 years of age (autism = 59, controls = 38) and were rescanned approximately 24 months later at age 4-5 years (autism = 38, controls = 21). Results and conclusions should be published soon (paper submitted to AGP, Archives of General Psychiatry).

*We also developed a novel framework that allows group-wise automatic mesh-based analysis of cortical thickness [[{{fullurl:{{FULLPAGENAME}}}}#ref_1 1], [{{fullurl:{{FULLPAGENAME}}}}#ref_2 2], [{{fullurl:{{FULLPAGENAME}}}}#ref_5 5], [{{fullurl:{{FULLPAGENAME}}}}#ref_6 6]]. Our analysis framework consists of a pipeline of C++ based automated 3D Slicer compatible modules. The approach is divided into four parts. First an individual pre-processing pipeline is applied on each subject to create genus-zero inflated white matter cortical surfaces with cortical thickness measurements. The second part performs an entropy-based group-wise shape correspondence on these meshes using a particle system, which establishes a trade-off between an even sampling of the cortical surfaces and the similarity of corresponding points across the population using sulcal depth information and spatial proximity. A novel automatic initial particle sampling is performed using a matched 98-lobe parcellation map prior to a particle-splitting phase. Third, corresponding re-sampled surfaces are computed with interpolated cortical thickness measurements, which are finally analyzed via a statistical vertex-wise analysis module.
**This framework has been tested on a small pediatric dataset and incorporated in an open source C++ based high-level module called GAMBIT. GAMBIT’s setup allows efficient batch processing, grid computing and quality control.

=Software=
[[Image:ArcticLogo.png|thumb|150px|ARCTIC Logo|right]]
*[http://www.nitrc.org/projects/gambit ARCTIC] (Automatic Regional Cortical ThICkness)
**Description:
***ARCTIC is an end-to-end application allowing individual lobar analysis of cortical thickness
***Pipeline: tissue segmentation, regional atlas deformable registration, cortical thickness measurements, volume information stored in spreadsheets
***Visualization: white matter and gray matter mesh creation
***Quality control: optimal QC via 3D Slicer MRML scenes
**Download:
***Source code, executables and tutorial are available on [http://www.nitrc.org/projects/gambit NITRC]
***Latest stable version is directly available as an extension in [http://www.slicer.org/pages/Special:SlicerDownloads Slicer 3.6.1 release] and soon in Slicer 3.6.2
**Documentation:
***[http://www.nitrc.org/plugins/mwiki/index.php/arctic:MainPage NITRC wiki page]
***[http://www.slicer.org/slicerWiki/index.php/Modules:ARCTIC-Documentation-3.6 Online documentation within Slicer 3.6]
**Tutorials:
***ARCTIC tutorials: [[Media:ARCTIC-Slicer3-Tutorial.ppt|‏ [ppt]]][[Media:ARCTIC-Slicer3-Tutorial.pdf|‏ [pdf]]]
****1st Prize: NAMIC tutorial contest AHM 2009
****2nd Prize: NAMIC tutorial contest summer project week 2009

<center>
{|class=wikitable
|[[Image:T1Image.jpg|150px|T1-weighted skull-stripped image]]
|[[Image:Parcellation.jpg|150px|Parcellation image]]
|[[Image:WMThickness.jpg|150px|Cortical thickness on WM surface]]
|[[Image:ThicknessInformation.jpg|150px|Cortical thickness information]]
|-
|T1-weighted skull-stripped image
|Parcellation image
|Cortical thickness on WM surface
|Cortical thickness information
|}
</center>

[[Image:GAMBITLogo.png|thumb|150px|GAMBIT Logo|right]]
*[http://www.nitrc.org/projects/gambit GAMBIT] (Group-wise Automatic Mesh Based analysis of cortIcal Thickness)
**Description:
***GAMBIT is an end-to-end application allowing allowing group-wise automatic mesh-based analysis of cortical thickness as well as other surface area measurements
***Pipeline: individual preprocessing pipeline, group-wise particle-based correspondence on inflated genus-zero white matter surfaces, group-wise statistical analysis
***Visualization: inflated and folded white matter surfaces in correspondence, with cortical thickness and sulcal depth as overlays
***Quality control: optimal QC via 3D Slicer MRML scenes
**Download:
***Source code, executables and tutorial are available on [http://www.nitrc.org/projects/gambit NITRC]
***Latest stable version is available soon as an extension in Slicer 3.6.2
**Documentation:
***[http://www.nitrc.org/plugins/mwiki/index.php/gambit:MainPage NITRC wiki page]
**Tutorials:
***GAMBIT tutorial presentations: [http://wiki.na-mic.org/Wiki/index.php/File:GAMBIT_TutorialContestSummer2010.ppt [ppt]] [http://wiki.na-mic.org/Wiki/index.php/File:GAMBIT_TutorialContestSummer2010.pdf [pdf]]

<center>
{|class=wikitable
|[[Image:GAMBIT QC CorticalThickness.png|500px|Cortical thickness overlayed on inflated cortical surfaces]]
|valign="center"|[[Image:GAMBIT_QC_SulcalDepth.png|500px|Sucal depth overlayed on inflated cortical surfaces]]
|-
|Cortical thickness overlayed on inflated cortical surfaces
|Sucal depth overlayed on inflated cortical surfaces
|}
</center>

=Listing and short description of the sample data=
[[Image:MeshBasedCortThick_T1Image.jpg|thumb|125px|T1-weighted image]]
*Pediatric Brain MRI data available on MIDAS
**[http://insight-journal.org/midas/community/view/24 Data of 2 autistic children and 2 normal controls] (male, female) scanned at 2 years with follow up at 4 years from a 1.5T Siemens scanner. Files include structural data, tissue segmentation label map and subcortical structures segmentation.
[[Image:MeshBasedCortThick_BrainROIAtlas_AllROIMesh.jpg|thumb|125px|T1-weigthed atlas with subcortical structures]]
*Brain Atlases available on MIDAS
** Average T1-weighted images (with/without skull) are provided with tissue segmentation probability maps (white matter, gray matter, csf, rest), subcortical structures probability maps (amygdala, caudate, hippocampus, pallidus, putamen) and lobar parcellation maps
**[http://www.insight-journal.org/midas/item/view/2277 Pediatric atlas]
**[http://www.insight-journal.org/midas/item/view/2328 Adult atlas]
**[http://www.insight-journal.org/midas/item/view/2330 Elderly atlas]
**[http://www.insight-journal.org/midas/item/view/2283 Primate atlas]
*Tutorial datasets:
**[http://www.nitrc.org/frs/download.php/545/ARCTIC_Tutorial_example_1.0.zip Download ARCTIC tutorial dataset]
**[http://www.nitrc.org/frs/download.php/2185/GAMBIT_Tutorial_example_1.0.zip Download GAMBIT tutorial dataset]

=Related pages=
*[http://www.slicer.org/pages/Special:SlicerDownloads Slicer 3.6 download]
*[http://www.slicer.org/slicerWiki/index.php/Documentation-3.6 Slicer 3.6 documentation]
*[http://www.niral.unc.edu/ Neuro Image Research and Analysis Laboratory, UNC Chapel Hill]
*[http://www.cidd.unc.edu/ UNC Carolina Institute for Developmental Disabilities]

=References=
#<cite id="ref_1"></cite>C. Vachet, H.C. Hazlett, M. Niethammer, I. Oguz, J.Cates, R. Whitaker, J. Piven, M. Styner, Group-wise Automatic Mesh-Based Analysis of Cortical Thickness, accepted to SPIE 2011
#<cite id="ref_2"></cite>Oguz I., Niethammer M., Cates J., Whitaker R., Fletcher T., Vachet C., Styner M. [http://www.na-mic.org/publications/item/view/1671 Cortical Correspondence with Probabilistic Fiber Connectivity.] Inf Process Med Imaging. 2009;21:651-63. PMID: 19694301. PMCID: PMC2751643.
#<cite id="ref_3"></cite>H.C. Hazlett, C. Vachet, C. Mathieu, M. Styner, J. Piven, Use of the Slicer3 Toolkit to Produce Regional Cortical Thickness Measurement of Pediatric MRI Data, presented at the 8th Annual International Meeting for Autism Research (IMFAR) Chicago, IL 2009.
#<cite id="ref_4"></cite>C. Mathieu, C. Vachet, H.C. Hazlett, G. Geric, J. Piven, and M. Styner, ARCTIC – Automatic Regional Cortical ThICkness Tool, UNC Radiology Research Day 2009 abstract.
#<cite id="ref_5"></cite>Oguz, I., Cates, J., Fletcher, T., Whitaker, R., Cool, D., Aylward, S., Styner, M., Cortical correspondence using entropy-based particle systems and local features, IEEE Symposium on Biomedical Imaging ISBI 2008. 1637-1640.
#<cite id="ref_6"></cite>J. Cates J., Fletcher P.T., Styner M., Hazlett H.C., Whitaker R. [http://www.na-mic.org/publications/item/view/1473 Particle-Based Shape Analysis of Multi-object Complexes.] Int Conf Med Image Comput Comput Assist Interv. 2008;11(Pt 1):477-485. PMID: 18979781. PMCID: PMC2753605.

DBP2:UNCFinal:2010

2011-02-01T13:10:18Z

Medcomparts:

[[DBP2:Main|back to DBP2 Main]]
__NOTOC__
=Overview=
[[Image:BrainDevelopment.jpg|thumb|177px|Brain development]]

The analysis of neuroimaging data from pediatric populations presents several challenges. There are normal variations in brain shape from infancy to adulthood and normal developmental changes related to tissue maturation. Measurement of cortical thickness is one important way to analyze these developmental tissue changes. As a DBP, our group is interested in the longitudinal study of early brain development by measuring cortical thickness in autistic children and controls.
*We first developed a novel framework to perform individual regional cortical thickness analysis [[{{fullurl:{{FULLPAGENAME}}}}#ref_3 3], [{{fullurl:{{FULLPAGENAME}}}}#ref_4 4]]. This framework entails first an atlas-based automatic tissue segmentation via an expectation maximization scheme in order to compute probabilistic and hard tissue segmentations. Secondly, the skull is stripped using a post-processed hard tissue segmentation label map as a mask. Thirdly, a B-spline registration, initialized by centers of mass, rigid then affine registration, is computed to register a T1-weighted atlas to the corrected T1w skull-stripped image. A multi-threaded coarse-to-fine registration scheme using mattes mutual information metric is considered. The full transformation is then applied to a lobar parcellation map defined in the atlas space, dividing the brain into 20+ lobes. Using both the tissue segmentation label map and the registered parcellation map, sparse and asymmetric cortical thickness measurements are finally computed for each lobe.
**This framework has been incorporated in our ARCTIC tool, an open source C++ based high-level module disseminated as part of the 3D Slicer toolkit. ARCTIC’s setup allows for efficient batch processing and grid computing via BatchMake (Kitware Inc).
**We performed a longitudinal MRI study investigating early growth trajectories in brain volume and cortical thickness. Cerebral gray and white matter volumes and cortical thickness in children with autism spectrum disorder and controls were examined. Subjects were seen at approximately 2 years of age (autism = 59, controls = 38) and were rescanned approximately 24 months later at age 4-5 years (autism = 38, controls = 21). Results and conclusions should be published soon (paper submitted to AGP, Archives of General Psychiatry).

*We also developed a novel framework that allows group-wise automatic mesh-based analysis of cortical thickness [[{{fullurl:{{FULLPAGENAME}}}}#ref_1 1], [{{fullurl:{{FULLPAGENAME}}}}#ref_2 2], [{{fullurl:{{FULLPAGENAME}}}}#ref_5 5], [{{fullurl:{{FULLPAGENAME}}}}#ref_6 6]]. Our analysis framework consists of a pipeline of C++ based automated 3D Slicer compatible modules. The approach is divided into four parts. First an individual pre-processing pipeline is applied on each subject to create genus-zero inflated white matter cortical surfaces with cortical thickness measurements. The second part performs an entropy-based group-wise shape correspondence on these meshes using a particle system, which establishes a trade-off between an even sampling of the cortical surfaces and the similarity of corresponding points across the population using sulcal depth information and spatial proximity. A novel automatic initial particle sampling is performed using a matched 98-lobe parcellation map prior to a particle-splitting phase. Third, corresponding re-sampled surfaces are computed with interpolated cortical thickness measurements, which are finally analyzed via a statistical vertex-wise analysis module.
**This framework has been tested on a small pediatric dataset and incorporated in an open source C++ based high-level module called GAMBIT. GAMBIT’s setup allows efficient batch processing, grid computing and quality control.

