NAMIC Wiki - User contributions [en]

SPIE2012 Slicer

2012-02-01T21:06:30Z

Cvachet:

Slicer4 binaries for the SPIE2012 course: [[SPIE_2012_DTI_Workshop | Go to event page]]

*Mac OSX 10.7 (Lion) - Tested Jan 31 : [http://slicer.kitware.com/midas3/item/305 Download at Slicer MIDAS]
*Windows 7 (VS 2008) : [http://slicer.kitware.com/midas3/item/310 Download at Slicer MIDAS]
*Linux 64 : [http://slicer.kitware.com/midas3/item/311 Download at Slicer MIDAS]

Tutorial list
# DICOM to NRRD conversion module [[media:2012-SPIE-DICOMToNRRDConversionTutorial.pptx‎ | PPTX ]]
# DTIPrep module [[media:2012-SPIE-DTIQC.pptx‎ | PPTX]]
# Pairwise registration to atlas via DTI-Reg [[media:2012-SPIE-DTI-Reg-Tutorial.pptx | PPTX]]
# FiberViewer Light module [[media:2012-SPIE-FiberViewerLightTutorial.pptx | PPTX]]

Timeline
*Jan 20: Martin sends 1-2 slides to Sonia for overview session
*Jan 20: Linux and Mac binaries ready
*Jan 27: windows binaries ready
*Jan 27: all draft presentations ready
*Jan 31: all tutorials tested on all platforms

2012 Winter Project Week:PairWiseDTIRegistration

2012-01-13T16:11:19Z

Cvachet:

__NOTOC__
<gallery>
Image:PW-SLC2012.png|[[2012_Winter_Project_Week#Projects|Projects List]]
Image:DTI-Reg_UI.jpg| DTI-Reg user interface
Image:DTIReg_Extension_Slicer4_CDash.jpg| DTI-Reg CDash
Image:DTI_Reg_Extension_Slicer4.jpg| DTI-Reg Slicer 4 extension
</gallery>

==DTI registration/processing pipeline in Slicer3==

==Key Investigators==
* UNC: Clement Vachet, Martin Styner
* IOWA: Mark Scully, Hans Johnson

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>
We focus on enabling the use of advanced DTI processing within NA-MIC and on automatizing steps to perform [[Projects:AtlasBasedDTIFiberAnalyzerFramework|atlas-based DTI population analysis]]. One component is pair-wise DTI registration which can be directly performed by DTI-Reg, a C++ application developed in that regard.

</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>

DTI-Reg is an open-source C++ application that performs pair-wise DTI registration.
Individual steps of the pair-wise registration pipeline are performed via external applications, called via BatchMake. Starting with two input DTI images, scalar FA maps are generated via dtiprocess. Registration is then performed between these FA maps, via BRAINSFit or BRAINSDemonWarp, which provide different registration schemes: rigid, affine, BSpline, diffeomorphic, logDemons. The final deformation is then applied to the source DTI image via resampleDTI.

</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>
*Advertised [http://www.nitrc.org/projects/dtireg/ DTI-Reg] to several collaborators (NITRC project)
**Part of the general [[Projects:AtlasBasedDTIFiberAnalyzerFramework| atlas-based DTI analysis framework]] from UNC/Utah
*Discussion with J-C with respect to Slicer4 extensions
*First attempt to build DTI-Reg built as a Slicer4 extension (cf images)
** Dependent packages would need to be extensions as well: (DTIProcess...)
**CMake files still need to be cleaned (using Slicer or individual libraries to compile the whole project)

</div>
</div>

<div style="width: 97%; float: left;">

==Delivery Mechanism==

This work will be delivered to the NA-MIC Kit as a Slicer extension

</div>

2012 Winter Project Week:PairWiseDTIRegistration

2012-01-13T15:52:07Z

Cvachet:

__NOTOC__
<gallery>
Image:PW-SLC2012.png|[[2012_Winter_Project_Week#Projects|Projects List]]
Image:DTI-Reg_UI.jpg| DTI-Reg user interface
Image:DTIReg_Extension_Slicer4_CDash.jpg| DTI-Reg CDash
Image:DTI_Reg_Extension_Slicer4.jpg| DTI-Reg Slicer 4 extension
</gallery>

==DTI registration/processing pipeline in Slicer3==

==Key Investigators==
* UNC: Clement Vachet, Martin Styner
* IOWA: Mark Scully, Hans Johnson

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>
We focus on enabling the use of advanced DTI processing within NA-MIC and on automatizing steps to perform [[Projects:AtlasBasedDTIFiberAnalyzerFramework|atlas-based DTI population analysis]]. One component is pair-wise DTI registration which can be directly performed by DTI-Reg, a C++ application developed in that regard.

</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>

DTI-Reg is an open-source C++ application that performs pair-wise DTI registration.
Individual steps of the pair-wise registration pipeline are performed via external applications, called via BatchMake. Starting with two input DTI images, scalar FA maps are generated via dtiprocess. Registration is then performed between these FA maps, via BRAINSFit or BRAINSDemonWarp, which provide different registration schemes: rigid, affine, BSpline, diffeomorphic, logDemons. The final deformation is then applied to the source DTI image via resampleDTI.

</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>
*Advertised [http://www.nitrc.org/projects/dtireg/ DTI-Reg] to several collaborators (NITRC project)
*Discussion with J-C with respect to Slicer4 extensions
*First attempt to build DTI-Reg built as a Slicer4 extension (cf images)
** Dependent packages would need to be extensions as well: (DTIProcess...)
**CMake files still need to be cleaned (using Slicer or individual libraries to compile the whole project)

</div>
</div>

<div style="width: 97%; float: left;">

==Delivery Mechanism==

This work will be delivered to the NA-MIC Kit as a Slicer extension

</div>

File:DTI Reg Extension Slicer4.jpg

2012-01-13T15:51:26Z

Cvachet:

File:DTIReg Extension Slicer4 CDash.jpg

2012-01-13T15:49:19Z

Cvachet:

File:DTI-Reg UI.jpg

2012-01-13T15:33:58Z

Cvachet: uploaded a new version of "File:DTI-Reg UI.jpg"

DTI-Reg user interface - Pairwise DTI registration module

2012 Winter Project Week:PairWiseDTIRegistration

2012-01-13T15:33:02Z

Cvachet:

__NOTOC__
<gallery>
Image:PW-SLC2012.png|[[2012_Winter_Project_Week#Projects|Projects List]]
Image:DTI-Reg_UI.jpg| DTI-Reg user interface
</gallery>

==DTI registration/processing pipeline in Slicer3==

==Key Investigators==
* UNC: Clement Vachet, Martin Styner
* IOWA: Mark Scully, Hans Johnson

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>
We focus on enabling the use of advanced DTI processing within NA-MIC and on automatizing steps to perform [[Projects:AtlasBasedDTIFiberAnalyzerFramework|atlas-based DTI population analysis]]. One component is pair-wise DTI registration which can be directly performed by DTI-Reg, a C++ application developed in that regard.

</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>

DTI-Reg is an open-source C++ application that performs pair-wise DTI registration.
Individual steps of the pair-wise registration pipeline are performed via external applications, called via BatchMake. Starting with two input DTI images, scalar FA maps are generated via dtiprocess. Registration is then performed between these FA maps, via BRAINSFit or BRAINSDemonWarp, which provide different registration schemes: rigid, affine, BSpline, diffeomorphic, logDemons. The final deformation is then applied to the source DTI image via resampleDTI.

</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>
*Advertised [http://www.nitrc.org/projects/dtireg/ DTI-Reg] to several collaborators (NITRC project)
*Discussion with J-C with respect to Slicer4 extensions
*First attempt to build DTI-Reg built as a Slicer4 extension (cf images)
** Dependent packages would need to be extensions as well: (DTIProcess...)
**CMake files still need to be cleaned (using Slicer or individual libraries to compile the whole project)

</div>
</div>

<div style="width: 97%; float: left;">

==Delivery Mechanism==

This work will be delivered to the NA-MIC Kit as a Slicer extension

</div>

2012 Winter Project Week:TBIDTIAnalysis

2012-01-13T15:23:01Z

Cvachet:

__NOTOC__
<gallery>
Image:PW-SLC2012.png|[[2012_Winter_Project_Week#Projects|Projects List]]
Image:MP_RAGE_PRECONTRAST.png‎|An example T1 weighted image from a patient with TBI
</gallery>

==Registration and analysis of white matter tract changes in TBI ==

==Key Investigators==
* UNC: Clement Vachet, Martin Styner
* Utah: Anuja Sharma, Marcel Prastawa, Guido Gerig
* Kitware: Danielle Pace, Stephen Aylward
* UCLA: Andrei Irimia, Jack van Horn

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>
In vivo neuroimaging is an increasingly relevant means for the neurological assessment of traumatic brain injury (TBI). However, standard automated image analysis methods are not sufficiently robust with respect to TBI-related changes in image contrast, brain shape, cranial fractures, white matter fiber alterations, and other signatures of head injury.
One of the objectives of the [http://www.na-mic.org/pages/DBP:TBI DBP] is to develop robust workflows for diffusion weighted imaging (e.g. DTI, HARDI) datasets from TBI patients, by using the NA-MIC Kit and Slicer to obtain reliable and robust metrics of white matter pathology and of white matter changes due to therapy and/or recovery.
</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>
*Objective: pairwise DTI registration and DTI analysis on TBI dataset
**Registration between acute baseline and follow-up (only two time points with some deformation due to recovery).
**Registration from subject to atlas (more challenging)
*Discuss with interested parties to find optimal methods handling such large deformations
*Perform feasibility test on [[DBP3:UCLA#Results|sample DTI dataset]]

</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>
*Discussion with UTAH team
*First attempt: use of DTI-Reg to register DTI images - 2 time points between acute baseline and follow-up scan
**ANTS used to drive the registration on skull-stripped FA images
*Data currently being processed

</div>
</div>

<div style="width: 97%; float: left;">

==Delivery Mechanism==

This work will be delivered to the NA-MIC Kit as a Slicer extension

</div>

Algorithm:UNC

2011-12-14T20:36:26Z

Cvachet: /* Atlas Based DTI Fiber Analysis Framework */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of UNC Algorithms (PI: Martin Styner) =

At UNC, we are interested in a range of algorithms and solutions for the surface based analysis of brain structures and the cortex. We pioneered the use of spherical harmonics based shape analysis for comparing brain structures across objects. We has also worked on incorporating various data sources for correspondence computation on surfaces of different complexity (ranging from simple brain structures to the highly folded cortical surface). A current topic includes the use of diffusion tensor imaging for connectivity analysis in pathological settings. Finally, investigating quality control, validation and evaluation methodology is another important topic of our NA-MIC research.

= UNC Projects =

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

| style="width:15%" |[[Image:UNC_DTIAnalysisFramework.jpg|200px]]
| style="width:85%" |

== [[Projects:AtlasBasedDTIFiberAnalyzerFramework| Atlas Based DTI Fiber Analysis Framework]] ==
This project aims to define an automatic framework for statistical comparison of fiber bundle diffusion properties between populations of diffusion weighted images.

Quality control is performed on diffusion weighted images and DTI images are computed for each individual subject. The data is then either mapped into a prior atlas or an unbiased diffeomorphic DTI atlas is generated from all datasets. This creates a normalized coordinate system for all diffusion images in a study. Fiber tracts of interest are generated on this atlas, and then mapped back to the individual subjects. Diffusion properties along fiber tracts, such as fractional anisotropy (FA), are modeled as multivariate functions of arc length and gathered in spreadsheets for statistical analysis.
[[Projects:AtlasBasedDTIFiberAnalyzerFramework|More...]]

<font color="red">'''New: '''</font> First versions on NITRC for [http://www.nitrc.org/projects/dti_tract_stat/ DTIAtlasFiberAnalyzer] and [http://www.nitrc.org/projects/fvlight/ FiberViewerLight ]

|-

| style="width:15%" |[[Image:UNC_longitudinalAtlasEx1.png|200px]]
| style="width:85%" |

== [[Projects:LongitudinalAtlasBuilding| Longitudinal Atlas Building]] ==
As part of the longitudinal intra- and interpatient analysis theme within NA-MIC, we are working on a deformable, longitudinal DTI atlas method. Our longitudinal framework explicitly accounts for temporal dependencies via iterative subject-specific statistical growth modeling, and cross-sectional atlas-building. To effectively account for measurements sparse in time, a continuous-discrete statistical growth model is proposed incorporating also patient co-variates[[Projects:LongitudinalAtlasBuilding|More...]]

