Projects:StructuralAndDWIPipeline

2009-10-06T07:06:57Z

Caff:

Back to [[Algorithm:Utah|Utah Algorithms]]
__NOTOC__
= A Framework for Joint Analysis of Structural and Diffusion MRIs =

{|
|[[Image:pipeline.png|thumb|200px|Joint structural and diﬀusion image analysis pipeline.]]
|}

= Description =
This framework addresses the simultaneous alignment and ﬁltering of DWI images to
correct eddy current artifacts and the subsequent alignment of those images to structural, T1 MRI to correct for susceptibility artifacts, and this
paper demonstrates the importance of performing these corrections. It also shows how a T1-based, group speciﬁc atlas can be used to generate
grey-matter regions of interest that can drive subsequent connectivity analyses. The result is a system that can be combined with a variety of
tools for MRI analysis for tissue classiﬁcation, morphometry, and cortical parcellation.

* ''Eddy Current Correction''

We implemented the diffusion weighted image (DWI) registration model from the paper of G.K.Rohde et al. Patient head motion and eddy currents distortion cause artifacts in maps of diffusion parameters computer from DWI. This model corrects these two distortions at the same time including brightness correction.

{|
|[[Image:DTIregistration.png|thumb|200px|Coronal slice from a unregisted DTI (left). The same slice after applying the registration model (right).]]
|}

* "Structural Image Preprocessing"
Preprocess structural images to remove skull, correct bias ﬁeld, normalize
intensities, and segment tissue classes (to provide a white matter mask).

* "Group Atlas"
Build a structural atlas from all sub jects’ T1 images. Seed regions for tract
endpoints are manually delineated in the structural atlas and then mapped
from the atlas to each individual. Automatically segment white matter tracts and quantify diﬀusion properties
using volumetric pathway analysis.
{|
|[[Image:seeds.png|thumb|200px|The structural atlas built from the ﬁve T1 images with manually outlined
frontal forceps seeds (left). The seeds mapped to each of the individual cases (right).]]
|}

{|
|[[Image:tracts.png|thumb|200px|Tracts]]
|}

[1] G.K.Rohde, A.S.Barnett, P.J.Basser, S.Marenco, and C.Pierpaoli, et al., "Comprehensive Approach for Correction of Motion and Distortion in Diffusion-Weighted MRI," Magnetic Resonance in Medicine 51:103-114(2004)

= Key Investigators =

* Utah: Ran Tao, Tom Fletcher, Ross Whitaker

2009-10-06T06:52:33Z

Caff:

Projects:StructuralAndDWIPipeline

2009-10-06T06:52:06Z

Caff: Created page with 'Back to Utah Algorithms __NOTOC__ = A Framework for Joint Analysis of Structural and Diffusion MRIs = {| |[[Image:pipeline.png|thumb|512px|Joint structural a…'

Back to [[Algorithm:Utah|Utah Algorithms]]
__NOTOC__
= A Framework for Joint Analysis of Structural and Diffusion MRIs =

{|
|[[Image:pipeline.png|thumb|512px|Joint structural and diﬀusion image analysis pipeline.]]
|}

= Description =
This framework addresses the simultaneous alignment and ﬁltering of DWI images to
correct eddy current artifacts and the subsequent alignment of those images to structural, T1 MRI to correct for susceptibility artifacts, and this
paper demonstrates the importance of performing these corrections. It also shows how a T1-based, group speciﬁc atlas can be used to generate
grey-matter regions of interest that can drive subsequent connectivity analyses. The result is a system that can be combined with a variety of
tools for MRI analysis for tissue classiﬁcation, morphometry, and cortical parcellation.

* ''Eddy Current Correction''

We implemented the diffusion weighted image (DWI) registration model from the paper of G.K.Rohde et al. Patient head motion and eddy currents distortion cause artifacts in maps of diffusion parameters computer from DWI. This model corrects these two distortions at the same time including brightness correction.