=Software=
[[Image:ArcticLogo.png|thumb|150px|ARCTIC Logo|right]]
*[http://www.nitrc.org/projects/gambit ARCTIC] (Automatic Regional Cortical ThICkness)
**Description:
***ARCTIC is an end-to-end application allowing individual lobar analysis of cortical thickness
***Pipeline: tissue segmentation, regional atlas deformable registration, cortical thickness measurements, volume information stored in spreadsheets
***Visualization: white matter and gray matter mesh creation
***Quality control: optimal QC via 3D Slicer MRML scenes
**Download:
***Source code, executables and tutorial are available on [http://www.nitrc.org/projects/gambit NITRC]
***Latest stable version is directly available as an extension in [http://www.slicer.org/pages/Special:SlicerDownloads Slicer 3.6.1 release] and soon in Slicer 3.6.2
**Documentation:
***[http://www.nitrc.org/plugins/mwiki/index.php/arctic:MainPage NITRC wiki page]
***[http://www.slicer.org/slicerWiki/index.php/Modules:ARCTIC-Documentation-3.6 Online documentation within Slicer 3.6]
**Tutorials:
***ARCTIC tutorials: [[Media:ARCTIC-Slicer3-Tutorial.ppt|‏ [ppt]]][[Media:ARCTIC-Slicer3-Tutorial.pdf|‏ [pdf]]]
****1st Prize: NAMIC tutorial contest AHM 2009
****2nd Prize: NAMIC tutorial contest summer project week 2009

<center>
{|class=wikitable
|[[Image:T1Image.jpg|150px|T1-weighted skull-stripped image]]
|[[Image:Parcellation.jpg|150px|Parcellation image]]
|[[Image:WMThickness.jpg|150px|Cortical thickness on WM surface]]
|[[Image:ThicknessInformation.jpg|150px|Cortical thickness information]]
|-
|T1-weighted skull-stripped image
|Parcellation image
|Cortical thickness on WM surface
|Cortical thickness information
|}
</center>

[[Image:GAMBITLogo.png|thumb|150px|GAMBIT Logo|right]]
*[http://www.nitrc.org/projects/gambit GAMBIT] (Group-wise Automatic Mesh Based analysis of cortIcal Thickness)
**Description:
***GAMBIT is an end-to-end application allowing allowing group-wise automatic mesh-based analysis of cortical thickness as well as other surface area measurements
***Pipeline: individual preprocessing pipeline, group-wise particle-based correspondence on inflated genus-zero white matter surfaces, group-wise statistical analysis
***Visualization: inflated and folded white matter surfaces in correspondence, with cortical thickness and sulcal depth as overlays
***Quality control: optimal QC via 3D Slicer MRML scenes
**Download:
***Source code, executables and tutorial are available on [http://www.nitrc.org/projects/gambit NITRC]
***Latest stable version is available soon as an extension in Slicer 3.6.2
**Documentation:
***[http://www.nitrc.org/plugins/mwiki/index.php/gambit:MainPage NITRC wiki page]
**Tutorials:
***GAMBIT tutorial presentations: [http://wiki.na-mic.org/Wiki/index.php/File:GAMBIT_TutorialContestSummer2010.ppt [ppt]] [http://wiki.na-mic.org/Wiki/index.php/File:GAMBIT_TutorialContestSummer2010.pdf [pdf]]

<center>
{|class=wikitable
|[[Image:GAMBIT QC CorticalThickness.png|500px|Cortical thickness overlayed on inflated cortical surfaces]]
|valign="center"|[[Image:GAMBIT_QC_SulcalDepth.png|500px|Sucal depth overlayed on inflated cortical surfaces]]
|-
|Cortical thickness overlayed on inflated cortical surfaces
|Sucal depth overlayed on inflated cortical surfaces
|}
</center>

=Listing and short description of the sample data=
[[Image:MeshBasedCortThick_T1Image.jpg|thumb|125px|T1-weighted image]]
*Pediatric Brain MRI data available on MIDAS
**[http://insight-journal.org/midas/community/view/24 Data of 2 autistic children and 2 normal controls] (male, female) scanned at 2 years with follow up at 4 years from a 1.5T Siemens scanner. Files include structural data, tissue segmentation label map and subcortical structures segmentation.
[[Image:MeshBasedCortThick_BrainROIAtlas_AllROIMesh.jpg|thumb|125px|T1-weigthed atlas with subcortical structures]]
*Brain Atlases available on MIDAS
** Average T1-weighted images (with/without skull) are provided with tissue segmentation probability maps (white matter, gray matter, csf, rest), subcortical structures probability maps (amygdala, caudate, hippocampus, pallidus, putamen) and lobar parcellation maps
**[http://www.insight-journal.org/midas/item/view/2277 Pediatric atlas]
**[http://www.insight-journal.org/midas/item/view/2328 Adult atlas]
**[http://www.insight-journal.org/midas/item/view/2330 Elderly atlas]
**[http://www.insight-journal.org/midas/item/view/2283 Primate atlas]
*Tutorial datasets:
**[http://www.nitrc.org/frs/download.php/545/ARCTIC_Tutorial_example_1.0.zip Download ARCTIC tutorial dataset]
**[http://www.nitrc.org/frs/download.php/2185/GAMBIT_Tutorial_example_1.0.zip Download GAMBIT tutorial dataset]

=Related pages=
*[http://www.slicer.org/pages/Special:SlicerDownloads Slicer 3.6 download]
*[http://www.slicer.org/slicerWiki/index.php/Documentation-3.6 Slicer 3.6 documentation]
*[http://www.niral.unc.edu/ Neuro Image Research and Analysis Laboratory, UNC Chapel Hill]
*[http://www.cidd.unc.edu/ UNC Carolina Institute for Developmental Disabilities]

=References=
#<cite id="ref_1"></cite>C. Vachet, H.C. Hazlett, M. Niethammer, I. Oguz, J.Cates, R. Whitaker, J. Piven, M. Styner, Group-wise Automatic Mesh-Based Analysis of Cortical Thickness, accepted to SPIE 2011
#<cite id="ref_2"></cite>Oguz I., Niethammer M., Cates J., Whitaker R., Fletcher T., Vachet C., Styner M. [http://www.na-mic.org/publications/item/view/1671 Cortical Correspondence with Probabilistic Fiber Connectivity.] Inf Process Med Imaging. 2009;21:651-63. PMID: 19694301. PMCID: PMC2751643.
#<cite id="ref_3"></cite>H.C. Hazlett, C. Vachet, C. Mathieu, M. Styner, J. Piven, Use of the Slicer3 Toolkit to Produce Regional Cortical Thickness Measurement of Pediatric MRI Data, presented at the 8th Annual International Meeting for Autism Research (IMFAR) Chicago, IL 2009.
#<cite id="ref_4"></cite>C. Mathieu, C. Vachet, H.C. Hazlett, G. Geric, J. Piven, and M. Styner, ARCTIC – Automatic Regional Cortical ThICkness Tool, UNC Radiology Research Day 2009 abstract.
#<cite id="ref_5"></cite>Oguz, I., Cates, J., Fletcher, T., Whitaker, R., Cool, D., Aylward, S., Styner, M., Cortical correspondence using entropy-based particle systems and local features, IEEE Symposium on Biomedical Imaging ISBI 2008. 1637-1640.
#<cite id="ref_6"></cite>J. Cates J., Fletcher P.T., Styner M., Hazlett H.C., Whitaker R. [http://www.na-mic.org/publications/item/view/1473 Particle-Based Shape Analysis of Multi-object Complexes.] Int Conf Med Image Comput Comput Assist Interv. 2008;11(Pt 1):477-485. PMID: 18979781. PMCID: PMC2753605.

Special:Badtitle/NS100:DBP:Autism

2011-01-31T20:35:19Z

Medcomparts:

=Autism Solutions=
{|class="wikitable"
|[[Image:Oguz-ISBI2008-fig1.png|600px]]
|
*'''Data:''' [http://wiki.na-mic.org/Wiki/index.php/Downloads#Data Brain: 2-4 Year Old from Autism Study]
*'''Tutorial:''' [http://wiki.na-mic.org/Wiki/index.php/Downloads#Tutorials ARCTIC: Automatic Cortical ThiCkness]
*'''Software for Slicer 3.6:''' [http://wiki.slicer.org/slicerWiki/index.php/Modules:ARCTIC-Documentation-3.6 ARCTIC]
*'''Representative Publication:''' [http://www.na-mic.org/publications/item/view/1444 1444]
*'''Final Report''' [http://wiki.na-mic.org/Wiki/index.php/DBP2:UNCFinal:2010 UNC''']
|}

===Longitudinal MRI Study of Early Brain Development in Neuropsychiatric Disorder-Autism===
The primary goal of the University of North Carolina DBP is to learn more about autism by examining cortical thickness patterns in the early developing brain. Increasing evidence indicates that brain volume in children with autism is enlarged relative to normal controls. Whether these differences are due to increased cortical thickness or increased cortical surface area, however, is less clear. Studies of cortical growth during early brain development have been limited because existing tools for measuring brain volume are designed for the mature brain. A collaborating center of the NIH-funded Neuroimaging Study of Autism, clinical researchers at UNC already had acquired MRI data from a longitudinal sample of toddlers with autism, along with a comparison group of age and developmentally matched controls. NA-MIC had the inherent capability in 3D Slicer to produce cortical thickness measures for both individual and group analysis that could be developed for the pediatric population. Over the past several years, UNC has worked closely with NA-MIC computer scientists and software engineers to develop and deploy an end-to-end solution for measuring cortical thickness using data from MRI scans of toddler brains. This module, called '''ARCTIC''' (Automatic Regional Cortical ThiCkness) provides end-users with complete capability to perform individual regional cortical thickness analysis in the early developing brain.

[[Projects/NAMICWeb:Driving_Biological_Projects|Back to Driving Biological Projects]]

Special:Badtitle/NS100:DBP:Prostate Cancer

2011-01-31T20:33:01Z

Medcomparts: /* Prostate Cancer Solutions */

=Prostate Cancer Solutions=
{|class="wikitable"
|[[Image:FIG.2-10.TRProstateBiopsy2-c.png|400px]]
|
*'''Data''' [http://wiki.na-mic.org/Wiki/index.php/Downloads#Data Prostate: 5 robot-assisted intervention cases for Prostate Cancer]
*'''Tutorial''' [http://wiki.na-mic.org/Wiki/index.php/Downloads#Tutorials Transrectal MR Guided Prostate Biopsy and Perkstation Slicer Tutorial]
*'''Software for Slicer 3.6''' [http://www.slicer.org/slicerWiki/index.php/Modules:ProstateNav-Documentation-3.6 ProstateNav]
*'''Representative Publications''' [http://www.na-mic.org/publications/item/view/1659 1659] | [http://www.na-mic.org/publications/item/view/1810 1810]
*'''Final Report''' [http://wiki.na-mic.org/Wiki/index.php/DBP2:QueensFinal:2010 Queen's University]
|}

===Segmentation and Registration Tools for Robotic Prostate Interventions===

Dr. Fichtinger and colleagues from Queens University are investigating methods to improve prostate cancer diagnosis and therapy through the use of machine robotics. Before collaborating with NA-MIC, Dr. Fichtinger had been developing a custom software application to integrate various segmentation, registration, organ tracking, and robotic control algorithms into a clinically usable system for treating prostate cancer. The development team had encountered several systemic limitations. First, significant effort was required to implement basic functionalities, such as image visualization, graphical user interface (GUI), and data management. Second, with each new integration of algorithms and features, the overall complexity of the software kept increasing, making it difficult for users to learn and for developers to maintain and enhance the system. Additionally, it had proved difficult to find collaborators with the appropriate technical capability and the willingness to learn a customized software and assist them to develop new methods within this framework. Through its collaboration with NA-MIC, this DBP has been able to build an application for their cutting-edge robotics technology. By leveraging existing NA-MIC tools, they have avoided duplicative work to implement basic functionalities and have developed a wide range of segmentation and registration algorithms. Moreover, as a consequence of the modular architecture of 3D Slicer, they have been able to integrate all of their previous algorithms into one framework and combine them with functionalities already implemented by others. As a result, this DBP now has a single software with manageable complexity that works with multiple generations of the prostate robot. As a consequence of this successful collaboration, Dr. Fichtinger and colleagues have started to use 3D Slicer as a basis for several new computer-aided intervention software applications, including an augmented reality image overlay system for needle insertion (Perk Station), a test bench for real-time monitoring of tissue ablation, and a gynecologic radiotherapy system. The open source robot control and treatment planning platform developed under NA-MIC has empowered this DBP to participate in several major international and national alliances in the USA, Canada, and Austria.