<font color="red">'''New: '''</font> Gabe Hart, Yundi Shi , Hongtu Zhu, Mar Sanchez, Martin Styner, Marc Niethammer. DTI Longitudinal Atlas Construction as an Average of Growth Models. Workshop on Spatio-Temporal Image Analysis for Longitudinal and Time-Series Image Data, MICCAI 2010 Aug.;

|-

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

== [[Projects:DWIAtlas|Diffusion Weighted Atlas Construction via model-based transformation and averaging of signal]] ==
This project investigated a method for model-based averaging of sets of diffusion weighted magnetic resonance images (DW-MRI) under space transformations (resulting for example from registration methods). A robust weighted least squares method is developed. Synthetic validation experiments show the improvement of the proposed estimation method in comparison to standard least squares estimation. The developed method is applied to construct an atlas of {\it diffusion weighted images} for a set of macaques, allowing for a more flexible representation of average diffusion information compared to standard diffusion tensor atlases.
[[Projects:DWIAtlas|More...]]

<font color="red">'''New: '''</font> Y. Shi, S. Benzaid, M. Sanchez, M. Styner, M. Niethammer. Diffusion Weighted Atlas Construction via robust model-based transformation. NeuroImage, in preparation.

<font color="red">'''New: '''</font> M. Niethammer, Y. Shi, S. Benzaid, M. Sanchez, and M. Styner. Robust model-based transformation and averaging of diffusion weighted images applied to diffusion weighted atlas construction. MICCAI, Workshop on Computational Diffusion MRI, 2010.

|-

| style="width:15%" |[[Image:UNC_GraphbasedConnectivity_Ex1.png‎|200px]]
| style="width:85%" |

== [[Projects:DiffusionGraphBasedConnectivity|Diffusion Imaging based Connectivity]] ==

This project focuses on connectivity measurements derived from diffusion imaging datasets in order to better understand cortical and subcortical white matter connectivity. Our research employs a novel, multi-directional graph propagation method that performs a fully deterministic, efficient and stable connectivity computation. The method handles crossing fibers and deals well with multiple seed regions. In addition to the analysis of these connectivity measures in describing brain pathology, they can also be used as scalar maps for use in DTI registration.
[[Projects:DiffusionGraphBasedConnectivity|More...]]

<font color="red">'''New: '''</font> Alexis Boucharin, Ipek Oguz, Clement Vachet, Yundi Shi, Mar Sanchez, Martin Styner. Efficient, graph-based white matter connectivity from orientation distribution functions via multi-directional graph propagation. Medical Imaging 2011: Image Processing (2011) vol. 7962 (1) pp. 79620S

|-

| style="width:15%" |[[Image:DTIPrep_example1.png|200px]]
| style="width:85%" |

== [[Projects:DTI_DWI_QualityControl|DWI and DTI Quality Control]] ==

DWI data suffers from inherent low SNR, overall long scanning time of multiple directional encoding with correspondingly large risk to encounter several kinds of artifacts. These artifacts can be too severe for a correct and stable estimation of the diffusion tensor. Thus, a quality control (QC) procedure is absolutely necessary for DTI studies. We are developing a framework for automatic DWI and DTI quality assessment and correction. We developed a tool called DTIPrep which pipelines the QC steps with designated protocol use and report generation. [[Projects:DTI_DWI_QualityControl|More...]]

<font color="red">'''New: '''</font> DTIPrep first full version on [http://www.nitrc.org/projects/dtiprep/ NITRC ]

<font color="red">'''New: '''</font> Zhexing Liu, Casey Goodlett, Guido Gerig, Martin Styner. Evaluation of DTI property maps as basis of DTI atlas building. Medical Imaging 2010: Image Processing (2010) vol. 7623 (1) pp. 762325

<font color="red">'''New: '''</font> Zhexing Liu, Yi Wang, Guido Gerig, Sylvain Gouttard, Ran Tao, Thomas Fletcher, Martin Styner. Quality control of diffusion weighted images. Medical Imaging 2010: Advanced PACS-based Imaging Informatics and Therapeutic Applications (2010) vol. 7628 (1) pp. 76280J

|-

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

== [[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. This correspondence method has been included in our NAMIC cortical thickness framework GAMBIT [[Projects:CorticalCorrespondenceWithParticleSystem|More...]]

<font color="red">'''New: '''</font> Vachet, C., Hazlett, H., Niethammer, M., Oguz, I., Cates, J., Whitaker, R., Piven, J., Styner, M., “Group-wise automatic mesh-based analysis of cortical thickness“. Medical Imaging 2011: Image Processing (2011) vol. 7962 (1) pp. 796227 1 - 10

<font color="red">'''New: '''</font> Lee J, Ehlers C, Crews F, Niethammer M, Budin F, Paniagua B, Sulik K, Johns J, Styner M, Oguz I. Automatic cortical thickness analysis on rodent brain. Medical Imaging 2011: Image Processing (2011) vol. 7962 (1) pp. 796248

|-

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

== [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|UNC-Utah Shape Analysis Framework]] ==

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-PDM) with correspondence and tested via statistical point-wise analysis. Additionally, the SPHARM correspondences can be improved with Entropy-based particle systems, by using an integration module recently added to the pipeline. [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|More...]]

* SPHARM-Particle Shape Analysis Toolkit disseminated on [http://www.nitrc.org/projects/spharm-pdm NITRC SPHARM PDM page]. All tools are Slicer compatible.
* Single Slicer 3 module for whole shape analysis pipeline with automatic generation of Slicer MRML scenes for result visualization

<font color="red">'''New: '''</font> Mark Walterfang, Jeffrey Chee Leong Looi, Martin Styner, Ruth H Walker, Adrian Danek, Marc Neithammer, Andrew Evans, Katya Kotschet, Guilherme R Rodrigues, Andrew Hughes, Dennis Velakoulis. Shape alterations in the striatum in chorea-acanthocytosis. Psychiatry research (2011) vol. 192 (1), pp. 29-36

<font color="red">'''New: '''</font> Beatriz Paniagua, Lucia Cevidanes, David Walker, Hongtu Zhu, Ruixin Guo, Martin Styner. Clinical application of SPHARM-PDM to quantify temporomandibular joint osteoarthritis. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society (2011) vol. 35(5), pp. 345-352

<font color="red">'''New: '''</font> Beatriz Paniagua, Lucia Cevidanes, Hongtu Zhu, Martin Styner. Outcome quantification using SPHARM-PDM toolbox in orthognathic surgery. International journal of computer assisted radiology and surgery (2011) vol. 6 (5) pp. 617-626

<font color="red">'''New: '''</font> Jeffrey Chee Leong Looi, Mark Walterfang, Martin Styner, Leif Svensson, Olof Lindberg, Per Ostberg, Lisa Botes, Eva Orndahl, Phyllis Chua, Rajeev Kumar, Dennis Velakoulis, Lars-Olof Wahlund. Shape analysis of the neostriatum in frontotemporal lobar degeneration, Alzheimer's disease, and controls. Neuroimage (2010) vol. 51 (3) pp. 970-86

<font color="red">'''New: '''</font> Maltbie E, Bhatt K, Paniagua B, Smith RG, Graves MM, Mosconi MW, Peterson S, White S, Blocher J, El-Sayed M, Hazlett HC, Styner M. Asymmetric bias in user guided segmentations of brain structures. NeuroImage 2011 Aug. [Epub ahead of print]

<font color="red">'''New: '''</font> Datar M, Gur Y, Paniagua B, Styner M, Whitaker R. Geometric Correspondence for Ensembles of. MICCAI 2011, Part II 2011 Aug.;6892:368–375.

<font color="red">'''New: '''</font> Looi JCL, Macfarlane MD, Walterfang M, Styner M, Velakoulis D, Lätt J, van Westen D, Nilsson C. Morphometric analysis of subcortical structures in progressive supranuclear palsy: In vivo evidence of neostriatal and mesencephalic atrophy. Psychiatry Research: Neuroimaging 2011 Sep.;:1–13

|-

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

== [[Projects:LocalStatisticalAnalysisViaPermutationTests|Local Statistical Analysis via Permutation Tests]] ==

We have further developed a set of statistical testing methods that allow the analysis of local shape differences via group differences tests as well interaction tests. Resulting significance maps (both raw and corrected for multiple comparisons) are easily visualized. Additional visualization of the group tests are provided via mean difference magnitude and vector maps, as well as maps of the group covariance information. Additional visualization of the interaction tests include Pearson and Spearman correlation maps. [[Projects:LocalStatisticalAnalysisViaPermutationTests|More...]]

<font color="red">'''New: '''</font> User-friendly GUI interface and statistical result visualization via automatically generated Slicer MRML scenes

<font color="red">'''New: '''</font> Available on NITRC either [http://www.nitrc.org/projects/shape_mancova separately (ShapeAnalysisMANCOVA)] or as part of the [http://www.nitrc.org/projects/spharm-pdm SPHARM-PDM shape analysis package]

* Paniagua B., Styner M., Macenko M., Pantazis D., Niethammer M, Local Shape Analysis using MANCOVA, Insight Journal, 2009 July-December, http://hdl.handle.net/10380/3124

|-
| | [[Image:Cause07Competition.gif|200px]]
| |

== [[Projects:MethodEvaluationValidation|Evaluation and Comparison of Medical Image Analysis Methods]] ==

In this project, we want to focus on the evaluation of medical image analysis methods for specific clinical applications in respect to development of evaluation methodology and the organization of venues promoting such comparison and validation studies.

<font color="red">'''New: '''</font> [[Events:_DTI_Tractography_Challenge_MICCAI_2011 | DTI fiber tractography challenge]] at MICCAI 2011

|-

| | [[Image:UNCShape_CaudatePval_MICCAI06.png|200px]]
| |

== [[Projects:PopulationBasedCorrespondence|Population Based Correspondence]] ==

We are developing methodology to automatically find dense point correspondences between a collection of polygonal genus 0 meshes. The advantage of this method is independence from indivisual templates, as well as enhanced modeling properties. The method is based on minimizing a cost function that describes the goodness of correspondence. Apart from a cost function derived from the description length of the model, we also employ a cost function working with arbitrary local features. We extended the original methods to use surface curvature measurements, which are independent to differences of object aligment. [[Projects:PopulationBasedCorrespondence|More...]]

* Styner M., Oguz I., Heimann T., Gerig G. Minimum description length with local geometry. Proceedings of the 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro 2008; 1283-1286
* Software available as part of UNC Neurolib open source ([http://www.ia.unc.edu/dev website])

|-

|}

2012 Winter Project Week:TBIDTIAnalysis

2011-12-07T15:18:27Z

Cvachet:

__NOTOC__
<gallery>
Image:PW-SLC2012.png|[[2012_Winter_Project_Week#Projects|Projects List]]
Image:MP_RAGE_PRECONTRAST.png‎|An example T1 weighted image from a patient with TBI
</gallery>

==Registration and analysis of white matter tract changes in TBI ==

==Key Investigators==
* UNC: Clement Vachet, Martin Styner
* Utah: Anuja Sharma, Marcel Prastawa, Guido Gerig
* Kitware: Danielle Pace, Stephen Aylward
* UCLA: Andrei Irimia, Jack van Horn

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>
In vivo neuroimaging is an increasingly relevant means for the neurological assessment of traumatic brain injury (TBI). However, standard automated image analysis methods are not sufficiently robust with respect to TBI-related changes in image contrast, brain shape, cranial fractures, white matter fiber alterations, and other signatures of head injury.
One of the objectives of the [http://www.na-mic.org/pages/DBP:TBI DBP] is to develop robust workflows for diffusion weighted imaging (e.g. DTI, HARDI) datasets from TBI patients, by using the NA-MIC Kit and Slicer to obtain reliable and robust metrics of white matter pathology and of white matter changes due to therapy and/or recovery.
</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>
*Objective: pairwise DTI registration and DTI analysis on TBI dataset
**Registration between acute baseline and follow-up (only two time points with some deformation due to recovery).
**Registration from subject to atlas (more challenging)
*Discuss with interested parties to find optimal methods handling such large deformations
*Perform feasibility test on [[DBP3:UCLA#Results|sample DTI dataset]]

</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>

</div>
</div>

<div style="width: 97%; float: left;">

==Delivery Mechanism==

This work will be delivered to the NA-MIC Kit as a Slicer extension

</div>

2012 Winter Project Week:PairWiseDTIRegistration

2011-12-06T22:09:26Z

Cvachet:

__NOTOC__
<gallery>
Image:PW-SLC2012.png|[[2012_Winter_Project_Week#Projects|Projects List]]
Image:DTI-Reg_UI.jpg| DTI-Reg user interface
</gallery>

==DTI registration/processing pipeline in Slicer3==

==Key Investigators==
* UNC: Clement Vachet, Martin Styner
* IOWA: Mark Scully, Hans Johnson

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>
We focus on enabling the use of advanced DTI processing within NA-MIC and on automatizing steps to perform [[Projects:AtlasBasedDTIFiberAnalyzerFramework|atlas-based DTI population analysis]]. One component is pair-wise DTI registration which can be directly performed by DTI-Reg, a C++ application developed in that regard.