{|
|[[Image:DTIregistration.png|thumb|512px|Coronal slice from a unregisted DTI (left). The same slice after applying the registration model (right).]]
|}

[1] G.K.Rohde, A.S.Barnett, P.J.Basser, S.Marenco, and C.Pierpaoli, et al., "Comprehensive Approach for Correction of Motion and Distortion in Diffusion-Weighted MRI," Magnetic Resonance in Medicine 51:103-114(2004)

= Key Investigators =

* Utah: Ran Tao, Tom Fletcher, Ross Whitaker

2009-10-05T18:11:45Z

Caff: /* DTI Processing and Statistics Tools */

Projects:DTIProcessingTools

2009-10-05T18:09:57Z

Caff: /* DTI Processing and Statistics Tools */

Back to [[Algorithm:Utah|Utah Algorithms]]
__NOTOC__
= DTI Processing and Statistics Tools =
* ''Correction for Geometric Distortion in Echo Planar Images'' We have developed a variational image-based approach to correct the susceptibility artifacts in the alignment of diffusion weighted and structural MRI.The correction is formulated as an optimization of a penalty that captures the intensity difference between the jacobian corrected EPI baseline images and a corresponding T2-weighted structural image.
{|
|[[Image:EPI.png|thumb|512px|Coronal slice from a fieldmap corrected EPI (left). The same slice using proposed correction method (right).]]
|}
R Tao, PT Fletcher, S Gerber, R Whitaker, A Variational Image-Based Approach to the Correction of Susceptibility Artifacts in the Alignment of Diffusion Weighted and Structural MRI, IPMI 2009

* ''Eddy Current Correction'' We implemented the diffusion weighted image (DWI) registration model from the paper of G.K.Rohde et al. Patient head motion and eddy currents distortion cause artifacts in maps of diffusion parameters computer from DWI. This model corrects these two distortions at the same time including brightness correction.

{|
|[[Image:DTIregistration.png|thumb|512px|Coronal slice from a unregisted DTI (left). The same slice after applying the registration model (right).]]
|}

[1] G.K.Rohde, A.S.Barnett, P.J.Basser, S.Marenco, and C.Pierpaoli, et al., "Comprehensive Approach for Correction of Motion and Distortion in Diffusion-Weighted MRI," Magnetic Resonance in Medicine 51:103-114(2004)

* ''Differential Geometry'' We will provide methods for computing geodesics and distances between diffusion tensors. Several different metrics will be made available, including a simple linear metric and also a symmetric space (curved) metric. These routines are the building blocks for the routines below.

* ''Statistics'' Given a collection of diffusion tensors, compute the average and covariance statistics. This can be done using the metrics and geometry routines above. A general method for testing differences between groups is planned. The hypothesis test also depends on the underlying geometry used.

* ''Interpolation'' Interpolation routines will be implemented as a weighted averaging of diffusion tensors in the metric framework. The metric may be chosen so that the interpolation preserves desired properties of the tensors, e.g., orientation, size, etc.

* ''Filtering'' We will provide anisotropic filtering of DTI using the full tensor data (as opposed to component-wise filtering). Filtering will also be able to use the different metrics, allowing control over what properties of the tensors are preserved in the smoothing. We have also developed methods for filtering the original diffusion weighted images (DWIs) that takes the Rician distribution of MR noise into account (see MICCAI 2006 paper below).

{|
|[[Image:DTIFiltering.jpg|thumb|512px|Coronal slice from a noisy DTI (left). The same slice after applying our Rician noise DTI filtering method (right).]]
|}

= Description =

* Developed a Slicer module for our DT-MRI Rician noise removal during the [[2007_Project_Half_Week|2007 Project Half Week]]. Also enhanced the method by including an automatic method for determining the noise sigma in the image.

* Developed prototype of DTI geometry package. This includes an abstract class for computing distances and geodesics between tensors, while derived classes can specify the particular metric to use. Current implemented subclasses are the basic linear metric and the symmetric space metric.

* Developed prototype of DTI statistical package. A general class has been developed for computing averages and principal modes of variation of tensor data. The statistics class can use any of the metrics described above.

* We have begun work on a general method for hypothesis testing of differences in two diffusion tensor groups. This method works on the full six-dimensional tensor information, rather than derived measures. The hypothesis testing class can also use any of the different tensor metrics.