[[Projects/NAMICWeb:Driving_Biological_Projects|Back to Driving Biological Projects]]

Special:Badtitle/NS100:DBP:Prostate Cancer

2011-01-31T20:32:42Z

Medcomparts:

=Prostate Cancer Solutions=
{|class="wikitable"
|[[Image:FIG.2-10.TRProstateBiopsy2-c.png|400px]]
|
*'''Data''' [http://wiki.na-mic.org/Wiki/index.php/Downloads#Data Prostate: 5 robot-assisted intervention cases for Prostate Cancer]
*'''Tutorial''' [http://wiki.na-mic.org/Wiki/index.php/Downloads#Tutorials Transrectal MR Guided Prostate Biopsy and Perkstation Slicer Tutorial]
*'''Software for Slicer 3.6''' [http://www.slicer.org/slicerWiki/index.php/Modules:ProstateNav-Documentation-3.6 ProstateNav]
*'''Representative Publications''' [http://www.na-mic.org/publications/item/view/1659 1659] | [http://www.na-mic.org/publications/item/view/1810 1810]
*'''Final Report''' [http://wiki.na-mic.org/Wiki/index.php/DBP2:QueensFinal:2010 Final Report]
|}

===Segmentation and Registration Tools for Robotic Prostate Interventions===

Dr. Fichtinger and colleagues from Queens University are investigating methods to improve prostate cancer diagnosis and therapy through the use of machine robotics. Before collaborating with NA-MIC, Dr. Fichtinger had been developing a custom software application to integrate various segmentation, registration, organ tracking, and robotic control algorithms into a clinically usable system for treating prostate cancer. The development team had encountered several systemic limitations. First, significant effort was required to implement basic functionalities, such as image visualization, graphical user interface (GUI), and data management. Second, with each new integration of algorithms and features, the overall complexity of the software kept increasing, making it difficult for users to learn and for developers to maintain and enhance the system. Additionally, it had proved difficult to find collaborators with the appropriate technical capability and the willingness to learn a customized software and assist them to develop new methods within this framework. Through its collaboration with NA-MIC, this DBP has been able to build an application for their cutting-edge robotics technology. By leveraging existing NA-MIC tools, they have avoided duplicative work to implement basic functionalities and have developed a wide range of segmentation and registration algorithms. Moreover, as a consequence of the modular architecture of 3D Slicer, they have been able to integrate all of their previous algorithms into one framework and combine them with functionalities already implemented by others. As a result, this DBP now has a single software with manageable complexity that works with multiple generations of the prostate robot. As a consequence of this successful collaboration, Dr. Fichtinger and colleagues have started to use 3D Slicer as a basis for several new computer-aided intervention software applications, including an augmented reality image overlay system for needle insertion (Perk Station), a test bench for real-time monitoring of tissue ablation, and a gynecologic radiotherapy system. The open source robot control and treatment planning platform developed under NA-MIC has empowered this DBP to participate in several major international and national alliances in the USA, Canada, and Austria.

[[Projects/NAMICWeb:Driving_Biological_Projects|Back to Driving Biological Projects]]

Special:Badtitle/NS100:DBP:Autism

2011-01-31T20:31:17Z

Medcomparts:

=Autism Solutions=
{|class="wikitable"
|[[Image:Oguz-ISBI2008-fig1.png|600px]]
|
*'''Data:''' [http://wiki.na-mic.org/Wiki/index.php/Downloads#Data Brain: 2-4 Year Old from Autism Study]
*'''Tutorial:''' [http://wiki.na-mic.org/Wiki/index.php/Downloads#Tutorials ARCTIC: Automatic Cortical ThiCkness]
*'''Software for Slicer 3.6:''' [http://wiki.slicer.org/slicerWiki/index.php/Modules:ARCTIC-Documentation-3.6 ARCTIC]
*'''Representative Publication:''' [http://www.na-mic.org/publications/item/view/1444 1444]
*'''[http://wiki.na-mic.org/Wiki/index.php/DBP2:UNCFinal:2010 Final Report''']
|}

===Longitudinal MRI Study of Early Brain Development in Neuropsychiatric Disorder-Autism===
The primary goal of the University of North Carolina DBP is to learn more about autism by examining cortical thickness patterns in the early developing brain. Increasing evidence indicates that brain volume in children with autism is enlarged relative to normal controls. Whether these differences are due to increased cortical thickness or increased cortical surface area, however, is less clear. Studies of cortical growth during early brain development have been limited because existing tools for measuring brain volume are designed for the mature brain. A collaborating center of the NIH-funded Neuroimaging Study of Autism, clinical researchers at UNC already had acquired MRI data from a longitudinal sample of toddlers with autism, along with a comparison group of age and developmentally matched controls. NA-MIC had the inherent capability in 3D Slicer to produce cortical thickness measures for both individual and group analysis that could be developed for the pediatric population. Over the past several years, UNC has worked closely with NA-MIC computer scientists and software engineers to develop and deploy an end-to-end solution for measuring cortical thickness using data from MRI scans of toddler brains. This module, called '''ARCTIC''' (Automatic Regional Cortical ThiCkness) provides end-users with complete capability to perform individual regional cortical thickness analysis in the early developing brain.

[[Projects/NAMICWeb:Driving_Biological_Projects|Back to Driving Biological Projects]]

Special:Badtitle/NS100:DBP:Prostate Cancer

2011-01-31T20:29:45Z

Medcomparts:

=Prostate Cancer Solutions=
{|class="wikitable"
|[[Image:FIG.2-10.TRProstateBiopsy2-c.png|400px]]
|
*'''Data''' [http://wiki.na-mic.org/Wiki/index.php/Downloads#Data Prostate: 5 robot-assisted intervention cases for Prostate Cancer]
*'''Tutorial''' [http://wiki.na-mic.org/Wiki/index.php/Downloads#Tutorials Transrectal MR Guided Prostate Biopsy and Perkstation Slicer Tutorial]
*'''Software for Slicer 3.6''' [http://www.slicer.org/slicerWiki/index.php/Modules:ProstateNav-Documentation-3.6 ProstateNav]
*'''Representative Publications''' [http://www.na-mic.org/publications/item/view/1659 1659] | [http://www.na-mic.org/publications/item/view/1810 1810]
*'''Final Report''' [http://wiki.na-mic.org/Wiki/index.php/DBP2:QueensFinal:2010]
|}

===Segmentation and Registration Tools for Robotic Prostate Interventions===

Dr. Fichtinger and colleagues from Queens University are investigating methods to improve prostate cancer diagnosis and therapy through the use of machine robotics. Before collaborating with NA-MIC, Dr. Fichtinger had been developing a custom software application to integrate various segmentation, registration, organ tracking, and robotic control algorithms into a clinically usable system for treating prostate cancer. The development team had encountered several systemic limitations. First, significant effort was required to implement basic functionalities, such as image visualization, graphical user interface (GUI), and data management. Second, with each new integration of algorithms and features, the overall complexity of the software kept increasing, making it difficult for users to learn and for developers to maintain and enhance the system. Additionally, it had proved difficult to find collaborators with the appropriate technical capability and the willingness to learn a customized software and assist them to develop new methods within this framework. Through its collaboration with NA-MIC, this DBP has been able to build an application for their cutting-edge robotics technology. By leveraging existing NA-MIC tools, they have avoided duplicative work to implement basic functionalities and have developed a wide range of segmentation and registration algorithms. Moreover, as a consequence of the modular architecture of 3D Slicer, they have been able to integrate all of their previous algorithms into one framework and combine them with functionalities already implemented by others. As a result, this DBP now has a single software with manageable complexity that works with multiple generations of the prostate robot. As a consequence of this successful collaboration, Dr. Fichtinger and colleagues have started to use 3D Slicer as a basis for several new computer-aided intervention software applications, including an augmented reality image overlay system for needle insertion (Perk Station), a test bench for real-time monitoring of tissue ablation, and a gynecologic radiotherapy system. The open source robot control and treatment planning platform developed under NA-MIC has empowered this DBP to participate in several major international and national alliances in the USA, Canada, and Austria.

[[Projects/NAMICWeb:Driving_Biological_Projects|Back to Driving Biological Projects]]

Special:Badtitle/NS100:About NA-MIC

2011-01-31T20:11:15Z

Medcomparts:

=Organization=
NA-MIC was funded in September of 2004 after submission of an application in response to an [http://grants1.nih.gov/grants/guide/rfa-files/RFA-RM-04-022.html RFA] issued by NIH as part of the [http://nihroadmap.nih.gov/ roadmap initiative], which called for the establishment of 7 National Centers for Biomedical Computing. All of the NCBC centers are organized around a series of specialized cores based on the requirements of the funding agency. The Computer Science Core consists of two teams. The [[Projects/NAMICWeb:Algorithms|algorithm]] team develops and implements medical image computing algorithms using the [http://www.na-mic.org/Wiki/index.php/NA-MIC-Kit NA-MIC Kit]. The [[Projects/NAMICWeb:Engineering|engineering]] team develops and maintains the [http://www.na-mic.org/Wiki/index.php/NA-MIC-Kit NA-MIC Kit], a software platform designed to enable research. The [[Projects/NAMICWeb:Driving Biological Projects|driving biological projects]] use the tools provided by the algorithm and engineering cores to develop software solutions that further their biomedical research. The [[Projects/NAMICWeb:Training|training]] and [[Projects/NAMICWeb:Dissemination|dissemination]] cores work on both internal and external outreach. The [[Projects/NAMICWeb:Service|service]] core supports the virtualized IT infrastructure that enables all these activities in a distributed environment. The [[Projects/NAMICWeb:Leadership|leadership]] core is responsible for the overall direction of the alliance. The PI works in close consultation with all the participants in the NA-MIC effort. Since its funding begun, NA-MIC has developed a network of internal and external collaborations. More information about the collaborations can be found on the [http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations '''NA-MIC Wiki'''].

<gallery Caption="NA-MIC Cores" >
Image:NAMIC_380x463.jpg|[[Projects/NAMICWeb:Leadership|<big>Leadership</big>]] PI: R. Kikinis
Image: Big-DBP-Logo.png |[[Projects/NAMICWeb:Driving Biological Projects#2007-2010|<big>DBP</big>]] University of Utah, UT University of Iowa, IA UCLA, CA MGH, HMS, MA
Image: Big-DBP-Logo.png |[[Projects/NAMICWeb:Driving Biological Projects#2004-2007|<big>DBP's, til '10</big>]] PNL, Brockton VA, HMS UCI, CA Dartmouth College, NH Indiana University, Indianapolis U of Toronto, Canada Mind Institute, CA JHU/Queen's University UNC, NC HMS, MA 
Image:Big-Algorithm-Logo.png|[[Projects/NAMICWeb:Algorithms|<big>CS (Algorithms)</big>]] Core PI: R. Whitaker University of Utah, UT MIT, MA UNC, NC Georgia Tech, GA
Image:Big-Engineering-Logo.png|[[Projects/NAMICWeb:Engineering|<big>CS (Engineering)</big>]] Core PI: W. Schroeder Kitware, Inc. BIRN CC, UCSD NRG, WUSTL GRC, GE Isomics, Inc.
Image:Big-Service-Logo.png|[[Projects/NAMICWeb:Service|<big>Service</big>]] Core PI: W. Schroeder Kitware, Inc.
Image:Big-Training-Logo.png|[[Projects/NAMICWeb:Training|<big>Training</big>]] Core PI: R. Gollub Martinos Center, MGH
Image:Big-Dissemination-Logo.png|[[Projects/NAMICWeb:Dissemination|<big>Dissemination</big>]] Core Co-PI: T. Kapur, S. Pieper SPL, BWH, Isomics Inc.