</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>

DTI-Reg is an open-source C++ application that performs pair-wise DTI registration.
Individual steps of the pair-wise registration pipeline are performed via external applications, called via BatchMake. Starting with two input DTI images, scalar FA maps are generated via dtiprocess. Registration is then performed between these FA maps, via BRAINSFit or BRAINSDemonWarp, which provide different registration schemes: rigid, affine, BSpline, diffeomorphic, logDemons. The final deformation is then applied to the source DTI image via resampleDTI.

</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>

</div>
</div>

<div style="width: 97%; float: left;">

==Delivery Mechanism==

This work will be delivered to the NA-MIC Kit as a Slicer extension

</div>

2012 Winter Project Week:TBIDTIAnalysis

2011-12-06T22:06:15Z

Cvachet:

__NOTOC__
<gallery>
Image:PW-SLC2012.png|[[2012_Winter_Project_Week#Projects|Projects List]]
Image:MP_RAGE_PRECONTRAST.png‎|An example T1 weighted image from a patient with TBI
</gallery>

==Registration and analysis of white matter tract changes in TBI ==

==Key Investigators==
* UNC: Clement Vachet, Martin Styner
* Utah: Anuja Sharma, Marcel Prastawa, Guido Gerig
* Kitware: Danielle Pace, Stephen Aylward
* UCLA: Andrei Irimia, Jack van Horn

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>
In vivo neuroimaging is an increasingly relevant means for the neurological assessment of traumatic brain injury (TBI). However, standard automated image analysis methods are not sufficiently robust with respect to TBI-related changes in image contrast, brain shape, cranial fractures, white matter fiber alterations, and other signatures of head injury.
One of the objectives of the [http://www.na-mic.org/pages/DBP:TBI DBP] is to develop robust workflows for diffusion weighted imaging (e.g. DTI, HARDI) datasets from TBI patients, by using the NA-MIC Kit and Slicer to obtain reliable and robust metrics of white matter pathology and of white matter changes due to therapy and/or recovery.
</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>
*Feasibility test on shared dataset with respect to registration:
**Registration between acute and chronic data, i.e. only two time points but of course some deformation due to recovery.
**Registration to an atlas (more challenging)
*Discuss with interested parties to find optimal methods to handle large deformations
**Combine DTI with sMRI data
</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>

</div>
</div>

<div style="width: 97%; float: left;">

==Delivery Mechanism==

This work will be delivered to the NA-MIC Kit as a Slicer extension

</div>

2012 Winter Project Week:TBIDTIAnalysis

2011-12-06T22:01:08Z

Cvachet: Created page with '__NOTOC__ <gallery> Image:PW-SLC2012.png|Projects List Image:MP_RAGE_PRECONTRAST.png‎|An example T1 weighted image from a patient with TBI…'

__NOTOC__
<gallery>
Image:PW-SLC2012.png|[[2012_Winter_Project_Week#Projects|Projects List]]
Image:MP_RAGE_PRECONTRAST.png‎|An example T1 weighted image from a patient with TBI
</gallery>

==Registration and analysis of white matter tract changes in TBI ==

==Key Investigators==
* UNC: Clement Vachet, Martin Styner
* Utah: Anuja Sharma, Marcel Prastawa, Guido Gerig
* Kitware: Danielle Pace, Stephen Aylward
* UCLA: Andrei Irimia, Jack van Horn

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>
In vivo neuroimaging is an increasingly relevant means for the neurological assessment of traumatic brain injury (TBI). However, standard automated image analysis methods are not sufficiently robust with respect to TBI-related changes in image contrast, brain shape, cranial fractures, white matter fiber alterations, and other signatures of head injury.
One of the objectives of the DBP is to develop robust workflows for diffusion weighted imaging (e.g. DTI, HARDI) datasets from TBI patients, by using the NA-MIC Kit and Slicer to obtain reliable and robust metrics of white matter pathology and of white matter changes due to therapy and/or recovery.
</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>
*Feasibility test on shared dataset with respect to registration:
**Registration between acute and chronic data, i.e. only two time points but of course some deformation due to recovery.
**Registration to an atlas (more challenging)
*Discuss with interested parties to find optimal methods to handle large deformations
**Combine DTI with sMRI data
</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>

</div>
</div>

<div style="width: 97%; float: left;">

==Delivery Mechanism==

This work will be delivered to the NA-MIC Kit as a Slicer extension

</div>

2012 Winter Project Week

2011-12-06T21:36:23Z

Cvachet: /* Traumatic Brain Injury DBP */

Back to [[Project Events]], [[Events]]
Back to [[Project Events]], [[AHM_2012]], [[Events]]

__NOTOC__
[[image:PW-SLC2012.png|300px]]

== Dates.Venue.Registration ==

Please [[AHM_2012#Dates_Venue_Registration|click here for Dates, Venue, and Registration]] for this event.

== Agenda==

Please [[AHM_2012#Agenda|click here for the agenda for AHM 2012 and Project Week]].

==Background==

From January 9-13, 2012, the 14th project week for hands-on research and development activity in Neuroscience and Image-Guided Therapy applications will be hosted in Salt Lake City, Utah. Participant engage in open source programming using the [[NA-MIC-Kit|NA-MIC Kit]], algorithms, medical imaging sequence development, tracking experiments, and clinical applications. The main goal of this event is to further the translational research deliverables of the sponsoring centers ([http://www.na-mic.org NA-MIC], [http://www.ncigt.org NCIGT], [http://nac.spl.harvard.edu NAC], [http://catalyst.harvard.edu/home.html Harvard Catalyst], and [http://www.cimit.org CIMIT]) and their collaborators by identifying and solving programming problems during planned and ad hoc break-out sessions.

Active preparation for this conference begins with a kick-off teleconference. Invitations to this call are sent to members of the sponsoring communities, their collaborators, past attendees of the event, as well as any parties expressing an interest in working with these centers. The main goal of the initial teleconference is to gather information about which groups/projects would be active at the upcoming event to ensure that there were sufficient resources available to meet everyone's needs. Focused discussions about individual projects are conducted during several subsequent teleconferences and permits the hosts to finalize the project teams, consolidate any common components, and identify topics that should be discussed in break-out sessions. In the final days leading up to the meeting, all project teams are asked to complete a template page on the wiki describing the objectives and research plan for each project.

On the first day of the conference, each project team leader delivers a short presentation to introduce their topic and individual members of their team. These brief presentations serve to both familiarize other teams doing similar work about common problems or practical solutions, and to identify potential subsets of individuals who might benefit from collaborative work. For the remainder of the conference, about 50% time is devoted to break-out discussions on topics of common interest to particular subsets and 50% to hands-on project work. For hands-on project work, attendees are organized into 30-50 small teams comprised of 2-4 individuals with a mix of multi-disciplinary expertise. To facilitate this work, a large room is setup with ample work tables, internet connection, and power access. This enables each computer software development-based team to gather on a table with their individual laptops, connect to the internet, download their software and data, and work on specific projects. On the final day of the event, each project team summarizes their accomplishments in a closing presentation.

A summary of all past NA-MIC Project Events is available [[Project_Events#Past|here]].

==Projects==

===IGT===
*MR guided laser ablation for neurosurgery (Dan Orringer, MD BWH, Jason Stafford, MD Anderson, Isaiah Norton BWH)
*Pelvic Registration (Sandy Wells, Firdaus Janoos, Mehdi Moradi UBC/BWH, jan egger, andrey fedorov)
*OpenIGTLink interface for Slicer4(Junichi, Clif Burdette/Jack Blevins, Tamas Ungi, Andras Lasso)
*Needle tracking (atushi yamada, radhika tibrewal, a needle navigation person)
*?mr susceptability (clare poynton, mr physics person?)
* [[2012_Winter_Project_Week:LiveUltrasound|Live ultrasound in Slicer4 using Plus and OpenIGTLink]] (Tamas Ungi, Elvis Chen)
* 4D Ultrasound (Laurent, Noby)

===Traumatic Brain Injury DBP===

* [[2012_Winter_Project_Week:TBIClinicalAnalysis|Segmentation of Serial MRI of TBI patients
using Personalized Atlas Construction]] (Bo Wang, Marcel Prastawa, Andrei Irimia, Micah Chambers, Jack van Horn, Guido Gerig, Danielle Pace, Stephen Aylward)
* [[2012_Winter_Project_Week:TBIDTIAnalysis|Registration and analysis of white matter tract changes in TBI]] (Clement Vachet, Anuja Sharma, Marcel Prastawa, Andrei Irimia, Jack van Horn, Guido Gerig, Martin Styner, Danielle Pace, Stephen Aylward)
* [[2012_Winter_Project_Week:TBIValidation|Validation, visualization and analysis of segmentation for TBI]] (Bo Wang, Marcel Prastawa, Andrei Irimia, Micah Chambers, Jack van Horn, Guido Gerig, Danielle Pace, Stephen Aylward)
*Geometric Metamorphosis for TBI (Danielle Pace, Marc Niethammer, Marcel Prastawa, Andrei Irimia, Jack van Horn, Danielle Pace, Stephen Aylward)
* [[2012_Winter_Project_Week:TBIRegistration|Multimodal Deformable Registration of Traumatic Brain Injury MR Volumes using Graphics Processing Units]] (Yifei Lou, Andrei Irimia, Patricio Vela, Allen Tannenbaum, Micah C. Chambers, Jack Van Horn and Paul M. Vespa, Danielle Pace, Stephen Aylward)
* [[2012_Winter_Project_Week:TBIRegistration|Integration of unscented Kalman filter (UKF) based multi-tensor tractography in Slicer]] (Christian Baumgartner, Yogesh Rathi)

===Predict Huntington's Disease DBP===
* [[2012_Winter_Project_Week:DWIPhantom|DTI tractography phantom: a software for evaluating tractography algorithms]] (Gwendoline Roger,Yundi Shi, Clement Vachet, Martin Styner, Sylvain Gouttard)
* [[2012_Winter_Project_Week:FVLight|FiberViewerLight: a fiber bundle visualization and clustering tool]] (Jean-Baptiste Berger, Clement Vachet, Martin Styner)
* [[2012_Winter_Project_Week:DTIAFA|DTIAtlasFiberAnalyzer]] (Jean-Baptiste Berger, Yundi Shi, Clement Vachet, Martin Styner)
* [[2012_Winter_Project_Week:PairWiseDTIRegistration|Pairwise DTI registration: DTI-Reg]] (Clement Vachet, Hans Johnson, Martin Styner)
* [[2012_Winter_Project_Week:ShapeAnalysisSubcorticalStructuresHD|Morphometric analysis in subcortical structures in HD]] (Beatriz Paniagua, Clement Vachet, Hans Johnson, Martin Styner)
* [[2012_Winter_Project_Week:DTI pipeline|Applying our DTI pipeline to analyse HD data]] (Gopalkrishna Veni, Hans Johnson, Martin Styner, Ross Whitaker)
* [[2012_Winter_Project_Week: DTI Change Modeling | Longitudinal change modeling of fiber tracts in serial HD DTI data]] (Anuja Sharma, Hans Johnson, Guido Gerig)
* [[2012_Winter_Project_Week: Continuous 4D shapes | Continuous 4d shape models from time-discrete data: Subcortical structures in HD]] (James Fishbaugh, Hans Johnson, Guido Gerig)

===Atrial fibrillation DBP===
* [[2012_Winter_Project_Week:EndoSeg|Endocardial Segmentation in DE-MRI for AFib]] (Yi Gao, Liang-Jia Zhu, Josh Cates, Greg Gardner, Alan Morris, Danny Perry, Rob MacLeod, Sylvain Bouix, Allen Tannenbaum)
* [[2012_Winter_Project_Week:LAWallRegistration|Longitudinal Alignment and Visualization of Left-Atrial Wall from DEMRI and MRA]] (Josh Cates, Yi Gao, Liang-Jia Zhu, Greg Gardner, Alan Morris, Danny Perry, Rob MacLeod, Sylvain Bouix, Allen Tannenbaum)
* [[2012_Winter_Project_Week:PVRegistration|Longitudinal Alignment and Visualization of Pulmonary Veins from DEMRI and MRA]] (Josh Cates, Yi Gao, Liang-Jia Zhu, Greg Gardner, Alan Morris, Danny Perry, Rob MacLeod, Sylvain Bouix, Allen Tannenbaum)
* [[2012_Winter_Project_Week:RealTime|OpenIGT for realtime MRI-guided RF ablation]] (Gene Payne, Rob MacLeod, and Junichi Tokuda)