* Participated in the [[Engineering:Programmers_Week_Summer_2005|Programmer's Week]] (June 2005, Boston). During this week the DTI statistics code was developed and added to the NA-MIC toolkit. See our [[Progress_Report:Diffusion_Tensor_Statistics|Progress Report (July 2005)]].

= Key Investigators =

* Utah: Tom Fletcher, Ran Tao, Saurav Basu, Sylvain Gouttard, Ross Whitaker

= Publications =

''In Print''
* [http://www.na-mic.org/publications/pages/display?search=DTIProcessingTools&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database]

[[Category:Diffusion MRI]] [[Category:Statistics]] [[Category:Registration]] [[Category:Slicer]]

Projects:DTIProcessingTools

2009-10-05T18:08:19Z

Caff: /* DTI Processing and Statistics Tools */

Back to [[Algorithm:Utah|Utah Algorithms]]
__NOTOC__
= DTI Processing and Statistics Tools =
* ''Correction for Geometric Distortion in Echo Planar Images'' We have developed a variational image-based approach to correct the susceptibility artifacts in the alignment of diffusion weighted and structural MRI.The correction is formulated as an optimization of a penalty that captures the intensity difference between the jacobian corrected EPI baseline images and a corresponding T2-weighted structural image.
{|
|[[Image:EPI.png|thumb|512px|Coronal slice from a unregisted DTI (left). The same slice after applying the registration model (right).]]
|}
R Tao, PT Fletcher, S Gerber, R Whitaker, A Variational Image-Based Approach to the Correction of Susceptibility Artifacts in the Alignment of Diffusion Weighted and Structural MRI, IPMI 2009

* ''Eddy Current Correction'' We implemented the diffusion weighted image (DWI) registration model from the paper of G.K.Rohde et al. Patient head motion and eddy currents distortion cause artifacts in maps of diffusion parameters computer from DWI. This model corrects these two distortions at the same time including brightness correction.

{|
|[[Image:DTIregistration.png|thumb|512px|Coronal slice from a unregisted DTI (left). The same slice after applying the registration model (right).]]
|}

[1] G.K.Rohde, A.S.Barnett, P.J.Basser, S.Marenco, and C.Pierpaoli, et al., "Comprehensive Approach for Correction of Motion and Distortion in Diffusion-Weighted MRI," Magnetic Resonance in Medicine 51:103-114(2004)

* ''Differential Geometry'' We will provide methods for computing geodesics and distances between diffusion tensors. Several different metrics will be made available, including a simple linear metric and also a symmetric space (curved) metric. These routines are the building blocks for the routines below.

* ''Statistics'' Given a collection of diffusion tensors, compute the average and covariance statistics. This can be done using the metrics and geometry routines above. A general method for testing differences between groups is planned. The hypothesis test also depends on the underlying geometry used.

* ''Interpolation'' Interpolation routines will be implemented as a weighted averaging of diffusion tensors in the metric framework. The metric may be chosen so that the interpolation preserves desired properties of the tensors, e.g., orientation, size, etc.

* ''Filtering'' We will provide anisotropic filtering of DTI using the full tensor data (as opposed to component-wise filtering). Filtering will also be able to use the different metrics, allowing control over what properties of the tensors are preserved in the smoothing. We have also developed methods for filtering the original diffusion weighted images (DWIs) that takes the Rician distribution of MR noise into account (see MICCAI 2006 paper below).

{|
|[[Image:DTIFiltering.jpg|thumb|512px|Coronal slice from a noisy DTI (left). The same slice after applying our Rician noise DTI filtering method (right).]]
|}

= Description =

* Developed a Slicer module for our DT-MRI Rician noise removal during the [[2007_Project_Half_Week|2007 Project Half Week]]. Also enhanced the method by including an automatic method for determining the noise sigma in the image.

* Developed prototype of DTI geometry package. This includes an abstract class for computing distances and geodesics between tensors, while derived classes can specify the particular metric to use. Current implemented subclasses are the basic linear metric and the symmetric space metric.

* Developed prototype of DTI statistical package. A general class has been developed for computing averages and principal modes of variation of tensor data. The statistics class can use any of the metrics described above.