</gallery>

Special:Badtitle/NS100:About NA-MIC

2011-01-31T20:09:50Z

Medcomparts:

=Organization=
NA-MIC was funded in September of 2004 after submission of an application in response to an [http://grants1.nih.gov/grants/guide/rfa-files/RFA-RM-04-022.html RFA] issued by NIH as part of the [http://nihroadmap.nih.gov/ roadmap initiative], which called for the establishment of 7 National Centers for Biomedical Computing. All of the NCBC centers are organized around a series of specialized cores based on the requirements of the funding agency. The Computer Science Core consists of two teams. The [[Projects/NAMICWeb:Algorithms|algorithm]] team develops and implements medical image computing algorithms using the [http://www.na-mic.org/Wiki/index.php/NA-MIC-Kit NA-MIC Kit]. The [[Projects/NAMICWeb:Engineering|engineering]] team develops and maintains the [http://www.na-mic.org/Wiki/index.php/NA-MIC-Kit NA-MIC Kit], a software platform designed to enable research. The [[Projects/NAMICWeb:Driving Biological Projects|driving biological projects]] use the tools provided by the algorithm and engineering cores to develop software solutions that further their biomedical research. The [[Projects/NAMICWeb:Training|training]] and [[Projects/NAMICWeb:Dissemination|dissemination]] cores work on both internal and external outreach. The [[Projects/NAMICWeb:Service|service]] core supports the virtualized IT infrastructure that enables all these activities in a distributed environment. The [[Projects/NAMICWeb:Leadership|leadership]] core is responsible for the overall direction of the alliance. The PI works in close consultation with all the participants in the NA-MIC effort. Since its funding begun, NA-MIC has developed a network of internal and external collaborations. More information about the collaborations can be found on the [http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations '''NA-MIC Wiki'''].

<gallery Caption="NA-MIC Cores" >
Image:NAMIC_380x463.jpg|[[Projects/NAMICWeb:Leadership|<big>Leadership</big>]] PI: R. Kikinis
Image: Big-DBP-Logo.png |[[Projects/NAMICWeb:Driving Biological Projects#2007-2010|<big>DBP</big>]] University of Utah, UT University of Iowa, IA UCLA, CA MGH, HMS, MA
Image: Big-DBP-Logo.png |[[Projects/NAMICWeb:Driving Biological Projects#2004-2007|<big>DBP's, til '10</big>]] PNL, Brockton VA, HMS UCI, CA Dartmouth College, NH Indiana University, Indianapolis U of Toronto, Canada Mind Institute, CA JHU/Queen's University UNC, NC HMS, MA 
Image:Big-Algorithm-Logo.png|[[Projects/NAMICWeb:Algorithms|<big>CS (Algorithms)</big>]] Core PI: R. Whitaker U of Utah, UT MIT, MA UNC, NC Georgia Tech, GA
Image:Big-Engineering-Logo.png|[[Projects/NAMICWeb:Engineering|<big>CS (Engineering)</big>]] Core PI: W. Schroeder Kitware, Inc. BIRN CC, UCSD NRG, WUSTL GRC, GE Isomics, Inc.
Image:Big-Service-Logo.png|[[Projects/NAMICWeb:Service|<big>Service</big>]] Core PI: W. Schroeder Kitware, Inc.
Image:Big-Training-Logo.png|[[Projects/NAMICWeb:Training|<big>Training</big>]] Core PI: R. Gollub Martinos Center, MGH
Image:Big-Dissemination-Logo.png|[[Projects/NAMICWeb:Dissemination|<big>Dissemination</big>]] Core Co-PI: T. Kapur, S. Pieper SPL, BWH, Isomics Inc.

</gallery>

Special:Badtitle/NS100:Training

2010-12-15T13:08:59Z

Medcomparts: /* Validation methodology and practice */

__NOTOC__
=Training=
'''PI: Randy Gollub, M.D., MGH'''
[[Image:Big-Training-Logo.png|150px|left]]
The goal of Training is to lower barriers to effective communication between the clinical translational investigators and the computer scientists engaged in the development and application of medical image analysis and data management software tools for NA-MIC. These communities have diverse educational backgrounds and often do not share a common vocabulary or forum for exchanging ideas or valuable tools and solutions. Training addresses this gap by educating members of the biomedical clinical and research communities in the domains of knowledge relevant to the application of medical image analysis and its interface with computer science. Initially, the primary activity of Training was to develop and deliver hands-on learning experiences to clinicians, algorithm developers, and computer scientists to increase their competence in all aspects of medical image analysis. We used components of the NA-MIC Kit, primarily the 3D Slicer software, to teach the fundamentals of applied medical image processing and visualization. This approach enabled us to develop a single set of training materials that were equally well suited for constituents from all backgrounds, that is, clinicians, statisticians, and computer scientists. These tools further served to strengthen communication among these communities by defining and promulgating common vocabulary. Currently, we are expanding our education outreach effort to include a greater proportion of the clinical translational research community.

==Focus Areas==
===On-line learning resources===
[[Image:Arctic-i.png‎|150 px|left]]
Developing new on-line medical image analysis training materials is one of our top priorities. We are constantly adding to the materials and datasets available through our website. These training materials are developed for specific use cases gleaned from the Driving Biological Projects (DBPs) and require close collaboration among all cores (Outreach, Computer Science, DBPs, and external collaborators). All of our tutorials can be self-taught or administered by an instructor. Each tutorial follows the rubric established in How People Learn [1,2,3], which requires learner-centered, goal-oriented experiential teaching. Access downloads from our tutorial library [http://wiki.na-mic.org/Wiki/index.php/Downloads#Tutorials here].

===Hands-on training===
[[Image: Hands-on-Slicer-Training-2009.png|150px|left]]
We offer a variety of hands-on training experiences to increase the impact of our training program. For example, at the 2009 Radiology Society of North America (RSNA) meeting (see figure), Slicer 3D was used in a 90-minute workshop entitled “Quantitative Medical Imaging for Clinical Research and Practice” co-sponsored by the national CTSC Imaging Working Group Education Subcommittee. The event was completely subscribed; with standing room only (~100 attendees).
Access our calender of upcoming events [http://www.na-mic.org/Wiki/index.php/Events here].

===Validation methodology and practice===
[[Image: Xu-MICCAI2010-fig3.png|442px|left|]] Validation plays an important role in the assessment of algorithm performance that benefits both developers and users. Among the challenges of validating segmentation and registration algorithms for patient-specific analyses are (1) definition of appropriate metrics to measure differences among tools and across a sequence of images of the same patient; (2) evaluation of the significance of the differences observed, and (3) comparison to a gold standard, where available. Validation enables developers to assess the performance and limitations of their tools and to identify areas for improvement. In addition, validation provides users with the ability to compare different tools in a standardized way. For example, a retrospective validation analysis of the clinical accuracy of MRI-guided robotic biopsy for prostate cancer developed by the Prostate DBP is shown in the figure [4]. We have developed a portfolio of validation approaches for image segmentation through the organization of Grand Challenge workshops at the Medical Image Computing Computer-Assisted Intervention (MICCAI) Conference, and through our pioneering initiative in the standardized evaluation of single-tensor imaging tractography algorithms. These approaches form the basis of our approach to new algorithm and software development methodologies.

==Suggested Reading==

# How People Learn: Brain, Mind, Experience, and School. John D. Bransford, Ann L. Brown, and Rodney R. Cocking, editors; National Research Council, NATIONAL ACADEMY PRESS, Washington, D.C. 1999 (Free [http://www.nap.edu/catalog.php?record_id=6160 online text]).
# Lai I, Gollub R, Hoge R, Greve D, Vangel M, Poldrack R, Greenberg J. Teaching Statistical Analysis of fMRI Data. Proceedings of the American Society for Engineering Education (CD-ROM DEStech Publications) Session 2109: 11 pages (2003).
# Pujol S., Kikinis R., Gollub R. [http://www.na-mic.org/publications/item/view/1187 Lowering the Barriers Inherent in Translating Advances in Neuroimage Analysis to Clinical Research Applications.] Acad Radiol. 2008 Jan;15(1):114-8. PMID: 18078914. PMCID: PMC2234595.
# Xu H., Lasso A., Vikal S., Guion P., Krieger A., Kaushal A., Whitcomb L.L., Fichtinger G. [http://www.na-mic.org/publications/item/view/1919 MRI-guided Robotic Prostate Biopsy: A Clinical Accuracy Validation.] Int Conf Med Image Comput Comput Assist Interv. 2010;13(Pt 3):383-91. PMID: 20879423.

Special:Badtitle/NS100:Training

2010-12-15T13:08:20Z

Medcomparts: /* Validation methodology and practice */

__NOTOC__
=Training=
'''PI: Randy Gollub, M.D., MGH'''
[[Image:Big-Training-Logo.png|150px|left]]
The goal of Training is to lower barriers to effective communication between the clinical translational investigators and the computer scientists engaged in the development and application of medical image analysis and data management software tools for NA-MIC. These communities have diverse educational backgrounds and often do not share a common vocabulary or forum for exchanging ideas or valuable tools and solutions. Training addresses this gap by educating members of the biomedical clinical and research communities in the domains of knowledge relevant to the application of medical image analysis and its interface with computer science. Initially, the primary activity of Training was to develop and deliver hands-on learning experiences to clinicians, algorithm developers, and computer scientists to increase their competence in all aspects of medical image analysis. We used components of the NA-MIC Kit, primarily the 3D Slicer software, to teach the fundamentals of applied medical image processing and visualization. This approach enabled us to develop a single set of training materials that were equally well suited for constituents from all backgrounds, that is, clinicians, statisticians, and computer scientists. These tools further served to strengthen communication among these communities by defining and promulgating common vocabulary. Currently, we are expanding our education outreach effort to include a greater proportion of the clinical translational research community.

==Focus Areas==
===On-line learning resources===
[[Image:Arctic-i.png‎|150 px|left]]
Developing new on-line medical image analysis training materials is one of our top priorities. We are constantly adding to the materials and datasets available through our website. These training materials are developed for specific use cases gleaned from the Driving Biological Projects (DBPs) and require close collaboration among all cores (Outreach, Computer Science, DBPs, and external collaborators). All of our tutorials can be self-taught or administered by an instructor. Each tutorial follows the rubric established in How People Learn [1,2,3], which requires learner-centered, goal-oriented experiential teaching. Access downloads from our tutorial library [http://wiki.na-mic.org/Wiki/index.php/Downloads#Tutorials here].

===Hands-on training===
[[Image: Hands-on-Slicer-Training-2009.png|150px|left]]
We offer a variety of hands-on training experiences to increase the impact of our training program. For example, at the 2009 Radiology Society of North America (RSNA) meeting (see figure), Slicer 3D was used in a 90-minute workshop entitled “Quantitative Medical Imaging for Clinical Research and Practice” co-sponsored by the national CTSC Imaging Working Group Education Subcommittee. The event was completely subscribed; with standing room only (~100 attendees).
Access our calender of upcoming events [http://www.na-mic.org/Wiki/index.php/Events here].

===Validation methodology and practice===
[[Image: Xu-MICCAI2010-fig3.png|442px|left|]] Validation plays an important role in the assessment of algorithm performance that benefits both developers and users. Among the challenges of validating segmentation and registration algorithms for patient-specific analyses are (1) definition of appropriate metrics to measure differences among tools and across a sequence of images of the same patient; (2) evaluation of the significance of the differences observed, and (3) comparison to a gold standard, where available. Validation enables developers to evaluate the performance and limitations of their tools and to identify areas for improvement. In addition, validation provides users with the ability to compare different tools in a standardized way. For example, a retrospective validation analysis of the clinical accuracy of MRI-guided robotic biopsy for prostate cancer developed by the Prostate DBP is shown in the figure [4]. We have developed a portfolio of validation approaches for image segmentation through the organization of Grand Challenge workshops at the Medical Image Computing Computer-Assisted Intervention (MICCAI) Conference, and through our pioneering initiative in the standardized evaluation of single-tensor imaging tractography algorithms. These approaches form the basis of our approach to new algorithm and software development methodologies.