===Head and Neck Cancer DBP===
* A patch-based approach to the segmentation of organs of risk (Christian Wachinger, Polina Golland)

===Radiation therapy===
* [[2012_Winter_Project_Week:RTTools|RT tools for Slicer4]] (Csaba Pinter, Kevin Wang, Andras Lasso, Greg Sharp)

===Musculoskeletal System===
* [[2012_Winter_Project_Week:Radnostics|Spine Segmentation & Osteoporosis Screening In CT Imaging Studies]] (Anthony Blumfield)

===Registration===
* [[2012_Winter_Project_Week:CMFreg|Framework for Cranio-Maxillo Facial registration in Slicer3]] (Beatriz Paniagua, Lucia Cevidanes, Martin Styner)
* [[2012_Winter_Project_Week:SlidingOrgans|Registration in the presence of sliding between organs (Danielle Pace, Marc Neithammer, Stephen Aylward)]]
* [[2012_Winter_Project_Week:GeometricMetamorphosis|Estimating the infiltration / recession of pathologies independent of background deformations (Danielle Pace, Stephen Aylward, Marc Niethammer)]]

===Shape Analysis===
* [[2012_Winter_Project_Week:PNSnormals|Principal Nested Spheres Normal Consistency in ShapeWorks]] (Beatriz Paniagua, Josh Cates, Manasi Datar, Ross Whitaker, Martin Styner)

===NA-MIC Kit Internals===
*Slicer4 release (Jean-Christophe Fillion-Robin (JC), and Julien Finet (J2))
*Slicer4 extensions (JC)
*Slicer4 documentation (JC)
*Slicer4 GUI Testing (Benjamin Long, J2, JC)
*Slicer4 data on MIDAS (Josh Cates, Patrick Reynolds)
*Slicer4 extension: Slicer4 Scene Views Module (Nicole Aucoin)
*Slicer4 Annotations Module
** File format refactor (Nicole Aucoin)
** QT 3D Text rendering proof of concept (Julien Finet, Steve Pieper, Nicole Aucoin)
*[[2012_Project_Week:DICOM|DICOM Networking, Database, and Slicer Integration]] (Steve)
*[[2012_Project_Week:EditorExtensions|Editor Extension Examples and Debugging]] (Steve, Andrey, Jc, Hans, Satra)
*[[2012_Project_Week:ViewerControls|Redesign of the slice viewer control panels]] (Julien Finet, Ron Kikinis, Hans Johnson, Greg Sharp)
* Automated Testing (Sonia Pujol, Steve Pieper, Jc, Benjamin)
* Remove legacy code from slicer4 (itk, modules, build scripts) (Hans, Jim, Steve, J2, JC)
*[[2012_Project_Week:BatchProcessing|Batch Processing with Slicer Modules]] (Steve, Andrey, JC, Hans, Satra)
*[[2012_Project_Week:4DImageSlicer4|Support for 4D Images in Slicer4]] (Andrey, Steve, Junichi, Alex)

=== Preparation ===

#Please make sure that you are on the [http://public.kitware.com/cgi-bin/mailman/listinfo/na-mic-project-week na-mic-project-week mailing list]
#Starting Thursday, October 27th, part of the weekly Thursday 3pm NA-MIC Engineering TCON will be used to prepare for this meeting. The schedule for these preparatory calls is as follows:
#*October 27: MGH DBP
#*November 3: Iowa DBP Huntingtons, Engineering Infrastructure Topics
#*November 10: Utah Atrial Fibrillation DBP
#*November 17: UCLA TBI DBP
#*November 24: No call. thanksgiving.
#*December 1:
#*December 8:
#*December 15:Finalize Projects
#*January 5: Loose Ends
#By December 15: [[Project_Week/Template|Complete a templated wiki page for your project]]. Please do not edit the template page itself, but create a new page for your project and cut-and-paste the text from this template page. If you have questions, please send an email to tkapur at bwh.harvard.edu.
#By December 15: Create a directory for each project on the [[Engineering:SandBox|NAMIC Sandbox]] (Zack)
##[https://www.kitware.com/Admin/SendPassword.cgi Ask Zack for a Sandbox account]
##Commit on each sandbox directory the code examples/snippets that represent our first guesses of appropriate methods. (Luis and Steve will help with this, as needed)
##Gather test images in any of the Data sharing resources we have (e.g. MIDAS, xNAT). These ones don't have to be many. At least three different cases, so we can get an idea of the modality-specific characteristics of these images. Put the IDs of these data sets on the wiki page. (the participants must do this.)
##Setup nightly tests on a separate Dashboard, where we will run the methods that we are experimenting with. The test should post result images and computation time. (Zack)
#Please note that by the time we get to the project event, we should be trying to close off a project milestone rather than starting to work on one...

Projects:AtlasBasedDTIFiberAnalyzerFramework

2011-12-02T19:01:11Z

Cvachet:

Back to [[Algorithm:UNC|UNC Algorithms]]
__NOTOC__

[[Image:UNC_DTIAnalysisFramework.jpg|thumb|right|400px|Atlas-based DTI fiber analysis framework]]

= Atlas Based DTI Fiber Analysis Framework =

This project aims to define an automatic framework for statistical comparison of fiber bundle diffusion properties between populations of diffusion weighted images.

Quality control is performed on diffusion weighted images and DTI images are computed for each individual subject. An unbiased diffeomorphic DTI atlas is then generated to compute a normalized coordinate system for populations of diffusion images. Fiber tracts of interest are generated on this atlas, and then mapped to the individual subjects. Diffusion properties along fiber tracts, such as fractional anisotropy (FA), are modeled as multivariate functions of arc length and gathered in spreadsheets for statistical analysis.

= Description =

The general framework entails the following steps:

==='''DWI and DTI quality control: '''===
DWI data suffers from inherent low SNR, overall long scanning time of multiple directional encoding with correspondingly large risk to encounter several kinds of artifacts. These artifacts can be too severe for a correct and stable estimation of the diffusion tensor. Thus, a quality control (QC) procedure is absolutely necessary for DTI studies.
We developed a tool called DTIPrep which pipelines the QC steps with designated protocol use and report generation, and which generates DTI images and related scalar maps. [[Projects:DTI_DWI_QualityControl|More...]]

==='''DTI preprocessing- skull-stripping: '''===
Skull-stripping is performed on DTI images and scalar maps. Several methods can be used in that regard:
* Direct Otsu Thresholding
* Masking using tissue label map generated by an intermediate atlas-based tissue segmentation, performed either on the idWI & B0 images or on the structural images (T1w & T2w).

[[Image:DTI_Atlas_Population_FiberTracts.png|thumb|right|300px|DTI atlas generation with fiber tracts]]

==='''Unbiased DTI atlas building or atlas mapping: '''===
Unbiased diffeomorphic atlas building is used to compute a normalized coordinate system for populations of diffusion images. The diffeomorphic transformations between each subject and the atlas provide spatial normalization for the comparison of tract statistics.
* '''DTI atlas creation:''' A DTI atlas can be generated for a specific study by averaging all individual subjects. For longitudinal studies, a [[Projects:LongitudinalAtlasBuilding|deformable longitudinal DTI atlas method]] can be used.
* '''DTI atlas mapping:''' An already existing DTI atlas can possibly be mapped to individual subjects. DTI-Reg can be used in that regard to perform DTI pairwise registration.

[[Image:NeonateAtlas_Tracts.png|thumb|right|300px|Fiber tracts defined in a DTI neonate atlas, DTI average of 270 individual subjects]]

==='''Tractography within 3D Slicer: '''===
Tractography is performed on the DTI atlas to generate a template geometry for tracts of interest. Several methods can be used in that regard:
* Single tensor-tractography Label seeding and ROI select
* Multi-tensor tractography with unscented kalman filter

[[Image:Tract-stats.png|thumb|right|300px|White matter diffusion properties along fiber tract: Left: Uncinate fasiculus with coordinate origin plane, Right: FA mean and standard deviation as function of arc-length, starting at frontal region. Dots mark location of coordinate origin.]]

==='''Fiber cleanup/clustering: '''===
Tracts generated on the DTI atlas often need to be cleaned up. FiberViewerLight performs such clustering via length, gravity, hausdorff and mean based methods, but also a normalized cut algorithm on pairwise mean distances. FiberViewerLight also provides 3D fibers visualization and 3D plane selection, for future FA analysis along fibers.

==='''Subjects fiber profile information via DTIAtlasFiberAnalyzer: '''===
Fiber tracts defined on the atlas are mapped to the individual subjects using previously computed deformations fields. Various tract-oriented scalar diffusion measures obtained from DTI brain images, are treated as a continuous function of white matter fibers' arc-length. To analyze the trend along a given fiber tract, a command line tool performs kernel regression on this data. Fiber profile information are gathered across subjects in spreadsheets for statistical analysis.

==='''Statistical analysis performed by statistician: '''===
Statistical analysis can be performed on fiber tracts to find cross-sectional or longitudinal intra- or interpatient differences.

==='''Merging statistics back to the original fiber bundle: '''===
MergeStatWithFiber - an application part of DTI Fiber Tracts Statistics (dtitractstat) package-, allows population statistical information to be merged back to the atlas fiber tracts of interest.

==='''3D visualization within 3D Slicer: '''===
Statistically significant group and/or longitudinal differences can directly be displayed on fiber bundles of interest in 3D Slicer.

= Publications =

*Casey B. Goodlett, P. Thomas Fletcher, John H. Gilmore, Guido Gerig. Group Analysis of DTI Fiber Tract Statistics with Application to Neurodevelopment. NeuroImage 45 (1) Supp. 1, 2009. p. S133-S142.

= Key Investigators =
* UNC Algorithms: Jean-Baptiste Berger, Benjamin Yvernault, Clement Vachet, Yundi Shi, Aditya Gupta, Martin Styner
* Utah Algorithms: Anuja Sharma, Sylvain Gouttard, Guido Gerig

= Links =
*[http://www.niral.unc.edu/download-software UNC NIRAL software download page]
*[http://www.nitrc.org/projects/dti_brain_atlas Human brain DTI atlas]

[[Category: Diffusion MRI]]

Projects:AtlasBasedDTIFiberAnalyzerFramework

2011-12-02T18:55:21Z

Cvachet: /* Publications */

Back to [[Algorithm:UNC|UNC Algorithms]]
__NOTOC__

[[Image:UNC_DTIAnalysisFramework.jpg|thumb|right|400px|Atlas-based DTI fiber analysis framework]]

= Atlas Based DTI Fiber Analysis Framework =

This project aims to define an automatic framework for statistical comparison of fiber bundle diffusion properties between populations of diffusion weighted images.

Quality control is performed on diffusion weighted images and DTI images are computed for each individual subject. An unbiased diffeomorphic DTI atlas is then generated to compute a normalized coordinate system for populations of diffusion images. Fiber tracts of interest are generated on this atlas, and then mapped to the individual subjects. Diffusion properties along fiber tracts, such as fractional anisotropy (FA), are modeled as multivariate functions of arc length and gathered in spreadsheets for statistical analysis.

= Description =

The general framework entails the following steps:

==='''DWI and DTI quality control: '''===
DWI data suffers from inherent low SNR, overall long scanning time of multiple directional encoding with correspondingly large risk to encounter several kinds of artifacts. These artifacts can be too severe for a correct and stable estimation of the diffusion tensor. Thus, a quality control (QC) procedure is absolutely necessary for DTI studies.
We developed a tool called DTIPrep which pipelines the QC steps with designated protocol use and report generation, and which generates DTI images and related scalar maps. [[Projects:DTI_DWI_QualityControl|More...]]

==='''DTI preprocessing- skull-stripping: '''===
Skull-stripping is performed on DTI images and scalar maps. Several methods can be used in that regard:
* Direct Otsu Thresholding
* Masking using tissue label map generated by an intermediate atlas-based tissue segmentation, performed either on the idWI & B0 images or on the structural images (T1w & T2w).

[[Image:DTI_Atlas_Population_FiberTracts.png|thumb|right|300px|DTI atlas generation with fiber tracts]]

==='''Unbiased DTI atlas building or atlas mapping: '''===
Unbiased diffeomorphic atlas building is used to compute a normalized coordinate system for populations of diffusion images. The diffeomorphic transformations between each subject and the atlas provide spatial normalization for the comparison of tract statistics.
* '''DTI atlas creation:''' A DTI atlas can be generated for a specific study by averaging all individual subjects. For longitudinal studies, a [[Projects:LongitudinalAtlasBuilding|deformable longitudinal DTI atlas method]] can be used.
* '''DTI atlas mapping:''' An already existing DTI atlas can possibly be mapped to individual subjects. DTI-Reg can be used in that regard to perform DTI pairwise registration.