* We have begun work on a general method for hypothesis testing of differences in two diffusion tensor groups. This method works on the full six-dimensional tensor information, rather than derived measures. The hypothesis testing class can also use any of the different tensor metrics.

* Participated in the [[Engineering:Programmers_Week_Summer_2005|Programmer's Week]] (June 2005, Boston). During this week the DTI statistics code was developed and added to the NA-MIC toolkit. See our [[Progress_Report:Diffusion_Tensor_Statistics|Progress Report (July 2005)]].

= Key Investigators =

* Utah: Tom Fletcher, Ran Tao, Saurav Basu, Sylvain Gouttard, Ross Whitaker

= Publications =

''In Print''
* [http://www.na-mic.org/publications/pages/display?search=DTIProcessingTools&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database]

[[Category:Diffusion MRI]] [[Category:Statistics]] [[Category:Registration]] [[Category:Slicer]]

Algorithm:Utah

2009-10-05T18:07:35Z

Caff: /* Diffusion Tensor Image Processing Tools */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of Utah Algorithms (PI: Ross Whitaker) =

We are developing new methods in the areas of statistical shape analysis, MRI tissue segmentation, and diffusion tensor image processing and analysis. We are building shape analysis tools that can generate efficient statistical models appropriate for analyzing anatomical shape differences in the brain. We are developing a wide range of tools for diffusion tensor imaging, that span the entire pipeline from image processing to automatic white matter tract extraction to statistical testing of clinical hypotheses.

= Utah Projects =

{| cellpadding="10" style="text-align:left;"

| | [[Image:HeadRegressionResult.png|200px]]
| |

== [[Projects:ShapeRegression|Particle Based Shape Regression]] ==

Shape regression promises to be an important tool to study the relationship between anatomy and underlying clinical or biological parameters, such as age. We propose a new method to building shape models that incorporates regression analysis in the process of optimizing correspondences on a set of open surfaces. The method is applied to provide new results on clinical MRI data related to early development of the human head.

'''New: ''' M Datar, J Cates, P T Fletcher, S Gouttard, G Gerig, R Whitaker, Particle Based Shape Regression of Open Surfaces with Applications to Developmental Neuroimaging, MICCAI 2009.

|-

{| cellpadding="10" style="text-align:left;"

| | [[Image:sgerber_brainmanifold_oasis_manifold.png|200px]]
| |

== [[Projects:BrainManifold|Brain Manifold Learning]] ==

This work is concerned with modeling the space of brain images. Common approach for representing populations are template or clustering based approaches. In this project we develop a data driven method to learn a manifold representation from a set of brain images. The presented approach is described and evaluated in the setting of brain MRI but generalizes to other application domains.

'''New: ''' S Gerber, T Tasdizen, S Joshi, R Whitaker, On the Manifold Structure of the Space of Brain Images, MICCAI 2009.

'''New: ''' S Gerber, T Tasdizen, R Whitaker, Dimensionality Reduction and Principal Surfaces via Kernel Map, ICCV 2009.
|-
| style="width:15%" | [[Image:DTIregistration200.png|200px]]
| style="width:85%" |

== [[Projects:DTIProcessingTools|Diffusion Tensor Image Processing Tools]] ==

We implement the diffusion weighted image (DWI) registration model from the paper of G.K.Rohde et al. Patient head motion and eddy currents distortion cause artifacts in maps of diffusion parameters computer from DWI. This model corrects these two distortions at the same time including brightness correction.

|-
| style="width:15%" | [[Image:EPI.png|200px]]
| style="width:85%" |

== [[Projects:DTIProcessingTools| Correction for Geometric Distortion in Echo Planar Images]] ==

We have developed a variational image-based approach to correct the susceptibility artifacts in the alignment of diffusion weighted and structural MRI.The correction is formulated as an optimization of a penalty that captures the intensity difference between the jacobian corrected EPI baseline images and a corresponding T2-weighted structural image.