==Suggested Reading==

# How People Learn: Brain, Mind, Experience, and School. John D. Bransford, Ann L. Brown, and Rodney R. Cocking, editors; National Research Council, NATIONAL ACADEMY PRESS, Washington, D.C. 1999 (Free [http://www.nap.edu/catalog.php?record_id=6160 online text]).
# Lai I, Gollub R, Hoge R, Greve D, Vangel M, Poldrack R, Greenberg J. Teaching Statistical Analysis of fMRI Data. Proceedings of the American Society for Engineering Education (CD-ROM DEStech Publications) Session 2109: 11 pages (2003).
# Pujol S., Kikinis R., Gollub R. [http://www.na-mic.org/publications/item/view/1187 Lowering the Barriers Inherent in Translating Advances in Neuroimage Analysis to Clinical Research Applications.] Acad Radiol. 2008 Jan;15(1):114-8. PMID: 18078914. PMCID: PMC2234595.
# Xu H., Lasso A., Vikal S., Guion P., Krieger A., Kaushal A., Whitcomb L.L., Fichtinger G. [http://www.na-mic.org/publications/item/view/1919 MRI-guided Robotic Prostate Biopsy: A Clinical Accuracy Validation.] Int Conf Med Image Comput Comput Assist Interv. 2010;13(Pt 3):383-91. PMID: 20879423.

Special:Badtitle/NS100:Service

2010-12-06T19:07:48Z

Medcomparts:

__NOTOC__
=Service=
'''PI: Will Schroeder, Ph.D., Kitware, Inc.'''
[[Image:Big-Service-Logo.png|150px|left]]
The Service core is responsible for the design and operation of the collaborative computing infrastructure that supports the research and outreach activities of the NA-MIC community. This infrastructure enables NA-MIC research to have a significant and lasting impact on the broader field of medical image analysis. The elements of this design are embodied in a community-based infrastructure that encourages research and development and promotes open science.

Community-based infrastructure, as its name implies, addresses the demands of developers and users while maintaining the ideals of the community. It enforces standards for coding style, documentation, and testing. Open science is achieved when the workproduct created by individual members or groups within the community is sufficiently documented and shared such that it can be replicated and used as a foundation for derivative work. The cost of participating in this open environment, however, must be minimized. When the burden of the infrastructure outweighs its benefits, the growth of the community and the spread of open science are hindered. The NA-MIC infrastructure offers clear benefits to developers and users while promoting the ideals of NA-MIC and open science, posing minimal burden on the community. The integrity of this infrastructure is maintained by our commitment to:
 
==Focus Areas==
#Maintain open licensing
#Support the delivery of quality software
#Support the development of useful algorithms
#Gather and respond to feedback from the community
#Facilitate documentation, software, and data-sharing

===Maintain Open Licensing===
Successful, community-based software requires the active involvement of developers and users with priorities that span the essential components of software development, .e.g., coding, algorithms, testing, and documentation. No single organization can meet all of those priorities with equal vigor: academics emphasize algorithms, programmers emphasize coding, teachers emphasize documentation/training, and industry emphasizes testing. The software must be distributed under a license that is suitable for both academics and industry. This is essential to attracting the diversity the platform needs to succeed.

The NA-MIC Kit is distributed under a BSD-style license, which permits royalty-free use of the NA-MIC Kit software and data for both academic and commercial applications. Licensing terms are clearly posted in the headers of the NA-MIC Kit code, in the text on NA-MIC websites, and in the “About” message of NA-MIC Kit applications. Code that infringes on patents or contains viral licenses is vigilantly avoided. All members of the NA-MIC community agree to be bound by the terms of the license for the good of the community and open science.

===Support the Delivery of Quality Software===
Large-scale, collaborative software development demands rigorous software processes to support the many activities that combine for effective software development. These include requirements generation, implementation, testing, documentation, distribution, and reports generation. Once established, these processes must be enforced. To maintain the integrity of the NA-MIC platform, we have identified champions, a common practice in large-scale software development, which have the authority to identify and correct deviations from standard practices. Our experience with VTK and ITK shows that novice developers occasionally resist standard practices because of the perceived coding overhead. Consequently, our infrastructure is designed to minimize the duration and magnitude of that overhead to encourage participation and ensure that the benefits of our software processes can be fully realized.

===Support the Delivery of Useful Algorithms===
NA-MIC facilitates the development of useful algorithms by fostering open communication and providing specialized infrastructure for algorithms validation, including distributed computing for parameter space explorations, algorithm explorations, algorithm comparisons, and longitudinal studies.

===Gather and Respond to Feedback===
Fostering communication early in the development process identifies synergies, avoids duplication of effort, and maintains a common design pattern. The key steps to guiding project development are: (1) share ideas, (2) collect votes from the community to establish which ideas are favored, and (3) respond, implement, and then repeat the process.

===Facilitate the Sharing of Documentation, Software, and Data===
Information sharing (software, documentation, data) is the foundation of NA-MIC and essential to accelerating the pace of research in the field of medical image analysis. This foundation is preserved by monitoring our communications infrastructure, supporting software modularity, holding open design discussions, hosting data repositories for use within and beyond NA-MIC, maintaining the PubDB repository of NA-MIC publications, and recognizing contributors. In all of these endeavors, acknowledging contributors is important to encouraging community involvement and a high priority for the Service core, which ensures that communication and publication channels carry appropriate acknowledgments for all contributors.

Special:Badtitle/NS100:Dissemination

2010-12-06T19:05:56Z

Medcomparts: /* Impact through collaborations */

__NOTOC__
=Dissemination=
'''Co-PIs: Tina Kapur, Ph.D., BWH and Steve Pieper, Ph.D., Isomics'''

[[Image:Big-Dissemination-Logo.png|150px|left]]
As a component of NA-MIC's Outreach activities, Dissemination is closely allied with Service and Training. Our primary objective is to facilitate others to learn, teach, and perform biomedical and behavioral research using the free and open source (FOSS) NA-MIC Kit, as well as the novel methodologies and techniques developed by NA-MIC investigators. Given the complex, specialized nature of NA-MIC's technology, it is encumbent upon our organization to provide top-notch technical support to individuals with a range of expertise. We complement the pedagogical approach of the Training core, which advances subject matter expertise, by supporting the broad community of biomedical researchers who use NA-MIC technology as a key component of their medical image analysis approach. We support this community through a variety of means: (1) maintaining an extensive web-presence that includes easy access to NA-MIC publications, software, and data, (2) organizing our “flagship” Project Week working events, (3) nurturing an “open organization” through active daily use of our public wiki to organize and document our progress, (4) organizing “birds of a feather” meetings on timely topics, and (5) developing close bi-directional collaborations with external researchers who either use or strengthen the NA-MIC Kit. These efforts actively sustain a community of like-minded researchers whose work, in turn, amplifies the scope and utility of NA-MIC activities.

==FOCUS AREAS==
As the overall identity of NA-MIC transitions from a provider of medical image analysis technologies to a provider of integrated biomedical research solutions, these changes are reflected in our organizational objectives.

===Impact through collaborations===
[[Image:HexQual5-Dec06small.png|150 px|left]]
‎In addition to actively encouraging new collaborations, we support a network of 21 funded collaborations. These collaborative groups are actively working to use and expand the NA-MIC Kit to address specific biomedical problems across a wide range of organ systems and pathologies. Access our collaboration partners [http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations here]

===Outreach events===
[[Image:Dissem1.png|150px|left|thumb|Programming session during Project Week]]
The outreach meetings, including hands-on training workshops and working research events, serve the two-fold purpose of building an active user community around the NA-MIC Kit and gathering in-person feedback to improve the materials that form the basis of the online learning resources mentioned above. Each year we offer a host of clinically oriented Project Events, which feature hands-on training and research using the end-to-end solutions developed by previous and current DBPs (i.e., Lupus, Prostate Cancer, Autism, Schizophrenia/velocardiofacial syndrome, Atrial Fibrillation, Huntington's Disease, Head and Neck Cancer, Traumatic Brain Injury).
Access our calender of upcoming events [http://www.na-mic.org/Wiki/index.php/Engineering:Programming_Events here].

Special:Badtitle/NS100:Dissemination

2010-12-06T19:04:47Z

Medcomparts: /* Outreach events */

__NOTOC__
=Dissemination=
'''Co-PIs: Tina Kapur, Ph.D., BWH and Steve Pieper, Ph.D., Isomics'''

[[Image:Big-Dissemination-Logo.png|150px|left]]
As a component of NA-MIC's Outreach activities, Dissemination is closely allied with Service and Training. Our primary objective is to facilitate others to learn, teach, and perform biomedical and behavioral research using the free and open source (FOSS) NA-MIC Kit, as well as the novel methodologies and techniques developed by NA-MIC investigators. Given the complex, specialized nature of NA-MIC's technology, it is encumbent upon our organization to provide top-notch technical support to individuals with a range of expertise. We complement the pedagogical approach of the Training core, which advances subject matter expertise, by supporting the broad community of biomedical researchers who use NA-MIC technology as a key component of their medical image analysis approach. We support this community through a variety of means: (1) maintaining an extensive web-presence that includes easy access to NA-MIC publications, software, and data, (2) organizing our “flagship” Project Week working events, (3) nurturing an “open organization” through active daily use of our public wiki to organize and document our progress, (4) organizing “birds of a feather” meetings on timely topics, and (5) developing close bi-directional collaborations with external researchers who either use or strengthen the NA-MIC Kit. These efforts actively sustain a community of like-minded researchers whose work, in turn, amplifies the scope and utility of NA-MIC activities.

==FOCUS AREAS==
As the overall identity of NA-MIC transitions from a provider of medical image analysis technologies to a provider of integrated biomedical research solutions, these changes are reflected in our organizational objectives.

===Impact through collaborations===
[[Image:HexQual5-Dec06small.png|150 px|left]]
‎In addition to actively encouraging new collaborations, we support a network of 21 funded collaborations. These collaborative groups are actively working to use and expand the NA-MIC Kit to address specific biomedical problems across a wide range of organ systems and pathologies.

===Outreach events===
[[Image:Dissem1.png|150px|left|thumb|Programming session during Project Week]]
The outreach meetings, including hands-on training workshops and working research events, serve the two-fold purpose of building an active user community around the NA-MIC Kit and gathering in-person feedback to improve the materials that form the basis of the online learning resources mentioned above. Each year we offer a host of clinically oriented Project Events, which feature hands-on training and research using the end-to-end solutions developed by previous and current DBPs (i.e., Lupus, Prostate Cancer, Autism, Schizophrenia/velocardiofacial syndrome, Atrial Fibrillation, Huntington's Disease, Head and Neck Cancer, Traumatic Brain Injury).
Access our calender of upcoming events [http://www.na-mic.org/Wiki/index.php/Engineering:Programming_Events here].

Special:Badtitle/NS100:Dissemination

2010-12-06T19:04:34Z

Medcomparts: /* Impact through collaborations */

__NOTOC__
=Dissemination=
'''Co-PIs: Tina Kapur, Ph.D., BWH and Steve Pieper, Ph.D., Isomics'''

[[Image:Big-Dissemination-Logo.png|150px|left]]
As a component of NA-MIC's Outreach activities, Dissemination is closely allied with Service and Training. Our primary objective is to facilitate others to learn, teach, and perform biomedical and behavioral research using the free and open source (FOSS) NA-MIC Kit, as well as the novel methodologies and techniques developed by NA-MIC investigators. Given the complex, specialized nature of NA-MIC's technology, it is encumbent upon our organization to provide top-notch technical support to individuals with a range of expertise. We complement the pedagogical approach of the Training core, which advances subject matter expertise, by supporting the broad community of biomedical researchers who use NA-MIC technology as a key component of their medical image analysis approach. We support this community through a variety of means: (1) maintaining an extensive web-presence that includes easy access to NA-MIC publications, software, and data, (2) organizing our “flagship” Project Week working events, (3) nurturing an “open organization” through active daily use of our public wiki to organize and document our progress, (4) organizing “birds of a feather” meetings on timely topics, and (5) developing close bi-directional collaborations with external researchers who either use or strengthen the NA-MIC Kit. These efforts actively sustain a community of like-minded researchers whose work, in turn, amplifies the scope and utility of NA-MIC activities.

==FOCUS AREAS==
As the overall identity of NA-MIC transitions from a provider of medical image analysis technologies to a provider of integrated biomedical research solutions, these changes are reflected in our organizational objectives.

===Impact through collaborations===
[[Image:HexQual5-Dec06small.png|150 px|left]]
‎In addition to actively encouraging new collaborations, we support a network of 21 funded collaborations. These collaborative groups are actively working to use and expand the NA-MIC Kit to address specific biomedical problems across a wide range of organ systems and pathologies.