[[Image:NeonateAtlas_Tracts.png|thumb|right|300px|Fiber tracts defined in a DTI neonate atlas, DTI average of 270 individual subjects]]

==='''Tractography within 3D Slicer: '''===
Tractography is performed on the DTI atlas to generate tracts of interest. Several methods can be used in that regard:
* Single tensor-tractography Label seeding and ROI select
* Multi-tensor tractography with unscented kalman filter

[[Image:Tract-stats.png|thumb|right|300px|White matter diffusion properties along fiber tract: Left: Uncinate fasiculus with coordinate origin plane, Right: FA mean and standard deviation as function of arc-length, starting at frontal region. Dots mark location of coordinate origin.]]

==='''Fiber cleanup/clustering: '''===
Tracts generated on the DTI atlas often need to be cleaned up. FiberViewerLight performs such clustering via length, gravity, hausdorff and mean based methods, but also a normalized cut algorithm on pairwise mean distances. FiberViewerLight also provides 3D fibers visualization and 3D plane selection, for future FA analysis along fibers.

==='''Subjects fiber profile information via DTIAtlasFiberAnalyzer: '''===
Fiber tracts defined on the atlas are mapped to the individual subjects using previously computed deformations fields. Various tract-oriented scalar diffusion measures obtained from DTI brain images, are treated as a continuous function of white matter fibers' arc-length. To analyze the trend along a given fiber tract, a command line tool performs kernel regression on this data. Fiber profile information are gathered across subjects in spreadsheets for statistical analysis.

==='''Statistical analysis performed by statistician: '''===
Statistical analysis can be performed on fiber tracts to find cross-sectional or longitudinal intra- or interpatient differences.

==='''Merging statistics back to the original fiber bundle: '''===
MergeStatWithFiber - an application part of DTI Fiber Tracts Statistics (dtitractstat) package-, allows population statistical information to be merged back to the atlas fiber tracts of interest.

==='''3D visualization within 3D Slicer: '''===
Statistically significant group and/or longitudinal differences can directly be displayed on fiber bundles of interest in 3D Slicer.

= Publications =

*Casey B. Goodlett, P. Thomas Fletcher, John H. Gilmore, Guido Gerig. Group Analysis of DTI Fiber Tract Statistics with Application to Neurodevelopment. NeuroImage 45 (1) Supp. 1, 2009. p. S133-S142.

= Key Investigators =
* UNC Algorithms: Jean-Baptiste Berger, Benjamin Yvernault, Clement Vachet, Yundi Shi, Aditya Gupta, Martin Styner
* Utah Algorithms: Anuja Sharma, Sylvain Gouttard, Guido Gerig

= Links =
*[http://www.niral.unc.edu/download-software UNC NIRAL software download page]
*[http://www.nitrc.org/projects/dti_brain_atlas Human brain DTI atlas]

[[Category: Diffusion MRI]]

Projects:AtlasBasedDTIFiberAnalyzerFramework

2011-12-02T18:44:55Z

Cvachet:

Back to [[Algorithm:UNC|UNC Algorithms]]
__NOTOC__

[[Image:UNC_DTIAnalysisFramework.jpg|thumb|right|400px|Atlas-based DTI fiber analysis framework]]

= Atlas Based DTI Fiber Analysis Framework =

This project aims to define an automatic framework for statistical comparison of fiber bundle diffusion properties between populations of diffusion weighted images.

Quality control is performed on diffusion weighted images and DTI images are computed for each individual subject. An unbiased diffeomorphic DTI atlas is then generated to compute a normalized coordinate system for populations of diffusion images. Fiber tracts of interest are generated on this atlas, and then mapped to the individual subjects. Diffusion properties along fiber tracts, such as fractional anisotropy (FA), are modeled as multivariate functions of arc length and gathered in spreadsheets for statistical analysis.

= Description =

The general framework entails the following steps:

==='''DWI and DTI quality control: '''===
DWI data suffers from inherent low SNR, overall long scanning time of multiple directional encoding with correspondingly large risk to encounter several kinds of artifacts. These artifacts can be too severe for a correct and stable estimation of the diffusion tensor. Thus, a quality control (QC) procedure is absolutely necessary for DTI studies.
We developed a tool called DTIPrep which pipelines the QC steps with designated protocol use and report generation, and which generates DTI images and related scalar maps. [[Projects:DTI_DWI_QualityControl|More...]]

==='''DTI preprocessing- skull-stripping: '''===
Skull-stripping is performed on DTI images and scalar maps. Several methods can be used in that regard:
* Direct Otsu Thresholding
* Masking using tissue label map generated by an intermediate atlas-based tissue segmentation, performed either on the idWI & B0 images or on the structural images (T1w & T2w).

[[Image:DTI_Atlas_Population_FiberTracts.png|thumb|right|300px|DTI atlas generation with fiber tracts]]

==='''Unbiased DTI atlas building or atlas mapping: '''===
Unbiased diffeomorphic atlas building is used to compute a normalized coordinate system for populations of diffusion images. The diffeomorphic transformations between each subject and the atlas provide spatial normalization for the comparison of tract statistics.
* '''DTI atlas creation:''' A DTI atlas can be generated for a specific study by averaging all individual subjects. For longitudinal studies, a [[Projects:LongitudinalAtlasBuilding|deformable longitudinal DTI atlas method]] can be used.
* '''DTI atlas mapping:''' An already existing DTI atlas can possibly be mapped to individual subjects. DTI-Reg can be used in that regard to perform DTI pairwise registration.

[[Image:NeonateAtlas_Tracts.png|thumb|right|300px|Fiber tracts defined in a DTI neonate atlas, DTI average of 270 individual subjects]]

==='''Tractography within 3D Slicer: '''===
Tractography is performed on the DTI atlas to generate tracts of interest. Several methods can be used in that regard:
* Single tensor-tractography Label seeding and ROI select
* Multi-tensor tractography with unscented kalman filter

[[Image:Tract-stats.png|thumb|right|300px|White matter diffusion properties along fiber tract: Left: Uncinate fasiculus with coordinate origin plane, Right: FA mean and standard deviation as function of arc-length, starting at frontal region. Dots mark location of coordinate origin.]]

==='''Fiber cleanup/clustering: '''===
Tracts generated on the DTI atlas often need to be cleaned up. FiberViewerLight performs such clustering via length, gravity, hausdorff and mean based methods, but also a normalized cut algorithm on pairwise mean distances. FiberViewerLight also provides 3D fibers visualization and 3D plane selection, for future FA analysis along fibers.

==='''Subjects fiber profile information via DTIAtlasFiberAnalyzer: '''===
Fiber tracts defined on the atlas are mapped to the individual subjects using previously computed deformations fields. Various tract-oriented scalar diffusion measures obtained from DTI brain images, are treated as a continuous function of white matter fibers' arc-length. To analyze the trend along a given fiber tract, a command line tool performs kernel regression on this data. Fiber profile information are gathered across subjects in spreadsheets for statistical analysis.

==='''Statistical analysis performed by statistician: '''===
Statistical analysis can be performed on fiber tracts to find cross-sectional or longitudinal intra- or interpatient differences.

==='''Merging statistics back to the original fiber bundle: '''===
MergeStatWithFiber - an application part of DTI Fiber Tracts Statistics (dtitractstat) package-, allows population statistical information to be merged back to the atlas fiber tracts of interest.

==='''3D visualization within 3D Slicer: '''===
Statistically significant group and/or longitudinal differences can directly be displayed on fiber bundles of interest in 3D Slicer.

= Publications =

= Key Investigators =
* UNC Algorithms: Jean-Baptiste Berger, Benjamin Yvernault, Clement Vachet, Yundi Shi, Aditya Gupta, Martin Styner
* Utah Algorithms: Anuja Sharma, Sylvain Gouttard, Guido Gerig

= Links =
*[http://www.niral.unc.edu/download-software UNC NIRAL software download page]
*[http://www.nitrc.org/projects/dti_brain_atlas Human brain DTI atlas]

[[Category: Diffusion MRI]]

Projects:AtlasBasedDTIFiberAnalyzerFramework

2011-12-02T17:18:19Z

Cvachet:

Back to [[Algorithm:UNC|UNC Algorithms]]
__NOTOC__

[[Image:UNC_DTIAnalysisFramework.jpg|thumb|right|400px|Atlas-based DTI fiber analysis framework]]

= Atlas Based DTI Fiber Analysis Framework =

This project aims to define an automatic framework for statistical comparison of fiber bundle diffusion properties between populations of diffusion weighted images.

Quality control is performed on diffusion weighted images and DTI images are computed for each individual subject. An unbiased diffeomorphic DTI atlas is then generated to compute a normalized coordinate system for populations of diffusion images. Fiber tracts of interest are generated on this atlas, and then mapped to the individual subjects. Diffusion properties along fiber tracts, such as fractional anisotropy (FA), are modeled as multivariate functions of arc length and gathered in spreadsheets for statistical analysis.

= Description =

The general framework entails the following steps:

==='''DWI and DTI quality control: '''===
DWI data suffers from inherent low SNR, overall long scanning time of multiple directional encoding with correspondingly large risk to encounter several kinds of artifacts. These artifacts can be too severe for a correct and stable estimation of the diffusion tensor. Thus, a quality control (QC) procedure is absolutely necessary for DTI studies.
We developed a tool called DTIPrep which pipelines the QC steps with designated protocol use and report generation, and which generates DTI images and related scalar maps. [[Projects:DTI_DWI_QualityControl|More...]]

==='''DTI preprocessing- skull-stripping: '''===
Skull-stripping is performed on DTI images and scalar maps. Several methods can be used in that regard:
* Direct Otsu Thresholding
* Masking using tissue label map generated by an intermediate atlas-based tissue segmentation, performed either on the idWI & B0 images or on the structural images (T1w & T2w).

[[Image:DTI_Atlas_Population_FiberTracts.png|thumb|right|250px|DTI atlas generation with fiber tracts]]

==='''Unbiased DTI atlas building or atlas mapping: '''===
Unbiased diffeomorphic atlas building is used to compute a normalized coordinate system for populations of diffusion images. The diffeomorphic transformations between each subject and the atlas provide spatial normalization for the comparison of tract statistics.
* '''DTI atlas creation:''' A DTI atlas can be generated for a specific study by averaging all individual subjects. For longitudinal studies, a [[Projects:LongitudinalAtlasBuilding|deformable longitudinal DTI atlas method]] can be used.
* '''DTI atlas mapping:''' An already existing DTI atlas can possibly be mapped to individual subjects. DTI-Reg can be used in that regard to perform DTI pairwise registration.

==='''Tractography within 3D Slicer: '''===
Tractography is performed on the DTI atlas to generate tracts of interest. Several methods can be used in that regard:
* Single tensor-tractography Label seeding and ROI select
* Multi-tensor tractography with unscented kalman filter

[[Image:NeonateAtlas_Tracts.png|thumb|right|250px|Fiber tracts defined in DTI neonate atlas, average of 270 individual subjects]]

==='''Fiber cleanup/clustering: '''===
Tracts generated on the DTI atlas often need to be cleaned up. FiberViewerLight performs such clustering via length, gravity, hausdorff and mean based methods, but also a normalized cut algorithm on pairwise mean distances. FiberViewerLight also provides 3D fibers visualization and 3D plane selection, for future FA analysis along fibers.

==='''Subjects fiber profile information via DTIAtlasFiberAnalyzer: '''===
Fiber tracts defined on the atlas are mapped to the individual subjects using previously computed deformations fields. Various tract-oriented scalar diffusion measures obtained from DTI brain images, are treated as a continuous function of white matter fibers' arc-length. To analyze the trend along a given fiber tract, a command line tool performs kernel regression on this data. Fiber profile information are gathered across subjects in spreadsheets for statistical analysis.

==='''Statistical analysis performed by statistician: '''===
Statistical analysis can be performed on fiber tracts to find cross-sectional or longitudinal intra- or interpatient differences.

==='''Merging statistics back to the original fiber bundle: '''===
MergeStatWithFiber - an application part of DTI Fiber Tracts Statistics (dtitractstat) package-, allows population statistical information to be merged back to the atlas fiber tracts of interest.

==='''3D visualization within 3D Slicer: '''===
Statistically significant group and/or longitudinal differences can directly be displayed on fiber bundles of interest in 3D Slicer.