'''New: ''' R Tao, PT Fletcher, S Gerber, R Whitaker, A Variational Image-Based Approach to the Correction of Susceptibility Artifacts
in the Alignment of Diffusion Weighted and Structural MRI, IPMI 2009,

|-

| | [[Image:Sulcaldepth.png|200px]]
| |

== [[Projects:CorticalCorrespondenceWithParticleSystem|Cortical Correspondence using Particle System]] ==

In this project, we want to compute cortical correspondence on populations, using various features such as cortical structure, DTI connectivity, vascular structure, and functional data (fMRI). This presents a challenge because of the highly convoluted surface of the cortex, as well as because of the different properties of the data features we want to incorporate together. [[Projects:CorticalCorrespondenceWithParticleSystem|More...]]

'''New: ''' Oguz I, Niethammer M, Cates J, Whitaker R, Fletcher T, Vachet C, Styner M. “Cortical Correspondence with Probabilistic Fiber Connectivity”. Proc. Information Processing in Medical Imaging, 2009.

|-

| | [[Image:CatesNamicFigure3.png|200px]]
| |

== [[Projects:ParticlesForShapesAndComplexes|Adaptive, Particle-Based Sampling for Shapes and Complexes]] ==

This research is a new method for constructing compact statistical point-based models of ensembles of similar shapes that does not rely on any specific surface parameterization. The method requires very little preprocessing or parameter tuning, and is applicable to a wider range of problems than existing methods, including nonmanifold surfaces and objects of arbitrary topology. [[Projects:ParticlesForShapesAndComplexes|More...]]

'''New: ''' Particle-Based Shape Analysis of Multi-object Complexes. Cates J., Fletcher P.T., Styner M., Hazlett H.C., Whitaker R. Int Conf Med Image Comput Comput Assist Interv. 2008;11(Pt 1):477-485.

|-

| | [[Image:UNCShape_OverviewAnalysis_MICCAI06.gif|200px]]
| |

== [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|Shape Analysis Framework using SPHARM-PDM]] ==

The UNC shape analysis is based on an analysis framework of objects with spherical topology, described mainly by sampled spherical harmonics SPHARM-PDM. The input of the shape analysis framework is a set of binary segmentations of a single brain structure, such as the hippocampus or caudate. These segmentations are converted into a shape description (SPHARM) with correspondence and analyzed via Hotelling T^2 two sample metric. [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|More...]]

'''New: ''' Zhao Z., Taylor W., Styner M., Steffens D., Krishnan R., Macfall J. , Hippocampus shape analysis and late-life depression. PLoS ONE. 2008 Mar 19;3(3):e1837.

|-

| | [[Image:FiberTracts-angle.jpg|200px]]
| |

== [[Projects:DTIVolumetricWhiteMatterConnectivity|DTI Volumetric White Matter Connectivity]] ==

We have developed a PDE-based approach to white matter connectivity from DTI that is founded on the principal of minimal paths through the tensor volume. Our method computes a volumetric representation of a white matter tract given two endpoint regions. We have also developed statistical methods for quantifying the full tensor data along these pathways, which should be useful in clinical studies using DT-MRI. [[Projects:DTIVolumetricWhiteMatterConnectivity|More...]]

|-

| | [[Image:Brain-seg-utah.png|200px]]
| |

== [[Projects:TissueClassificationWithNeighborhoodStatistics| Tissue Classification with Neighborhood Statistics]] ==

We have implemented an MRI tissue classification algorithm based on unsupervised non-parametric density estimation of tissue intensity classes.
[[Projects:TissueClassificationWithNeighborhoodStatistics|More...]]

|}

Algorithm:Utah

2009-10-05T18:07:00Z

Caff: /* Diffusion Tensor Image Processing Tools */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of Utah Algorithms (PI: Ross Whitaker) =

We are developing new methods in the areas of statistical shape analysis, MRI tissue segmentation, and diffusion tensor image processing and analysis. We are building shape analysis tools that can generate efficient statistical models appropriate for analyzing anatomical shape differences in the brain. We are developing a wide range of tools for diffusion tensor imaging, that span the entire pipeline from image processing to automatic white matter tract extraction to statistical testing of clinical hypotheses.