==Outreach events==
[[Image:Dissem1.png|150px|left|thumb|Programming session during Project Week]]
The outreach meetings, including hands-on training workshops and working research events, serve the two-fold purpose of building an active user community around the NA-MIC Kit and gathering in-person feedback to improve the materials that form the basis of the online learning resources mentioned above. Each year we offer a host of clinically oriented Project Events, which feature hands-on training and research using the end-to-end solutions developed by previous and current DBPs (i.e., Lupus, Prostate Cancer, Autism, Schizophrenia/velocardiofacial syndrome, Atrial Fibrillation, Huntington's Disease, Head and Neck Cancer, Traumatic Brain Injury).
Access our calender of upcoming events [http://www.na-mic.org/Wiki/index.php/Engineering:Programming_Events here].

Special:Badtitle/NS100:Training

2010-12-06T19:03:16Z

Medcomparts: /* Validation Methodology and Practice */

__NOTOC__
=Training=
'''PI: Randy Gollub, M.D., MGH'''
[[Image:Big-Training-Logo.png|150px|left]]
The goal of Training is to lower barriers to effective communication between the clinical translational investigators and the computer scientists engaged in the development and application of medical image analysis and data management software tools for NA-MIC. These communities have diverse educational backgrounds and often do not share a common vocabulary or forum for exchanging ideas or valuable tools and solutions. Training addresses this gap by educating members of the biomedical clinical and research communities in the domains of knowledge relevant to the application of medical image analysis and its interface with computer science. Initially, the primary activity of Training was to develop and deliver hands-on learning experiences to clinicians, algorithm developers, and computer scientists to increase their competence in all aspects of medical image analysis. We used components of the NA-MIC Kit, primarily the 3D Slicer software, to teach the fundamentals of applied medical image processing and visualization. This approach enabled us to develop a single set of training materials that were equally well suited for constituents from all backgrounds, that is, clinicians, statisticians, and computer scientists. These tools further served to strengthen communication among these communities by defining and promulgating common vocabulary. Currently, we are expanding our education outreach effort to include a greater proportion of the clinical translational research community.

==Focus Areas==
===On-line learning resources===
[[Image:Arctic-i.png‎|150 px|left]]
Developing new on-line medical image analysis training materials is one of our top priorities. We are constantly adding to the materials and datasets available through our website. These training materials are developed for specific use cases gleaned from the Driving Biological Projects (DBPs) and require close collaboration among all cores (Outreach, Computer Science, DBPs, and external collaborators). All of our tutorials can be self-taught or administered by an instructor. Each tutorial follows the rubric established in How People Learn [1,2,3], which requires learner-centered, goal-oriented experiential teaching. Access downloads from our tutorial library [http://wiki.na-mic.org/Wiki/index.php/Downloads#Tutorials here].

===Hands-on training===
[[Image: Hands-on-Slicer-Training-2009.png|150px|left]]
We offer a variety of hands-on training experiences to increase the impact of our training program. For example, at the 2009 Radiology Society of North America (RSNA) meeting (see figure), Slicer 3D was used in a 90-minute workshop entitled “Quantitative Medical Imaging for Clinical Research and Practice” co-sponsored by the national CTSC Imaging Working Group Education Subcommittee. The event was completely subscribed; with standing room only (~100 attendees).
Access our calender of upcoming events [http://www.na-mic.org/Wiki/index.php/Events here].

===Validation methodology and practice===
[[Image: Xu-MICCAI2010-fig3.png|442px|left|]] Validation plays an important role in the assessment of algorithm performance that benefits both developers and users. Among the challenges of validating segmentation and registration algorithms for patient-specific analyses are (1) the definition of appropriate metrics to measure differences among tools and across a sequence of images of the same patient; (2) the evaluation of the significance of the differences observed, and (3) comparison to a gold standard, where available. Validation enables developers to evaluate the performance and limitations of their tools and to identify areas for improvement. In addition, validation provides users with the ability to compare different tools in a standardized way. For example, a retrospective validation analysis of the clinical accuracy of MRI-guided robotic biopsy for prostate cancer developed by the Prostate DBP is shown in the figure [4]. We have developed a portfolio of validation approaches for image segmentation through the organization of Grand Challenge workshops at the Medical Image Computing Computer-Assisted Intervention (MICCAI) Conference, and through our pioneering initiative in the standardized evaluation of single-tensor imaging tractography algorithms. These approaches form the basis of our approach to new algorithm and software development methodologies.

==Suggested Reading==

# How People Learn: Brain, Mind, Experience, and School. John D. Bransford, Ann L. Brown, and Rodney R. Cocking, editors; National Research Council, NATIONAL ACADEMY PRESS, Washington, D.C. 1999 (Free [http://www.nap.edu/catalog.php?record_id=6160 online text]).
# Lai I, Gollub R, Hoge R, Greve D, Vangel M, Poldrack R, Greenberg J. Teaching Statistical Analysis of fMRI Data. Proceedings of the American Society for Engineering Education (CD-ROM DEStech Publications) Session 2109: 11 pages (2003).
# Pujol S., Kikinis R., Gollub R. [http://www.na-mic.org/publications/item/view/1187 Lowering the Barriers Inherent in Translating Advances in Neuroimage Analysis to Clinical Research Applications.] Acad Radiol. 2008 Jan;15(1):114-8. PMID: 18078914. PMCID: PMC2234595.
# Xu H., Lasso A., Vikal S., Guion P., Krieger A., Kaushal A., Whitcomb L.L., Fichtinger G. [http://www.na-mic.org/publications/item/view/1919 MRI-guided Robotic Prostate Biopsy: A Clinical Accuracy Validation.] Int Conf Med Image Comput Comput Assist Interv. 2010;13(Pt 3):383-91. PMID: 20879423.

Special:Badtitle/NS100:Training

2010-12-06T19:03:03Z

Medcomparts: /* Hands-on Training */

__NOTOC__
=Training=
'''PI: Randy Gollub, M.D., MGH'''
[[Image:Big-Training-Logo.png|150px|left]]
The goal of Training is to lower barriers to effective communication between the clinical translational investigators and the computer scientists engaged in the development and application of medical image analysis and data management software tools for NA-MIC. These communities have diverse educational backgrounds and often do not share a common vocabulary or forum for exchanging ideas or valuable tools and solutions. Training addresses this gap by educating members of the biomedical clinical and research communities in the domains of knowledge relevant to the application of medical image analysis and its interface with computer science. Initially, the primary activity of Training was to develop and deliver hands-on learning experiences to clinicians, algorithm developers, and computer scientists to increase their competence in all aspects of medical image analysis. We used components of the NA-MIC Kit, primarily the 3D Slicer software, to teach the fundamentals of applied medical image processing and visualization. This approach enabled us to develop a single set of training materials that were equally well suited for constituents from all backgrounds, that is, clinicians, statisticians, and computer scientists. These tools further served to strengthen communication among these communities by defining and promulgating common vocabulary. Currently, we are expanding our education outreach effort to include a greater proportion of the clinical translational research community.

==Focus Areas==
===On-line learning resources===
[[Image:Arctic-i.png‎|150 px|left]]
Developing new on-line medical image analysis training materials is one of our top priorities. We are constantly adding to the materials and datasets available through our website. These training materials are developed for specific use cases gleaned from the Driving Biological Projects (DBPs) and require close collaboration among all cores (Outreach, Computer Science, DBPs, and external collaborators). All of our tutorials can be self-taught or administered by an instructor. Each tutorial follows the rubric established in How People Learn [1,2,3], which requires learner-centered, goal-oriented experiential teaching. Access downloads from our tutorial library [http://wiki.na-mic.org/Wiki/index.php/Downloads#Tutorials here].

===Hands-on training===
[[Image: Hands-on-Slicer-Training-2009.png|150px|left]]
We offer a variety of hands-on training experiences to increase the impact of our training program. For example, at the 2009 Radiology Society of North America (RSNA) meeting (see figure), Slicer 3D was used in a 90-minute workshop entitled “Quantitative Medical Imaging for Clinical Research and Practice” co-sponsored by the national CTSC Imaging Working Group Education Subcommittee. The event was completely subscribed; with standing room only (~100 attendees).
Access our calender of upcoming events [http://www.na-mic.org/Wiki/index.php/Events here].

===Validation Methodology and Practice===
[[Image: Xu-MICCAI2010-fig3.png|442px|left|]] Validation plays an important role in the assessment of algorithm performance that benefits both developers and users. Among the challenges of validating segmentation and registration algorithms for patient-specific analyses are (1) the definition of appropriate metrics to measure differences among tools and across a sequence of images of the same patient; (2) the evaluation of the significance of the differences observed, and (3) comparison to a gold standard, where available. Validation enables developers to evaluate the performance and limitations of their tools and to identify areas for improvement. In addition, validation provides users with the ability to compare different tools in a standardized way. For example, a retrospective validation analysis of the clinical accuracy of MRI-guided robotic biopsy for prostate cancer developed by the Prostate DBP is shown in the figure [4]. We have developed a portfolio of validation approaches for image segmentation through the organization of Grand Challenge workshops at the Medical Image Computing Computer-Assisted Intervention (MICCAI) Conference, and through our pioneering initiative in the standardized evaluation of single-tensor imaging tractography algorithms. These approaches form the basis of our approach to new algorithm and software development methodologies.

==Suggested Reading==

# How People Learn: Brain, Mind, Experience, and School. John D. Bransford, Ann L. Brown, and Rodney R. Cocking, editors; National Research Council, NATIONAL ACADEMY PRESS, Washington, D.C. 1999 (Free [http://www.nap.edu/catalog.php?record_id=6160 online text]).
# Lai I, Gollub R, Hoge R, Greve D, Vangel M, Poldrack R, Greenberg J. Teaching Statistical Analysis of fMRI Data. Proceedings of the American Society for Engineering Education (CD-ROM DEStech Publications) Session 2109: 11 pages (2003).
# Pujol S., Kikinis R., Gollub R. [http://www.na-mic.org/publications/item/view/1187 Lowering the Barriers Inherent in Translating Advances in Neuroimage Analysis to Clinical Research Applications.] Acad Radiol. 2008 Jan;15(1):114-8. PMID: 18078914. PMCID: PMC2234595.
# Xu H., Lasso A., Vikal S., Guion P., Krieger A., Kaushal A., Whitcomb L.L., Fichtinger G. [http://www.na-mic.org/publications/item/view/1919 MRI-guided Robotic Prostate Biopsy: A Clinical Accuracy Validation.] Int Conf Med Image Comput Comput Assist Interv. 2010;13(Pt 3):383-91. PMID: 20879423.

Special:Badtitle/NS100:Training

2010-12-06T19:02:45Z

Medcomparts:

__NOTOC__
=Training=
'''PI: Randy Gollub, M.D., MGH'''
[[Image:Big-Training-Logo.png|150px|left]]
The goal of Training is to lower barriers to effective communication between the clinical translational investigators and the computer scientists engaged in the development and application of medical image analysis and data management software tools for NA-MIC. These communities have diverse educational backgrounds and often do not share a common vocabulary or forum for exchanging ideas or valuable tools and solutions. Training addresses this gap by educating members of the biomedical clinical and research communities in the domains of knowledge relevant to the application of medical image analysis and its interface with computer science. Initially, the primary activity of Training was to develop and deliver hands-on learning experiences to clinicians, algorithm developers, and computer scientists to increase their competence in all aspects of medical image analysis. We used components of the NA-MIC Kit, primarily the 3D Slicer software, to teach the fundamentals of applied medical image processing and visualization. This approach enabled us to develop a single set of training materials that were equally well suited for constituents from all backgrounds, that is, clinicians, statisticians, and computer scientists. These tools further served to strengthen communication among these communities by defining and promulgating common vocabulary. Currently, we are expanding our education outreach effort to include a greater proportion of the clinical translational research community.

==Focus Areas==
===On-line learning resources===
[[Image:Arctic-i.png‎|150 px|left]]
Developing new on-line medical image analysis training materials is one of our top priorities. We are constantly adding to the materials and datasets available through our website. These training materials are developed for specific use cases gleaned from the Driving Biological Projects (DBPs) and require close collaboration among all cores (Outreach, Computer Science, DBPs, and external collaborators). All of our tutorials can be self-taught or administered by an instructor. Each tutorial follows the rubric established in How People Learn [1,2,3], which requires learner-centered, goal-oriented experiential teaching. Access downloads from our tutorial library [http://wiki.na-mic.org/Wiki/index.php/Downloads#Tutorials here].