= Publications =

= Key Investigators =
* UNC Algorithms: Jean-Baptiste Berger, Benjamin Yvernault, Clement Vachet, Yundi Shi, Aditya Gupta, Martin Styner
* Utah Algorithms: Anuja Sharma, Sylvain Gouttard, Guido Gerig

= Links =
*[http://www.niral.unc.edu/download-software UNC NIRAL software download page]
*[http://www.nitrc.org/projects/dti_brain_atlas Human brain DTI atlas]

[[Category: Diffusion MRI]]

Algorithm:UNC

2011-12-02T17:16:33Z

Cvachet: /* UNC Projects */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of UNC Algorithms (PI: Martin Styner) =

At UNC, we are interested in a range of algorithms and solutions for the surface based analysis of brain structures and the cortex. We pioneered the use of spherical harmonics based shape analysis for comparing brain structures across objects. We has also worked on incorporating various data sources for correspondence computation on surfaces of different complexity (ranging from simple brain structures to the highly folded cortical surface). A current topic includes the use of diffusion tensor imaging for connectivity analysis in pathological settings. Finally, investigating quality control, validation and evaluation methodology is another important topic of our NA-MIC research.

= UNC Projects =

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

| style="width:15%" |[[Image:UNC_DTIAnalysisFramework.jpg|200px]]
| style="width:85%" |

== [[Projects:AtlasBasedDTIFiberAnalyzerFramework| Atlas Based DTI Fiber Analysis Framework]] ==
This project aims to define an automatic framework for statistical comparison of fiber bundle diffusion properties between populations of diffusion weighted images.

Quality control is performed on diffusion weighted images and DTI images are computed for each individual subject. An unbiased diffeomorphic DTI atlas is then generated to compute a normalized coordinate system for populations of diffusion images. Fiber tracts of interest are generated on this atlas, and then mapped to the individual subjects. Diffusion properties along fiber tracts, such as fractional anisotropy (FA), are modeled as multivariate functions of arc length and gathered in spreadsheets for statistical analysis.
[[Projects:AtlasBasedDTIFiberAnalyzerFramework|More...]]

|-

| style="width:15%" |[[Image:UNC_longitudinalAtlasEx1.png|200px]]
| style="width:85%" |

== [[Projects:LongitudinalAtlasBuilding| Longitudinal Atlas Building]] ==
As part of the longitudinal intra- and interpatient analysis theme within NA-MIC, we are working on a deformable, longitudinal DTI atlas method. Our longitudinal framework explicitly accounts for temporal dependencies via iterative subject-specific statistical growth modeling, and cross-sectional atlas-building. To effectively account for measurements sparse in time, a continuous-discrete statistical growth model is proposed incorporating also patient co-variates[[Projects:LongitudinalAtlasBuilding|More...]]

<font color="red">'''New: '''</font> Gabe Hart, Yundi Shi , Hongtu Zhu, Mar Sanchez, Martin Styner, Marc Niethammer. DTI Longitudinal Atlas Construction as an Average of Growth Models. Workshop on Spatio-Temporal Image Analysis for Longitudinal and Time-Series Image Data, MICCAI 2010 Aug.;

|-

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

== [[Projects:DWIAtlas|Diffusion Weighted Atlas Construction via model-based transformation and averaging of signal]] ==
This project investigated a method for model-based averaging of sets of diffusion weighted magnetic resonance images (DW-MRI) under space transformations (resulting for example from registration methods). A robust weighted least squares method is developed. Synthetic validation experiments show the improvement of the proposed estimation method in comparison to standard least squares estimation. The developed method is applied to construct an atlas of {\it diffusion weighted images} for a set of macaques, allowing for a more flexible representation of average diffusion information compared to standard diffusion tensor atlases.
[[Projects:DWIAtlas|More...]]

<font color="red">'''New: '''</font> Y. Shi, S. Benzaid, M. Sanchez, M. Styner, M. Niethammer. Diffusion Weighted Atlas Construction via robust model-based transformation. NeuroImage, in preparation.

<font color="red">'''New: '''</font> M. Niethammer, Y. Shi, S. Benzaid, M. Sanchez, and M. Styner. Robust model-based transformation and averaging of diffusion weighted images applied to diffusion weighted atlas construction. MICCAI, Workshop on Computational Diffusion MRI, 2010.

|-

| style="width:15%" |[[Image:UNC_GraphbasedConnectivity_Ex1.png‎|200px]]
| style="width:85%" |

== [[Projects:DiffusionGraphBasedConnectivity|Diffusion Imaging based Connectivity]] ==

This project focuses on connectivity measurements derived from diffusion imaging datasets in order to better understand cortical and subcortical white matter connectivity. Our research employs a novel, multi-directional graph propagation method that performs a fully deterministic, efficient and stable connectivity computation. The method handles crossing fibers and deals well with multiple seed regions. In addition to the analysis of these connectivity measures in describing brain pathology, they can also be used as scalar maps for use in DTI registration.
[[Projects:DiffusionGraphBasedConnectivity|More...]]

<font color="red">'''New: '''</font> Alexis Boucharin, Ipek Oguz, Clement Vachet, Yundi Shi, Mar Sanchez, Martin Styner. Efficient, graph-based white matter connectivity from orientation distribution functions via multi-directional graph propagation. Medical Imaging 2011: Image Processing (2011) vol. 7962 (1) pp. 79620S

|-

| style="width:15%" |[[Image:DTIPrep_example1.png|200px]]
| style="width:85%" |

== [[Projects:DTI_DWI_QualityControl|DWI and DTI Quality Control]] ==

DWI data suffers from inherent low SNR, overall long scanning time of multiple directional encoding with correspondingly large risk to encounter several kinds of artifacts. These artifacts can be too severe for a correct and stable estimation of the diffusion tensor. Thus, a quality control (QC) procedure is absolutely necessary for DTI studies. We are developing a framework for automatic DWI and DTI quality assessment and correction. We developed a tool called DTIPrep which pipelines the QC steps with designated protocol use and report generation. [[Projects:DTI_DWI_QualityControl|More...]]

<font color="red">'''New: '''</font> DTIPrep first full version on [http://www.nitrc.org/projects/dtiprep/ NITRC ]

<font color="red">'''New: '''</font> Zhexing Liu, Casey Goodlett, Guido Gerig, Martin Styner. Evaluation of DTI property maps as basis of DTI atlas building. Medical Imaging 2010: Image Processing (2010) vol. 7623 (1) pp. 762325

<font color="red">'''New: '''</font> Zhexing Liu, Yi Wang, Guido Gerig, Sylvain Gouttard, Ran Tao, Thomas Fletcher, Martin Styner. Quality control of diffusion weighted images. Medical Imaging 2010: Advanced PACS-based Imaging Informatics and Therapeutic Applications (2010) vol. 7628 (1) pp. 76280J

|-

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

== [[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. This correspondence method has been included in our NAMIC cortical thickness framework GAMBIT [[Projects:CorticalCorrespondenceWithParticleSystem|More...]]

<font color="red">'''New: '''</font> Vachet, C., Hazlett, H., Niethammer, M., Oguz, I., Cates, J., Whitaker, R., Piven, J., Styner, M., “Group-wise automatic mesh-based analysis of cortical thickness“. Medical Imaging 2011: Image Processing (2011) vol. 7962 (1) pp. 796227 1 - 10

<font color="red">'''New: '''</font> Lee J, Ehlers C, Crews F, Niethammer M, Budin F, Paniagua B, Sulik K, Johns J, Styner M, Oguz I. Automatic cortical thickness analysis on rodent brain. Medical Imaging 2011: Image Processing (2011) vol. 7962 (1) pp. 796248

|-

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

== [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|UNC-Utah Shape Analysis Framework]] ==

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-PDM) with correspondence and tested via statistical point-wise analysis. Additionally, the SPHARM correspondences can be improved with Entropy-based particle systems, by using an integration module recently added to the pipeline. [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|More...]]

* SPHARM-Particle Shape Analysis Toolkit disseminated on [http://www.nitrc.org/projects/spharm-pdm NITRC SPHARM PDM page]. All tools are Slicer compatible.
* Single Slicer 3 module for whole shape analysis pipeline with automatic generation of Slicer MRML scenes for result visualization

<font color="red">'''New: '''</font> Mark Walterfang, Jeffrey Chee Leong Looi, Martin Styner, Ruth H Walker, Adrian Danek, Marc Neithammer, Andrew Evans, Katya Kotschet, Guilherme R Rodrigues, Andrew Hughes, Dennis Velakoulis. Shape alterations in the striatum in chorea-acanthocytosis. Psychiatry research (2011) vol. 192 (1), pp. 29-36

<font color="red">'''New: '''</font> Beatriz Paniagua, Lucia Cevidanes, David Walker, Hongtu Zhu, Ruixin Guo, Martin Styner. Clinical application of SPHARM-PDM to quantify temporomandibular joint osteoarthritis. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society (2011) vol. 35(5), pp. 345-352

<font color="red">'''New: '''</font> Beatriz Paniagua, Lucia Cevidanes, Hongtu Zhu, Martin Styner. Outcome quantification using SPHARM-PDM toolbox in orthognathic surgery. International journal of computer assisted radiology and surgery (2011) vol. 6 (5) pp. 617-626

<font color="red">'''New: '''</font> Jeffrey Chee Leong Looi, Mark Walterfang, Martin Styner, Leif Svensson, Olof Lindberg, Per Ostberg, Lisa Botes, Eva Orndahl, Phyllis Chua, Rajeev Kumar, Dennis Velakoulis, Lars-Olof Wahlund. Shape analysis of the neostriatum in frontotemporal lobar degeneration, Alzheimer's disease, and controls. Neuroimage (2010) vol. 51 (3) pp. 970-86

<font color="red">'''New: '''</font> Maltbie E, Bhatt K, Paniagua B, Smith RG, Graves MM, Mosconi MW, Peterson S, White S, Blocher J, El-Sayed M, Hazlett HC, Styner M. Asymmetric bias in user guided segmentations of brain structures. NeuroImage 2011 Aug. [Epub ahead of print]

<font color="red">'''New: '''</font> Datar M, Gur Y, Paniagua B, Styner M, Whitaker R. Geometric Correspondence for Ensembles of. MICCAI 2011, Part II 2011 Aug.;6892:368–375.

<font color="red">'''New: '''</font> Looi JCL, Macfarlane MD, Walterfang M, Styner M, Velakoulis D, Lätt J, van Westen D, Nilsson C. Morphometric analysis of subcortical structures in progressive supranuclear palsy: In vivo evidence of neostriatal and mesencephalic atrophy. Psychiatry Research: Neuroimaging 2011 Sep.;:1–13

|-

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

== [[Projects:LocalStatisticalAnalysisViaPermutationTests|Local Statistical Analysis via Permutation Tests]] ==

We have further developed a set of statistical testing methods that allow the analysis of local shape differences via group differences tests as well interaction tests. Resulting significance maps (both raw and corrected for multiple comparisons) are easily visualized. Additional visualization of the group tests are provided via mean difference magnitude and vector maps, as well as maps of the group covariance information. Additional visualization of the interaction tests include Pearson and Spearman correlation maps. [[Projects:LocalStatisticalAnalysisViaPermutationTests|More...]]

<font color="red">'''New: '''</font> User-friendly GUI interface and statistical result visualization via automatically generated Slicer MRML scenes

<font color="red">'''New: '''</font> Available on NITRC either [http://www.nitrc.org/projects/shape_mancova separately (ShapeAnalysisMANCOVA)] or as part of the [http://www.nitrc.org/projects/spharm-pdm SPHARM-PDM shape analysis package]

* Paniagua B., Styner M., Macenko M., Pantazis D., Niethammer M, Local Shape Analysis using MANCOVA, Insight Journal, 2009 July-December, http://hdl.handle.net/10380/3124

|-
| | [[Image:Cause07Competition.gif|200px]]
| |

== [[Projects:MethodEvaluationValidation|Evaluation and Comparison of Medical Image Analysis Methods]] ==

In this project, we want to focus on the evaluation of medical image analysis methods for specific clinical applications in respect to development of evaluation methodology and the organization of venues promoting such comparison and validation studies.

<font color="red">'''New: '''</font> [[Events:_DTI_Tractography_Challenge_MICCAI_2011 | DTI fiber tractography challenge]] at MICCAI 2011

|-

| | [[Image:UNCShape_CaudatePval_MICCAI06.png|200px]]
| |

== [[Projects:PopulationBasedCorrespondence|Population Based Correspondence]] ==

We are developing methodology to automatically find dense point correspondences between a collection of polygonal genus 0 meshes. The advantage of this method is independence from indivisual templates, as well as enhanced modeling properties. The method is based on minimizing a cost function that describes the goodness of correspondence. Apart from a cost function derived from the description length of the model, we also employ a cost function working with arbitrary local features. We extended the original methods to use surface curvature measurements, which are independent to differences of object aligment. [[Projects:PopulationBasedCorrespondence|More...]]