= Utah Projects =

{| cellpadding="10" style="text-align:left;"

| | [[Image:HeadRegressionResult.png|200px]]
| |

== [[Projects:ShapeRegression|Particle Based Shape Regression]] ==

Shape regression promises to be an important tool to study the relationship between anatomy and underlying clinical or biological parameters, such as age. We propose a new method to building shape models that incorporates regression analysis in the process of optimizing correspondences on a set of open surfaces. The method is applied to provide new results on clinical MRI data related to early development of the human head.

'''New: ''' M Datar, J Cates, P T Fletcher, S Gouttard, G Gerig, R Whitaker, Particle Based Shape Regression of Open Surfaces with Applications to Developmental Neuroimaging, MICCAI 2009.

|-

{| cellpadding="10" style="text-align:left;"

| | [[Image:sgerber_brainmanifold_oasis_manifold.png|200px]]
| |

== [[Projects:BrainManifold|Brain Manifold Learning]] ==

This work is concerned with modeling the space of brain images. Common approach for representing populations are template or clustering based approaches. In this project we develop a data driven method to learn a manifold representation from a set of brain images. The presented approach is described and evaluated in the setting of brain MRI but generalizes to other application domains.

'''New: ''' S Gerber, T Tasdizen, S Joshi, R Whitaker, On the Manifold Structure of the Space of Brain Images, MICCAI 2009.

'''New: ''' S Gerber, T Tasdizen, R Whitaker, Dimensionality Reduction and Principal Surfaces via Kernel Map, ICCV 2009.
|-
| style="width:15%" | [[Image:DTIregistration200.png|200px]]
| style="width:85%" |

== [[Projects:DTIProcessingTools|Diffusion Tensor Image Processing Tools]] ==

We implement the diffusion weighted image (DWI) registration model from the paper of G.K.Rohde et al. Patient head motion and eddy currents distortion cause artifacts in maps of diffusion parameters computer from DWI. This model corrects these two distortions at the same time including brightness correction.

|-

| | [[Image:f_27.png|95px]] [[Image:r_27.png|95px]]
| |

|-
| style="width:15%" | [[Image:EPI.png|200px]]
| style="width:85%" |

== [[Projects:DTIProcessingTools| Correction for Geometric Distortion in Echo Planar Images]] ==

We have developed a variational image-based approach to correct the susceptibility artifacts in the alignment of diffusion weighted and structural MRI.The correction is formulated as an optimization of a penalty that captures the intensity difference between the jacobian corrected EPI baseline images and a corresponding T2-weighted structural image.

'''New: ''' R Tao, PT Fletcher, S Gerber, R Whitaker, A Variational Image-Based Approach to the Correction of Susceptibility Artifacts
in the Alignment of Diffusion Weighted and Structural MRI, IPMI 2009,

|-

| | [[Image:Sulcaldepth.png|200px]]
| |

== [[Projects:CorticalCorrespondenceWithParticleSystem|Cortical Correspondence using Particle System]] ==

In this project, we want to compute cortical correspondence on populations, using various features such as cortical structure, DTI connectivity, vascular structure, and functional data (fMRI). This presents a challenge because of the highly convoluted surface of the cortex, as well as because of the different properties of the data features we want to incorporate together. [[Projects:CorticalCorrespondenceWithParticleSystem|More...]]

'''New: ''' Oguz I, Niethammer M, Cates J, Whitaker R, Fletcher T, Vachet C, Styner M. “Cortical Correspondence with Probabilistic Fiber Connectivity”. Proc. Information Processing in Medical Imaging, 2009.

|-

| | [[Image:CatesNamicFigure3.png|200px]]
| |

== [[Projects:ParticlesForShapesAndComplexes|Adaptive, Particle-Based Sampling for Shapes and Complexes]] ==

This research is a new method for constructing compact statistical point-based models of ensembles of similar shapes that does not rely on any specific surface parameterization. The method requires very little preprocessing or parameter tuning, and is applicable to a wider range of problems than existing methods, including nonmanifold surfaces and objects of arbitrary topology. [[Projects:ParticlesForShapesAndComplexes|More...]]