===Hands-on Training===
[[Image: Hands-on-Slicer-Training-2009.png|150px|left]]
We offer a variety of hands-on training experiences to increase the impact of our training program. For example, at the 2009 Radiology Society of North America (RSNA) meeting (see figure), Slicer 3D was used in a 90-minute workshop entitled “Quantitative Medical Imaging for Clinical Research and Practice” co-sponsored by the national CTSC Imaging Working Group Education Subcommittee. The event was completely subscribed; with standing room only (~100 attendees).
Access our calender of upcoming events [http://www.na-mic.org/Wiki/index.php/Events here].

===Validation Methodology and Practice===
[[Image: Xu-MICCAI2010-fig3.png|442px|left|]] Validation plays an important role in the assessment of algorithm performance that benefits both developers and users. Among the challenges of validating segmentation and registration algorithms for patient-specific analyses are (1) the definition of appropriate metrics to measure differences among tools and across a sequence of images of the same patient; (2) the evaluation of the significance of the differences observed, and (3) comparison to a gold standard, where available. Validation enables developers to evaluate the performance and limitations of their tools and to identify areas for improvement. In addition, validation provides users with the ability to compare different tools in a standardized way. For example, a retrospective validation analysis of the clinical accuracy of MRI-guided robotic biopsy for prostate cancer developed by the Prostate DBP is shown in the figure [4]. We have developed a portfolio of validation approaches for image segmentation through the organization of Grand Challenge workshops at the Medical Image Computing Computer-Assisted Intervention (MICCAI) Conference, and through our pioneering initiative in the standardized evaluation of single-tensor imaging tractography algorithms. These approaches form the basis of our approach to new algorithm and software development methodologies.

==Suggested Reading==

# How People Learn: Brain, Mind, Experience, and School. John D. Bransford, Ann L. Brown, and Rodney R. Cocking, editors; National Research Council, NATIONAL ACADEMY PRESS, Washington, D.C. 1999 (Free [http://www.nap.edu/catalog.php?record_id=6160 online text]).
# Lai I, Gollub R, Hoge R, Greve D, Vangel M, Poldrack R, Greenberg J. Teaching Statistical Analysis of fMRI Data. Proceedings of the American Society for Engineering Education (CD-ROM DEStech Publications) Session 2109: 11 pages (2003).
# Pujol S., Kikinis R., Gollub R. [http://www.na-mic.org/publications/item/view/1187 Lowering the Barriers Inherent in Translating Advances in Neuroimage Analysis to Clinical Research Applications.] Acad Radiol. 2008 Jan;15(1):114-8. PMID: 18078914. PMCID: PMC2234595.
# Xu H., Lasso A., Vikal S., Guion P., Krieger A., Kaushal A., Whitcomb L.L., Fichtinger G. [http://www.na-mic.org/publications/item/view/1919 MRI-guided Robotic Prostate Biopsy: A Clinical Accuracy Validation.] Int Conf Med Image Comput Comput Assist Interv. 2010;13(Pt 3):383-91. PMID: 20879423.

Special:Badtitle/NS100:Training

2010-12-06T19:00:44Z

Medcomparts:

__NOTOC__
=Training=
'''PI: Randy Gollub, M.D., MGH'''
[[Image:Big-Training-Logo.png|150px|left]]
The goal of Training is to lower barriers to effective communication between the clinical translational investigators and the computer scientists engaged in the development and application of medical image analysis and data management software tools for NA-MIC. These communities have diverse educational backgrounds and often do not share a common vocabulary or forum for exchanging ideas or valuable tools and solutions. Training addresses this gap by educating members of the biomedical clinical and research communities in the domains of knowledge relevant to the application of medical image analysis and its interface with computer science. Initially, the primary activity of Training was to develop and deliver hands-on learning experiences to clinicians, algorithm developers, and computer scientists to increase their competence in all aspects of medical image analysis. We used components of the NA-MIC Kit, primarily the 3D Slicer software, to teach the fundamentals of applied medical image processing and visualization. This approach enabled us to develop a single set of training materials that were equally well suited for constituents from all backgrounds, that is, clinicians, statisticians, and computer scientists. These tools further served to strengthen communication among these communities by defining and promulgating common vocabulary. Currently, we are expanding our education outreach effort to include a greater proportion of the clinical translational research community.

==Focus Areas==
==On-line learning resources==
[[Image:Arctic-i.png‎|150 px|left]]
Developing new on-line medical image analysis training materials is one of our top priorities. We are constantly adding to the materials and datasets available through our website. These training materials are developed for specific use cases gleaned from the Driving Biological Projects (DBPs) and require close collaboration among all cores (Outreach, Computer Science, DBPs, and external collaborators). All of our tutorials can be self-taught or administered by an instructor. Each tutorial follows the rubric established in How People Learn [1,2,3], which requires learner-centered, goal-oriented experiential teaching. Access downloads from our tutorial library [http://wiki.na-mic.org/Wiki/index.php/Downloads#Tutorials here].

===Hands-on Training===
[[Image: Hands-on-Slicer-Training-2009.png|150px|left]]
We offer a variety of hands-on training experiences to increase the impact of our training program. For example, at the 2009 Radiology Society of North America (RSNA) meeting (see figure), Slicer 3D was used in a 90-minute workshop entitled “Quantitative Medical Imaging for Clinical Research and Practice” co-sponsored by the national CTSC Imaging Working Group Education Subcommittee. The event was completely subscribed; with standing room only (~100 attendees).
Access our calender of upcoming events [http://www.na-mic.org/Wiki/index.php/Events here].

===Validation Methodology and Practice===
[[Image: Xu-MICCAI2010-fig3.png|442px|left|]] Validation plays an important role in the assessment of algorithm performance that benefits both developers and users. Among the challenges of validating segmentation and registration algorithms for patient-specific analyses are (1) the definition of appropriate metrics to measure differences among tools and across a sequence of images of the same patient; (2) the evaluation of the significance of the differences observed, and (3) comparison to a gold standard, where available. Validation enables developers to evaluate the performance and limitations of their tools and to identify areas for improvement. In addition, validation provides users with the ability to compare different tools in a standardized way. For example, a retrospective validation analysis of the clinical accuracy of MRI-guided robotic biopsy for prostate cancer developed by the Prostate DBP is shown in the figure [4]. We have developed a portfolio of validation approaches for image segmentation through the organization of Grand Challenge workshops at the Medical Image Computing Computer-Assisted Intervention (MICCAI) Conference, and through our pioneering initiative in the standardized evaluation of single-tensor imaging tractography algorithms. These approaches form the basis of our approach to new algorithm and software development methodologies.

==Suggested Reading==

# How People Learn: Brain, Mind, Experience, and School. John D. Bransford, Ann L. Brown, and Rodney R. Cocking, editors; National Research Council, NATIONAL ACADEMY PRESS, Washington, D.C. 1999 (Free [http://www.nap.edu/catalog.php?record_id=6160 online text]).
# Lai I, Gollub R, Hoge R, Greve D, Vangel M, Poldrack R, Greenberg J. Teaching Statistical Analysis of fMRI Data. Proceedings of the American Society for Engineering Education (CD-ROM DEStech Publications) Session 2109: 11 pages (2003).
# Pujol S., Kikinis R., Gollub R. [http://www.na-mic.org/publications/item/view/1187 Lowering the Barriers Inherent in Translating Advances in Neuroimage Analysis to Clinical Research Applications.] Acad Radiol. 2008 Jan;15(1):114-8. PMID: 18078914. PMCID: PMC2234595.
# Xu H., Lasso A., Vikal S., Guion P., Krieger A., Kaushal A., Whitcomb L.L., Fichtinger G. [http://www.na-mic.org/publications/item/view/1919 MRI-guided Robotic Prostate Biopsy: A Clinical Accuracy Validation.] Int Conf Med Image Comput Comput Assist Interv. 2010;13(Pt 3):383-91. PMID: 20879423.

Special:Badtitle/NS100:Training

2010-12-06T18:54:59Z

Medcomparts: /* Validation Methodology and Practice */

__NOTOC__
=Training=
'''PI: Randy Gollub, M.D., MGH'''
[[Image:Big-Training-Logo.png|150px|left]]
The goal of Training is to lower barriers to effective communication between the clinical translational investigators and the computer scientists engaged in the development and application of medical image analysis and data management software tools for NA-MIC. These communities have diverse educational backgrounds and often do not share a common vocabulary or forum for exchanging ideas or valuable tools and solutions. Training addresses this gap by educating members of the biomedical clinical and research communities in the domains of knowledge relevant to the application of medical image analysis and its interface with computer science. Initially, the primary activity of Training was to develop and deliver hands-on learning experiences to clinicians, algorithm developers, and computer scientists to increase their competence in all aspects of medical image analysis. We used components of the NA-MIC Kit, primarily the 3D Slicer software, to teach the fundamentals of applied medical image processing and visualization. This approach enabled us to develop a single set of training materials that were equally well suited for constituents from all backgrounds, that is, clinicians, statisticians, and computer scientists. These tools further served to strengthen communication among these communities by defining and promulgating common vocabulary. Currently, we are expanding our education outreach effort to include a greater proportion of the clinical translational research community.

==Focus Areas==
==On-line learning resources==
[[Image:Arctic-i.png‎|150 px|left]]
Developing new on-line medical image analysis training materials is one of our top priorities. We are constantly adding to the materials and datasets available through our website. These training materials are developed for specific use cases gleaned from the Driving Biological Projects (DBPs) and require close collaboration among all cores (Outreach, Computer Science, DBPs, and external collaborators). All of our tutorials can be self-taught or administered by an instructor. Each tutorial follows the rubric established in How People Learn [1,2,3], which requires learner-centered, goal-oriented experiential teaching. Access downloads from our tutorial library [http://wiki.na-mic.org/Wiki/index.php/Downloads#Tutorials here].

===Hands-on Training===
[[Image: Hands-on-Slicer-Training-2009.png|150px|left]]
We offer a variety of hands-on training experiences to increase the impact of our training program. For example, at the 2009 Radiology Society of North America (RSNA) meeting (see figure), Slicer 3D was used in a 90-minute workshop entitled “Quantitative Medical Imaging for Clinical Research and Practice” co-sponsored by the national CTSC Imaging Working Group Education Subcommittee. The event was completely subscribed; with standing room only (~100 attendees).
Access our calender of upcoming events [http://www.na-mic.org/Wiki/index.php/Events here].

===Validation Methodology and Practice===
[[Image: Xu-MICCAI2010-fig3.png|442px|left|]] As an alliance of 14 different sites, NA-MIC has access to algorithms, software, data, and ground truth expertise from many world-leading computer and clinical scientists. This access puts us in a unique position to lead effective comparative research efforts. We have developed a portfolio of validation approaches for image segmentation through the organization of Grand Challenge workshops at the Medical Image Computing Computer-Assisted Intervention (MICCAI) Conference, and through our pioneering initiative in the standardized evaluation of single-tensor imaging tractography algorithms. These approaches form the basis of our approach to new algorithm and software development methodologies.

==Suggested Reading==

# How People Learn: Brain, Mind, Experience, and School. John D. Bransford, Ann L. Brown, and Rodney R. Cocking, editors; National Research Council, NATIONAL ACADEMY PRESS, Washington, D.C. 1999 (Free [http://www.nap.edu/catalog.php?record_id=6160 online text]).
# Lai I, Gollub R, Hoge R, Greve D, Vangel M, Poldrack R, Greenberg J. Teaching Statistical Analysis of fMRI Data. Proceedings of the American Society for Engineering Education (CD-ROM DEStech Publications) Session 2109: 11 pages (2003).
# Pujol S., Kikinis R., Gollub R. [http://www.na-mic.org/publications/item/view/1187 Lowering the Barriers Inherent in Translating Advances in Neuroimage Analysis to Clinical Research Applications.] Acad Radiol. 2008 Jan;15(1):114-8. PMID: 18078914. PMCID: PMC2234595.
# Xu H., Lasso A., Vikal S., Guion P., Krieger A., Kaushal A., Whitcomb L.L., Fichtinger G. [http://www.na-mic.org/publications/item/view/1919 MRI-guided Robotic Prostate Biopsy: A Clinical Accuracy Validation.] Int Conf Med Image Comput Comput Assist Interv. 2010;13(Pt 3):383-91. PMID: 20879423.