* Styner M., Oguz I., Heimann T., Gerig G. Minimum description length with local geometry. Proceedings of the 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro 2008; 1283-1286
* Software available as part of UNC Neurolib open source ([http://www.ia.unc.edu/dev website])

|-

|}

File:DTI Atlas Population FiberTracts.png

2011-12-02T15:54:25Z

Cvachet:

Projects:AtlasBasedDTIFiberAnalyzerFramework

2011-12-02T15:50:12Z

Cvachet:

Back to [[Algorithm:UNC|UNC Algorithms]]
__NOTOC__

= Atlas Based DTI Fiber Analysis Framework =

This project aims to define an automatic framework for statistical comparison of fiber bundle diffusion properties between populations of diffusion weighted images.

= Description =

The general framework entails the following steps:

'''DWI and DTI quality control: '''
DWI data suffers from inherent low SNR, overall long scanning time of multiple directional encoding with correspondingly large risk to encounter several kinds of artifacts. These artifacts can be too severe for a correct and stable estimation of the diffusion tensor. Thus, a quality control (QC) procedure is absolutely necessary for DTI studies.
We developed a tool called DTIPrep which pipelines the QC steps with designated protocol use and report generation, and which generates DTI images and related scalar maps. [[Projects:DTI_DWI_QualityControl|More...]]

[[Image:NeonateAtlas_Tracts.png|thumb|300px|Fiber tracts defined in neonate atlas]]

'''DTI preprocessing: skull-stripping'''
Skull-stripping is performed on DTI images and scalar maps. Several methods can be used in that regard:
* Direct Otsu Thresholding
* Masking using tissue label map from an intermediate atlas-based tissue segmentation, performed either on the idWI & B0 images or on the structural images (T1w & T2w).

'''Unbiased DTI atlas building or atlas mapping: '''
Unbiased diffeomorphic atlas building is used to compute a normalized coordinate system for populations of diffusion images. The diffeomorphic transformations between each subject and the atlas provide spatial normalization for the comparison of tract statistics.
* '''DTI atlas creation:''' A DTI atlas can be generated for a specific study by averaging all individual subjects. For longitudinal studies, a [[Projects:LongitudinalAtlasBuilding|deformable longitudinal DTI atlas method]] can be used.
* '''DTI atlas mapping:''' An already existing DTI atlas can possibly be mapped to individual subjects. DTI-Reg can be used in that regard to perform DTI pairwise registration.

'''Tractography within 3D Slicer: ''' Tractography is performed on the DTI atlas to generate tracts of interest. Several methods can be used in that regard:
* Single tensor-tractography Label seeding and ROI select
* Multi-tensor tractography with unscented kalman filter

'''Fiber cleanup/clustering: '''
Tracts generated on the DTI atlas often need to be cleaned up. FiberViewerLight enables several clustering methods: Length, Gravity, Hausdorff, and Mean methods but also a Normalized Cut algorithm.

'''DTIAtlasFiberAnalyzer: '''

'''Statistical analysis performed by statistician: '''

'''Merging statistics back to the original fiber bundle: '''
MergeStatWithFiber - an application part of DTI Fiber Tracts Statistics package-, allows population statistical information to be merged back to the atlas fiber bundle.

'''3D visualization within 3D Slicer: '''
Statically significan group differences can directly be displayed on a fiber bundle in 3D Slicer.

[[Image:UNC_DTIAnalysisFramework.jpg|600px|Atlas-based DTI fiber analysis framework]]

= Publications =

= Key Investigators =
* UNC Algorithms: Jean-Baptiste Berger, Benjamin Yvernault, Clement Vachet, Yundi Shi, Aditya Gupta, Martin Styner
* Utah Algorithms: Anuja Sharma, Sylvain Gouttard, Guido Gerig

= Links =
*[http://www.niral.unc.edu/download-software UNC NIRAL software download page]
*[http://www.nitrc.org/projects/dti_brain_atlas Human brain DTI atlas]

[[Category: Diffusion MRI]]

File:UNC DTIAnalysisFramework.jpg

2011-12-02T15:10:07Z

Cvachet:

Projects:AtlasBasedDTIFiberAnalyzerFramework

2011-12-02T01:37:13Z

Cvachet:

Back to [[Algorithm:UNC|UNC Algorithms]]
__NOTOC__

= Atlas Based DTI Fiber Analysis Framework =

This project aims to define an automatic framework for statistical comparison of fiber bundle diffusion properties between populations of diffusion weighted images.

= Description =

The general framework entails the following steps:

'''DWI and DTI quality control: '''
DWI data suffers from inherent low SNR, overall long scanning time of multiple directional encoding with correspondingly large risk to encounter several kinds of artifacts. These artifacts can be too severe for a correct and stable estimation of the diffusion tensor. Thus, a quality control (QC) procedure is absolutely necessary for DTI studies.
We developed a tool called DTIPrep which pipelines the QC steps with designated protocol use and report generation. [[Projects:DTI_DWI_QualityControl|More...]]

[[Image:NeonateAtlas_Tracts.png|thumb|300px|Fiber tracts defined in neonate atlas]]

'''Unbiased DTI atlas building or atlas mapping: '''
Unbiased diffeomorphic atlas building is used to compute a normalized coordinate system for populations of diffusion images. The diffeomorphic transformations between each subject and the atlas provide spatial normalization for the comparison of tract statistics.
A DTI atlas can be generated for a specific study, or an existing DTI atlas can also possibly be mapped to individual subjects. DTI-Reg can be used in that regard to perform such DTI pairwise registration.

'''Tractography within 3D Slicer: ''' Tractography can be performed on the DTI atlas to generate tracts of interest. Several methods can be used in that regard:
* 3D Slicer modules: Label seeding and ROI select
* Tractography with unscented kalman filter

'''Fiber cleanup/clustering: '''
Tracts generated on the DTI atlas often needs to be cleaned up. FiberViewerLight enables several clustering methods: Length, Gravity, Hausdorff, and Mean methods but also a Normalized Cut algorithm.

'''DTIAtlasFiberAnalyzer: '''

'''Statistical analysis performed by statistician: '''

'''Merging statistics back to the original fiber bundle: '''
MergeStatWithFiber - an application part of DTI Fiber Tracts Statistics package-, allows population statistical information to be merged back to the atlas fiber bundle.

'''3D visualization within 3D Slicer: '''
Statically significan group differences can directly be displayed on a fiber bundle in 3D Slicer.

= Publications =

= Key Investigators =
* UNC Algorithms: Jean-Baptiste Berger, Benjamin Yvernault, Clement Vachet, Yundi Shi, Aditya Gupta, Martin Styner
* Utah Algorithms: Anuja Sharma, Sylvain Gouttard, Guido Gerig

= Links =
*[http://www.niral.unc.edu/download-software UNC NIRAL software download page]
*[http://www.nitrc.org/projects/dti_brain_atlas Human brain DTI atlas]

[[Category: Diffusion MRI]]

File:NeonateAtlas Tracts.png

2011-12-02T01:35:33Z

Cvachet:

Projects:AtlasBasedDTIFiberAnalyzerFramework

2011-11-28T22:58:50Z

Cvachet:

Back to [[Algorithm:UNC|UNC Algorithms]]
__NOTOC__

= Atlas Based DTI Fiber Analysis Framework =

This project aims to define an automatic framework for statistical comparison of fiber bundle diffusion properties between populations of diffusion weighted images.

= Description =
[[Image:AtlasBuilderLogo.jpg|thumb|300px|Length analysis of Cingulum tractography. Colors go from red to blue where red ones are the longest and blue ones are the shortest]]

The general framework entails the following steps:

'''DWI and DTI quality control: '''
DWI data suffers from inherent low SNR, overall long scanning time of multiple directional encoding with correspondingly large risk to encounter several kinds of artifacts. These artifacts can be too severe for a correct and stable estimation of the diffusion tensor. Thus, a quality control (QC) procedure is absolutely necessary for DTI studies.
We developed a tool called DTIPrep which pipelines the QC steps with designated protocol use and report generation. [[Projects:DTI_DWI_QualityControl|More...]]

'''Unbiased DTI atlas building or atlas mapping: '''
Unbiased diffeomorphic atlas building is used to compute a normalized coordinate system for populations of diffusion images. The diffeomorphic transformations between each subject and the atlas provide spatial normalization for the comparison of tract statistics.
A DTI atlas can be generated for a specific study, or an existing DTI atlas can also possibly be mapped to individual subjects. DTI-Reg can be used in that regard to perform such DTI pairwise registration.

'''Tractography within 3D Slicer: ''' Tractography can be performed on the DTI atlas to generate tracts of interest. Several methods can be used in that regard:
* 3D Slicer modules: Label seeding and ROI select
* Tractography with unscented kalman filter

'''Fiber cleanup/clustering: '''
Tracts generated on the DTI atlas often needs to be cleaned up. FiberViewerLight enables several clustering methods: Length, Gravity, Hausdorff, and Mean methods but also a Normalized Cut algorithm.

'''DTIAtlasFiberAnalyzer: '''

'''Statistical analysis performed by statistician: '''

'''Merging statistics back to the original fiber bundle: '''
MergeStatWithFiber - an application part of DTI Fiber Tracts Statistics package-, allows population statistical information to be merged back to the atlas fiber bundle.

'''3D visualization within 3D Slicer: '''
Statically significan group differences can directly be displayed on a fiber bundle in 3D Slicer.

= Publications =

= Key Investigators =
* UNC Algorithms: Jean-Baptiste Berger, Benjamin Yvernault, Clement Vachet, Yundi Shi, Aditya Gupta, Martin Styner
* Utah Algorithms: Anuja Sharma, Sylvain Gouttard, Guido Gerig

= Links =
*[http://www.niral.unc.edu/download-software UNC NIRAL software download page]
*[http://www.nitrc.org/projects/dti_brain_atlas Human brain DTI atlas]

[[Category: Diffusion MRI]]

Projects:AtlasBasedDTIFiberAnalyzerFramework

2011-11-28T22:53:29Z

Cvachet:

Back to [[Algorithm:UNC|UNC Algorithms]]
__NOTOC__

= Atlas Based DTI FIber Analyzer Framework =

This project aims to define an automatic framework for statistical comparison of fiber bundle diffusion properties between populations of diffusion weighted images.

= Description =
[[Image:AtlasBuilderLogo.jpg|thumb|300px|Length analysis of Cingulum tractography. Colors go from red to blue where red ones are the longest and blue ones are the shortest]]

The general framework entails the following steps:

'''DWI and DTI quality control: '''
DWI data suffers from inherent low SNR, overall long scanning time of multiple directional encoding with correspondingly large risk to encounter several kinds of artifacts. These artifacts can be too severe for a correct and stable estimation of the diffusion tensor. Thus, a quality control (QC) procedure is absolutely necessary for DTI studies.
We developed a tool called DTIPrep which pipelines the QC steps with designated protocol use and report generation. [[Projects:DTI_DWI_QualityControl|More...]]

'''Unbiased DTI atlas building or atlas mapping: '''
Unbiased diffeomorphic atlas building is used to compute a normalized coordinate system for populations of diffusion images. The diffeomorphic transformations between each subject and the atlas provide spatial normalization for the comparison of tract statistics.
A DTI atlas can be generated for a specific study, or an existing DTI atlas can also possibly be mapped to individual subjects. DTI-Reg can be used in that regard to perform such DTI pairwise registration.

'''Tractography within 3D Slicer: ''' Tractography can be performed on the DTI atlas to generate tracts of interest. Several methods can be used in that regard:
* 3D Slicer modules: Label seeding and ROI select
* Tractography with unscented kalman filter

'''Fiber cleanup/clustering: '''
Tracts generated on the DTI atlas often needs to be cleaned up. FiberViewerLight enables several clustering methods: Length, Gravity, Hausdorff, and Mean methods but also a Normalized Cut algorithm.

'''DTIAtlasFiberAnalyzer: '''

'''Statistical analysis performed by statistician: '''

'''Merging statistics back to the original fiber bundle: '''
MergeStatWithFiber - an application part of DTI Fiber Tracts Statistics package-, allows population statistical information to be merged back to the atlas fiber bundle.

'''3D visualization within 3D Slicer: '''
Statically significan group differences can directly be displayed on a fiber bundle in 3D Slicer.

= Publications =

= Key Investigators =
* UNC Algorithms: Jean-Baptiste Berger, Benjamin Yvernault, Clement Vachet, Yundi Shi, Aditya Gupta, Martin Styner
* Utah Algorithms: Anuja Sharma, Sylvain Gouttard, Guido Gerig

= Links =
*[http://www.niral.unc.edu/download-software UNC NIRAL software download page]
*[http://www.nitrc.org/projects/dti_brain_atlas Human brain DTI atlas]

[[Category: Diffusion MRI]]

Projects:AtlasBasedDTIFiberAnalyzerFramework

2011-11-28T22:49:47Z

Cvachet:

Back to [[Algorithm:UNC|UNC Algorithms]]
__NOTOC__

= Atlas Based DTI FIber Analyzer Framework =

This project aims to define an automatic framework for statistical comparison of fiber bundle diffusion properties between populations of diffusion weighted images.