'''New: ''' Particle-Based Shape Analysis of Multi-object Complexes. Cates J., Fletcher P.T., Styner M., Hazlett H.C., Whitaker R. Int Conf Med Image Comput Comput Assist Interv. 2008;11(Pt 1):477-485.

|-

| | [[Image:UNCShape_OverviewAnalysis_MICCAI06.gif|200px]]
| |

== [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|Shape Analysis Framework using SPHARM-PDM]] ==

The UNC shape analysis is based on an analysis framework of objects with spherical topology, described mainly by sampled spherical harmonics SPHARM-PDM. The input of the shape analysis framework is a set of binary segmentations of a single brain structure, such as the hippocampus or caudate. These segmentations are converted into a shape description (SPHARM) with correspondence and analyzed via Hotelling T^2 two sample metric. [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|More...]]

'''New: ''' Zhao Z., Taylor W., Styner M., Steffens D., Krishnan R., Macfall J. , Hippocampus shape analysis and late-life depression. PLoS ONE. 2008 Mar 19;3(3):e1837.

|-

| | [[Image:FiberTracts-angle.jpg|200px]]
| |

== [[Projects:DTIVolumetricWhiteMatterConnectivity|DTI Volumetric White Matter Connectivity]] ==

We have developed a PDE-based approach to white matter connectivity from DTI that is founded on the principal of minimal paths through the tensor volume. Our method computes a volumetric representation of a white matter tract given two endpoint regions. We have also developed statistical methods for quantifying the full tensor data along these pathways, which should be useful in clinical studies using DT-MRI. [[Projects:DTIVolumetricWhiteMatterConnectivity|More...]]

|-

| | [[Image:Brain-seg-utah.png|200px]]
| |

== [[Projects:TissueClassificationWithNeighborhoodStatistics| Tissue Classification with Neighborhood Statistics]] ==

We have implemented an MRI tissue classification algorithm based on unsupervised non-parametric density estimation of tissue intensity classes.
[[Projects:TissueClassificationWithNeighborhoodStatistics|More...]]

|}

2009-10-05T17:19:19Z

Caff: /* Correction for Geometric Distortion in Echo Planar Images */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of Utah Algorithms (PI: Ross Whitaker) =

We are developing new methods in the areas of statistical shape analysis, MRI tissue segmentation, and diffusion tensor image processing and analysis. We are building shape analysis tools that can generate efficient statistical models appropriate for analyzing anatomical shape differences in the brain. We are developing a wide range of tools for diffusion tensor imaging, that span the entire pipeline from image processing to automatic white matter tract extraction to statistical testing of clinical hypotheses.

= Utah Projects =

{| cellpadding="10" style="text-align:left;"

| | [[Image:HeadRegressionResult.png|200px]]
| |

== [[Projects:ShapeRegression|Particle Based Shape Regression]] ==

Shape regression promises to be an important tool to study the relationship between anatomy and underlying clinical or biological parameters, such as age. We propose a new method to building shape models that incorporates regression analysis in the process of optimizing correspondences on a set of open surfaces. The method is applied to provide new results on clinical MRI data related to early development of the human head.

'''New: ''' M Datar, J Cates, P T Fletcher, S Gouttard, G Gerig, R Whitaker, Particle Based Shape Regression of Open Surfaces with Applications to Developmental Neuroimaging, MICCAI 2009.

|-

{| cellpadding="10" style="text-align:left;"

| | [[Image:sgerber_brainmanifold_oasis_manifold.png|200px]]
| |

== [[Projects:BrainManifold|Brain Manifold Learning]] ==

This work is concerned with modeling the space of brain images. Common approach for representing populations are template or clustering based approaches. In this project we develop a data driven method to learn a manifold representation from a set of brain images. The presented approach is described and evaluated in the setting of brain MRI but generalizes to other application domains.

'''New: ''' S Gerber, T Tasdizen, S Joshi, R Whitaker, On the Manifold Structure of the Space of Brain Images, MICCAI 2009.