Special:Badtitle/NS100:Training

2010-12-06T18:53:33Z

Medcomparts: /* Validations methodology and practice */

__NOTOC__
=Training=
'''PI: Randy Gollub, M.D., MGH'''
[[Image:Big-Training-Logo.png|150px|left]]
The goal of Training is to lower barriers to effective communication between the clinical translational investigators and the computer scientists engaged in the development and application of medical image analysis and data management software tools for NA-MIC. These communities have diverse educational backgrounds and often do not share a common vocabulary or forum for exchanging ideas or valuable tools and solutions. Training addresses this gap by educating members of the biomedical clinical and research communities in the domains of knowledge relevant to the application of medical image analysis and its interface with computer science. Initially, the primary activity of Training was to develop and deliver hands-on learning experiences to clinicians, algorithm developers, and computer scientists to increase their competence in all aspects of medical image analysis. We used components of the NA-MIC Kit, primarily the 3D Slicer software, to teach the fundamentals of applied medical image processing and visualization. This approach enabled us to develop a single set of training materials that were equally well suited for constituents from all backgrounds, that is, clinicians, statisticians, and computer scientists. These tools further served to strengthen communication among these communities by defining and promulgating common vocabulary. Currently, we are expanding our education outreach effort to include a greater proportion of the clinical translational research community.

==Focus Areas==
==On-line learning resources==
[[Image:Arctic-i.png‎|150 px|left]]
Developing new on-line medical image analysis training materials is one of our top priorities. We are constantly adding to the materials and datasets available through our website. These training materials are developed for specific use cases gleaned from the Driving Biological Projects (DBPs) and require close collaboration among all cores (Outreach, Computer Science, DBPs, and external collaborators). All of our tutorials can be self-taught or administered by an instructor. Each tutorial follows the rubric established in How People Learn [1,2,3], which requires learner-centered, goal-oriented experiential teaching. Access downloads from our tutorial library [http://wiki.na-mic.org/Wiki/index.php/Downloads#Tutorials here].

===Hands-on Training===
[[Image: Hands-on-Slicer-Training-2009.png|150px|left]]
We offer a variety of hands-on training experiences to increase the impact of our training program. For example, at the 2009 Radiology Society of North America (RSNA) meeting (see figure), Slicer 3D was used in a 90-minute workshop entitled “Quantitative Medical Imaging for Clinical Research and Practice” co-sponsored by the national CTSC Imaging Working Group Education Subcommittee. The event was completely subscribed; with standing room only (~100 attendees).
Access our calender of upcoming events [http://www.na-mic.org/Wiki/index.php/Events here].

===Validation Methodology and Practice===
[[Image: Xu-MICCAI2010-fig3.png|442px|left|]] Validation plays an important role in the assessment of algorithm performance that benefits both developers and users. Among the challenges of validating segmentation and registration algorithms for patient-specific analyses are (1) the definition of appropriate metrics to measure differences among tools and across a sequence of images of the same patient; (2) the evaluation of the significance of the differences observed, and (3) comparison to a gold standard, where available. Validation enables developers to evaluate the performance and limitations of their tools and to identify areas for improvement. In addition, validation provides users with the ability to compare different tools in a standardized way. For example, a retrospective validation analysis of the clinical accuracy of MRI-guided robotic biopsy for prostate cancer developed by the Prostate DBP is shown in the figure [4]. This work was organized through and presented at the Medical Image Computing and Computer Assisted Intervention (MICCAI) conference.

==Suggested Reading==

# How People Learn: Brain, Mind, Experience, and School. John D. Bransford, Ann L. Brown, and Rodney R. Cocking, editors; National Research Council, NATIONAL ACADEMY PRESS, Washington, D.C. 1999 (Free [http://www.nap.edu/catalog.php?record_id=6160 online text]).
# Lai I, Gollub R, Hoge R, Greve D, Vangel M, Poldrack R, Greenberg J. Teaching Statistical Analysis of fMRI Data. Proceedings of the American Society for Engineering Education (CD-ROM DEStech Publications) Session 2109: 11 pages (2003).
# Pujol S., Kikinis R., Gollub R. [http://www.na-mic.org/publications/item/view/1187 Lowering the Barriers Inherent in Translating Advances in Neuroimage Analysis to Clinical Research Applications.] Acad Radiol. 2008 Jan;15(1):114-8. PMID: 18078914. PMCID: PMC2234595.
# Xu H., Lasso A., Vikal S., Guion P., Krieger A., Kaushal A., Whitcomb L.L., Fichtinger G. [http://www.na-mic.org/publications/item/view/1919 MRI-guided Robotic Prostate Biopsy: A Clinical Accuracy Validation.] Int Conf Med Image Comput Comput Assist Interv. 2010;13(Pt 3):383-91. PMID: 20879423.

Special:Badtitle/NS100:Dissemination

2010-12-06T18:51:25Z

Medcomparts:

__NOTOC__
=Dissemination=
'''Co-PIs: Tina Kapur, Ph.D., BWH and Steve Pieper, Ph.D., Isomics'''

[[Image:Big-Dissemination-Logo.png|150px|left]]
As a component of NA-MIC's Outreach activities, Dissemination is closely allied with Service and Training. Our primary objective is to facilitate others to learn, teach, and perform biomedical and behavioral research using the free and open source (FOSS) NA-MIC Kit, as well as the novel methodologies and techniques developed by NA-MIC investigators. Given the complex, specialized nature of NA-MIC's technology, it is encumbent upon our organization to provide top-notch technical support to individuals with a range of expertise. We complement the pedagogical approach of the Training core, which advances subject matter expertise, by supporting the broad community of biomedical researchers who use NA-MIC technology as a key component of their medical image analysis approach. We support this community through a variety of means: (1) maintaining an extensive web-presence that includes easy access to NA-MIC publications, software, and data, (2) organizing our “flagship” Project Week working events, (3) nurturing an “open organization” through active daily use of our public wiki to organize and document our progress, (4) organizing “birds of a feather” meetings on timely topics, and (5) developing close bi-directional collaborations with external researchers who either use or strengthen the NA-MIC Kit. These efforts actively sustain a community of like-minded researchers whose work, in turn, amplifies the scope and utility of NA-MIC activities.

==FOCUS AREAS==
As the overall identity of NA-MIC transitions from a provider of medical image analysis technologies to a provider of integrated biomedical research solutions, these changes are reflected in our organizational objectives.

==Impact through collaborations==
[[Image:HexQual5-Dec06small.png|150 px|left]]
‎In addition to actively encouraging new collaborations, we support a network of 21 funded collaborations. These collaborative groups are actively working to use and expand the NA-MIC Kit to address specific biomedical problems across a wide range of organ systems and pathologies.

==Outreach events==
[[Image:Dissem1.png|150px|left|thumb|Programming session during Project Week]]
The outreach meetings, including hands-on training workshops and working research events, serve the two-fold purpose of building an active user community around the NA-MIC Kit and gathering in-person feedback to improve the materials that form the basis of the online learning resources mentioned above. Each year we offer a host of clinically oriented Project Events, which feature hands-on training and research using the end-to-end solutions developed by previous and current DBPs (i.e., Lupus, Prostate Cancer, Autism, Schizophrenia/velocardiofacial syndrome, Atrial Fibrillation, Huntington's Disease, Head and Neck Cancer, Traumatic Brain Injury).
Access our calender of upcoming events [http://www.na-mic.org/Wiki/index.php/Engineering:Programming_Events here].

Special:Badtitle/NS100:Training

2010-12-06T18:49:34Z

Medcomparts:

__NOTOC__
=Training=
'''PI: Randy Gollub, M.D., MGH'''
[[Image:Big-Training-Logo.png|150px|left]]
The goal of Training is to lower barriers to effective communication between the clinical translational investigators and the computer scientists engaged in the development and application of medical image analysis and data management software tools for NA-MIC. These communities have diverse educational backgrounds and often do not share a common vocabulary or forum for exchanging ideas or valuable tools and solutions. Training addresses this gap by educating members of the biomedical clinical and research communities in the domains of knowledge relevant to the application of medical image analysis and its interface with computer science. Initially, the primary activity of Training was to develop and deliver hands-on learning experiences to clinicians, algorithm developers, and computer scientists to increase their competence in all aspects of medical image analysis. We used components of the NA-MIC Kit, primarily the 3D Slicer software, to teach the fundamentals of applied medical image processing and visualization. This approach enabled us to develop a single set of training materials that were equally well suited for constituents from all backgrounds, that is, clinicians, statisticians, and computer scientists. These tools further served to strengthen communication among these communities by defining and promulgating common vocabulary. Currently, we are expanding our education outreach effort to include a greater proportion of the clinical translational research community.

==Focus Areas==
==On-line learning resources==
[[Image:Arctic-i.png‎|150 px|left]]
Developing new on-line medical image analysis training materials is one of our top priorities. We are constantly adding to the materials and datasets available through our website. These training materials are developed for specific use cases gleaned from the Driving Biological Projects (DBPs) and require close collaboration among all cores (Outreach, Computer Science, DBPs, and external collaborators). All of our tutorials can be self-taught or administered by an instructor. Each tutorial follows the rubric established in How People Learn [1,2,3], which requires learner-centered, goal-oriented experiential teaching. Access downloads from our tutorial library [http://wiki.na-mic.org/Wiki/index.php/Downloads#Tutorials here].

===Hands-on Training===
[[Image: Hands-on-Slicer-Training-2009.png|150px|left]]
We offer a variety of hands-on training experiences to increase the impact of our training program. For example, at the 2009 Radiology Society of North America (RSNA) meeting (see figure), Slicer 3D was used in a 90-minute workshop entitled “Quantitative Medical Imaging for Clinical Research and Practice” co-sponsored by the national CTSC Imaging Working Group Education Subcommittee. The event was completely subscribed; with standing room only (~100 attendees).
Access our calender of upcoming events [http://www.na-mic.org/Wiki/index.php/Events here].

===Validation Methodology and Practice===
[[Image: Xu-MICCAI2010-fig3.png|442px|left|]] Validation plays an important role in the assessment of algorithm performance that benefits both developers and users. Among the challenges of validating segmentation and registration algorithms for patient-specific analyses are (1) the definition of appropriate metrics to measure differences among tools and across a sequence of images of the same patient; (2) the evaluation of the significance of the differences observed, and (3) comparison to a gold standard, where available. Validation enables developers to evaluate the performance and limitations of their tools and to identify areas for improvement. In addition, validation provides users with the ability to compare different tools in a standardized way. For example, a retrospective validation analysis of the clinical accuracy of MRI-guided robotic biopsy for prostate cancer developed by the Prostate DBP is shown in the figure [4]. This work was organized through and presented at the Medical Image Computing and Computer Assisted Intervention (MICCAI) conference.

==Validations methodology and practice==
As an alliance of 14 different sites, NA-MIC has access to algorithms, software, data, and ground truth expertise from many world-leading computer and clinical scientists. This access puts us in a unique position to lead effective comparative research efforts. We have developed a portfolio of validation approaches for image segmentation through the organization of Grand Challenge workshops at the Medical Image Computing Computer-Assisted Intervention (MICCAI) Conference, and through our pioneering initiative in the standardized evaluation of single-tensor imaging tractography algorithms. These approaches form the basis of our approach to new algorithm and software development methodologies.

==Suggested Reading==

# How People Learn: Brain, Mind, Experience, and School. John D. Bransford, Ann L. Brown, and Rodney R. Cocking, editors; National Research Council, NATIONAL ACADEMY PRESS, Washington, D.C. 1999 (Free [http://www.nap.edu/catalog.php?record_id=6160 online text]).
# Lai I, Gollub R, Hoge R, Greve D, Vangel M, Poldrack R, Greenberg J. Teaching Statistical Analysis of fMRI Data. Proceedings of the American Society for Engineering Education (CD-ROM DEStech Publications) Session 2109: 11 pages (2003).
# Pujol S., Kikinis R., Gollub R. [http://www.na-mic.org/publications/item/view/1187 Lowering the Barriers Inherent in Translating Advances in Neuroimage Analysis to Clinical Research Applications.] Acad Radiol. 2008 Jan;15(1):114-8. PMID: 18078914. PMCID: PMC2234595.
# Xu H., Lasso A., Vikal S., Guion P., Krieger A., Kaushal A., Whitcomb L.L., Fichtinger G. [http://www.na-mic.org/publications/item/view/1919 MRI-guided Robotic Prostate Biopsy: A Clinical Accuracy Validation.] Int Conf Med Image Comput Comput Assist Interv. 2010;13(Pt 3):383-91. PMID: 20879423.