'''New: ''' S Gerber, T Tasdizen, R Whitaker, Dimensionality Reduction and Principal Surfaces via Kernel Map, ICCV 2009.
|-
| style="width:15%" | [[Image:DTIregistration200.png|200px]]
| style="width:85%" |

== [[Projects:DTIProcessingTools|Diffusion Tensor Image Processing Tools]] ==

We implement the diffusion weighted image (DWI) registration model from the paper of G.K.Rohde et al. Patient head motion and eddy currents distortion cause artifacts in maps of diffusion parameters computer from DWI. This model corrects these two distortions at the same time including brightness correction.

|-

| | [[Image:f_27.png|100px]] [[Image:r_27.png|100px]]
| |

== [[Projects:DTIProcessingTools| Correction for Geometric Distortion in Echo Planar Images]] ==

We have developed a variational image-based approach to correct the susceptibility artifacts in the alignment of diffusion weighted and structural MRI.The correction is formulated as an optimization of a penalty that captures the intensity difference between the jacobian corrected EPI baseline images and a corresponding T2-weighted structural image.

'''New: ''' R Tao, PT Fletcher, S Gerber, R Whitaker, A Variational Image-Based Approach to the Correction of Susceptibility Artifacts
in the Alignment of Diffusion Weighted and Structural MRI, IPMI 2009,

|-

| | [[Image:Sulcaldepth.png|200px]]
| |

== [[Projects:CorticalCorrespondenceWithParticleSystem|Cortical Correspondence using Particle System]] ==

In this project, we want to compute cortical correspondence on populations, using various features such as cortical structure, DTI connectivity, vascular structure, and functional data (fMRI). This presents a challenge because of the highly convoluted surface of the cortex, as well as because of the different properties of the data features we want to incorporate together. [[Projects:CorticalCorrespondenceWithParticleSystem|More...]]

'''New: ''' Oguz I, Niethammer M, Cates J, Whitaker R, Fletcher T, Vachet C, Styner M. “Cortical Correspondence with Probabilistic Fiber Connectivity”. Proc. Information Processing in Medical Imaging, 2009.

|-

| | [[Image:CatesNamicFigure3.png|200px]]
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== [[Projects:ParticlesForShapesAndComplexes|Adaptive, Particle-Based Sampling for Shapes and Complexes]] ==

This research is a new method for constructing compact statistical point-based models of ensembles of similar shapes that does not rely on any specific surface parameterization. The method requires very little preprocessing or parameter tuning, and is applicable to a wider range of problems than existing methods, including nonmanifold surfaces and objects of arbitrary topology. [[Projects:ParticlesForShapesAndComplexes|More...]]

'''New: ''' Particle-Based Shape Analysis of Multi-object Complexes. Cates J., Fletcher P.T., Styner M., Hazlett H.C., Whitaker R. Int Conf Med Image Comput Comput Assist Interv. 2008;11(Pt 1):477-485.

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== [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|Shape Analysis Framework using SPHARM-PDM]] ==

The UNC shape analysis is based on an analysis framework of objects with spherical topology, described mainly by sampled spherical harmonics SPHARM-PDM. The input of the shape analysis framework is a set of binary segmentations of a single brain structure, such as the hippocampus or caudate. These segmentations are converted into a shape description (SPHARM) with correspondence and analyzed via Hotelling T^2 two sample metric. [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|More...]]

'''New: ''' Zhao Z., Taylor W., Styner M., Steffens D., Krishnan R., Macfall J. , Hippocampus shape analysis and late-life depression. PLoS ONE. 2008 Mar 19;3(3):e1837.

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== [[Projects:DTIVolumetricWhiteMatterConnectivity|DTI Volumetric White Matter Connectivity]] ==

We have developed a PDE-based approach to white matter connectivity from DTI that is founded on the principal of minimal paths through the tensor volume. Our method computes a volumetric representation of a white matter tract given two endpoint regions. We have also developed statistical methods for quantifying the full tensor data along these pathways, which should be useful in clinical studies using DT-MRI. [[Projects:DTIVolumetricWhiteMatterConnectivity|More...]]

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== [[Projects:TissueClassificationWithNeighborhoodStatistics| Tissue Classification with Neighborhood Statistics]] ==

We have implemented an MRI tissue classification algorithm based on unsupervised non-parametric density estimation of tissue intensity classes.
[[Projects:TissueClassificationWithNeighborhoodStatistics|More...]]

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