= Description =
[[Image:AtlasBuilderLogo.jpg|thumb|300px|Length analysis of Cingulum tractography. Colors go from red to blue where red ones are the longest and blue ones are the shortest]]

The general framework entails the following steps:

'''DWI and DTI quality control: '''
DWI data suffers from inherent low SNR, overall long scanning time of multiple directional encoding with correspondingly large risk to encounter several kinds of artifacts. These artifacts can be too severe for a correct and stable estimation of the diffusion tensor. Thus, a quality control (QC) procedure is absolutely necessary for DTI studies.
We developed a tool called DTIPrep which pipelines the QC steps with designated protocol use and report generation. [[Projects:DTI_DWI_QualityControl|More...]]

'''Unbiased DTI atlas building or atlas mapping: '''
Unbiased diffeomorphic atlas building is used to compute a normalized coordinate system for populations of diffusion images. The diffeomorphic transformations between each subject and the atlas provide spatial normalization for the comparison of tract statistics.
A DTI atlas can be generated for a specific study, or an existing DTI atlas can also possibly be mapped to individual subjects. DTI-Reg can be used in that regard to perform such DTI pairwise registration.

'''Tractography within 3D Slicer: '''
* 3D Slicer modules: Label seeding and ROI select
* Tractography with unscented kalman filter

'''Fiber cleanup/clustering: '''
FiberViewerLight enables several clustering methods: Length, Gravity, Hausdorff, and Mean methods but also a Normalized Cut algorithm.

'''DTIAtlasFiberAnalyzer: '''

'''Statistical analysis performed by statistician: '''

'''Merging statistics back to the original fiber bundle: '''
MergeStatWithFiber - an application part of DTI Fiber Tracts Statistics package-, allows population statistical information to be merged back to the atlas fiber bundle.

'''3D visualization within 3D Slicer: '''
Statically significan group differences can directly be displayed on a fiber bundle in 3D Slicer.

= Publications =

= Key Investigators =
* UNC Algorithms: Jean-Baptiste Berger, Benjamin Yvernault, Clement Vachet, Yundi Shi, Aditya Gupta, Martin Styner
* Utah Algorithms: Anuja Sharma, Sylvain Gouttard, Guido Gerig

= Links =
*[http://www.niral.unc.edu/download-software UNC NIRAL software download page]
*[http://www.nitrc.org/projects/dti_brain_atlas Human brain DTI atlas]

[[Category: Diffusion MRI]]

Projects:AtlasBasedDTIFiberAnalyzerFramework

2011-11-28T22:39:46Z

Cvachet:

Back to [[Algorithm:UNC|UNC Algorithms]]
__NOTOC__

= Atlas Based DTI FIber Analyzer Framework =

This project aims to define an automatic framework for statistical comparison of fiber bundle diffusion properties between populations of diffusion weighted images.

= Description =
[[Image:AtlasBuilderLogo.jpg|thumb|300px|Length analysis of Cingulum tractography. Colors go from red to blue where red ones are the longest and blue ones are the shortest]]

The general framework entails the following steps:

'''DWI and DTI quality control: '''
DWI data suffers from inherent low SNR, overall long scanning time of multiple directional encoding with correspondingly large risk to encounter several kinds of artifacts. These artifacts can be too severe for a correct and stable estimation of the diffusion tensor. Thus, a quality control (QC) procedure is absolutely necessary for DTI studies.
We developed a tool called DTIPrep which pipelines the QC steps with designated protocol use and report generation.

'''Unbiased DTI atlas building or atlas mapping'''
*Unbiased DTI atlas building
*Mapping of an existing DTI atlas

'''Tractography within 3D Slicer'''
* 3D Slicer modules: Label seeding and ROI select
* Tractography with unscented kalman filter

'''Fiber cleanup/clustering: '''
FiberViewerLight enables several clustering methods: Length, Gravity, Hausdorff, and Mean methods but also a Normalized Cut algorithm.

'''DTIAtlasFiberAnalyzer'''

'''Statistical analysis performed by statistician'''

'''Merging statistics back to the original fiber bundle'''
MergeStatWithFiber - an application part of DTI Fiber Tracts Statistics package-, allows population statistical information to be merged back to the atlas fiber bundle.

'''3D visualization within 3D Slicer'''
Statically significan group differences can directly be displayed on a fiber bundle in 3D Slicer.

= Publications =

= Key Investigators =
* UNC Algorithms: Jean-Baptiste Berger, Benjamin Yvernault, Clement Vachet, Yundi Shi, Aditya Gupta, Martin Styner
* Utah Algorithms: Anuja Sharma, Sylvain Gouttard, Guido Gerig

= Links =
*[http://www.niral.unc.edu/download-software UNC NIRAL software download page]
*[http://www.nitrc.org/projects/dti_brain_atlas Human brain DTI atlas]

[[Category: Diffusion MRI]]

Algorithm:UNC

2011-11-28T22:01:39Z

Cvachet: /* Atlas Based DTI Fiber Analyzer Framework */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of UNC Algorithms (PI: Martin Styner) =

At UNC, we are interested in a range of algorithms and solutions for the surface based analysis of brain structures and the cortex. We pioneered the use of spherical harmonics based shape analysis for comparing brain structures across objects. We has also worked on incorporating various data sources for correspondence computation on surfaces of different complexity (ranging from simple brain structures to the highly folded cortical surface). A current topic includes the use of diffusion tensor imaging for connectivity analysis in pathological settings. Finally, investigating quality control, validation and evaluation methodology is another important topic of our NA-MIC research.

= UNC Projects =

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

| style="width:15%" |[[Image:AtlasBuilderLogo.jpg|200px]]
| style="width:85%" |

== [[Projects:AtlasBasedDTIFiberAnalyzerFramework| Atlas Based DTI Fiber Analysis Framework]] ==
This project aims to define an automatic framework for statistical comparison of fiber bundle diffusion properties between populations of diffusion weighted images.
[[Projects:AtlasBasedDTIFiberAnalyzerFramework|More...]]

|-

| style="width:15%" |[[Image:UNC_longitudinalAtlasEx1.png|200px]]
| style="width:85%" |

== [[Projects:LongitudinalAtlasBuilding| Longitudinal Atlas Building]] ==
As part of the longitudinal intra- and interpatient analysis theme within NA-MIC, we are working on a deformable, longitudinal DTI atlas method. Our longitudinal framework explicitly accounts for temporal dependencies via iterative subject-specific statistical growth modeling, and cross-sectional atlas-building. To effectively account for measurements sparse in time, a continuous-discrete statistical growth model is proposed incorporating also patient co-variates[[Projects:LongitudinalAtlasBuilding|More...]]

<font color="red">'''New: '''</font> Gabe Hart, Yundi Shi , Hongtu Zhu, Mar Sanchez, Martin Styner, Marc Niethammer. DTI Longitudinal Atlas Construction as an Average of Growth Models. Workshop on Spatio-Temporal Image Analysis for Longitudinal and Time-Series Image Data, MICCAI 2010 Aug.;

|-

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

== [[Projects:DWIAtlas|Diffusion Weighted Atlas Construction via model-based transformation and averaging of signal]] ==
This project investigated a method for model-based averaging of sets of diffusion weighted magnetic resonance images (DW-MRI) under space transformations (resulting for example from registration methods). A robust weighted least squares method is developed. Synthetic validation experiments show the improvement of the proposed estimation method in comparison to standard least squares estimation. The developed method is applied to construct an atlas of {\it diffusion weighted images} for a set of macaques, allowing for a more flexible representation of average diffusion information compared to standard diffusion tensor atlases.
[[Projects:DWIAtlas|More...]]

<font color="red">'''New: '''</font> Y. Shi, S. Benzaid, M. Sanchez, M. Styner, M. Niethammer. Diffusion Weighted Atlas Construction via robust model-based transformation. NeuroImage, in preparation.

<font color="red">'''New: '''</font> M. Niethammer, Y. Shi, S. Benzaid, M. Sanchez, and M. Styner. Robust model-based transformation and averaging of diffusion weighted images applied to diffusion weighted atlas construction. MICCAI, Workshop on Computational Diffusion MRI, 2010.

|-

| style="width:15%" |[[Image:UNC_GraphbasedConnectivity_Ex1.png‎|200px]]
| style="width:85%" |

== [[Projects:DiffusionGraphBasedConnectivity|Diffusion Imaging based Connectivity]] ==

This project focuses on connectivity measurements derived from diffusion imaging datasets in order to better understand cortical and subcortical white matter connectivity. Our research employs a novel, multi-directional graph propagation method that performs a fully deterministic, efficient and stable connectivity computation. The method handles crossing fibers and deals well with multiple seed regions. In addition to the analysis of these connectivity measures in describing brain pathology, they can also be used as scalar maps for use in DTI registration.
[[Projects:DiffusionGraphBasedConnectivity|More...]]

<font color="red">'''New: '''</font> Alexis Boucharin, Ipek Oguz, Clement Vachet, Yundi Shi, Mar Sanchez, Martin Styner. Efficient, graph-based white matter connectivity from orientation distribution functions via multi-directional graph propagation. Medical Imaging 2011: Image Processing (2011) vol. 7962 (1) pp. 79620S

|-

| style="width:15%" |[[Image:DTIPrep_example1.png|200px]]
| style="width:85%" |

== [[Projects:DTI_DWI_QualityControl|DWI and DTI Quality Control]] ==

DWI data suffers from inherent low SNR, overall long scanning time of multiple directional encoding with correspondingly large risk to encounter several kinds of artifacts. These artifacts can be too severe for a correct and stable estimation of the diffusion tensor. Thus, a quality control (QC) procedure is absolutely necessary for DTI studies. We are developing a framework for automatic DWI and DTI quality assessment and correction. We developed a tool called DTIPrep which pipelines the QC steps with designated protocol use and report generation. [[Projects:DTI_DWI_QualityControl|More...]]

<font color="red">'''New: '''</font> DTIPrep first full version on [http://www.nitrc.org/projects/dtiprep/ NITRC ]

<font color="red">'''New: '''</font> Zhexing Liu, Casey Goodlett, Guido Gerig, Martin Styner. Evaluation of DTI property maps as basis of DTI atlas building. Medical Imaging 2010: Image Processing (2010) vol. 7623 (1) pp. 762325

<font color="red">'''New: '''</font> Zhexing Liu, Yi Wang, Guido Gerig, Sylvain Gouttard, Ran Tao, Thomas Fletcher, Martin Styner. Quality control of diffusion weighted images. Medical Imaging 2010: Advanced PACS-based Imaging Informatics and Therapeutic Applications (2010) vol. 7628 (1) pp. 76280J

|-

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

== [[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. This correspondence method has been included in our NAMIC cortical thickness framework GAMBIT [[Projects:CorticalCorrespondenceWithParticleSystem|More...]]

<font color="red">'''New: '''</font> Vachet, C., Hazlett, H., Niethammer, M., Oguz, I., Cates, J., Whitaker, R., Piven, J., Styner, M., “Group-wise automatic mesh-based analysis of cortical thickness“. Medical Imaging 2011: Image Processing (2011) vol. 7962 (1) pp. 796227 1 - 10

<font color="red">'''New: '''</font> Lee J, Ehlers C, Crews F, Niethammer M, Budin F, Paniagua B, Sulik K, Johns J, Styner M, Oguz I. Automatic cortical thickness analysis on rodent brain. Medical Imaging 2011: Image Processing (2011) vol. 7962 (1) pp. 796248

|-

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

== [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|UNC-Utah Shape Analysis Framework]] ==

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-PDM) with correspondence and tested via statistical point-wise analysis. Additionally, the SPHARM correspondences can be improved with Entropy-based particle systems, by using an integration module recently added to the pipeline. [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|More...]]

* SPHARM-Particle Shape Analysis Toolkit disseminated on [http://www.nitrc.org/projects/spharm-pdm NITRC SPHARM PDM page]. All tools are Slicer compatible.
* Single Slicer 3 module for whole shape analysis pipeline with automatic generation of Slicer MRML scenes for result visualization

<font color="red">'''New: '''</font> Mark Walterfang, Jeffrey Chee Leong Looi, Martin Styner, Ruth H Walker, Adrian Danek, Marc Neithammer, Andrew Evans, Katya Kotschet, Guilherme R Rodrigues, Andrew Hughes, Dennis Velakoulis. Shape alterations in the striatum in chorea-acanthocytosis. Psychiatry research (2011) vol. 192 (1), pp. 29-36

<font color="red">'''New: '''</font> Beatriz Paniagua, Lucia Cevidanes, David Walker, Hongtu Zhu, Ruixin Guo, Martin Styner. Clinical application of SPHARM-PDM to quantify temporomandibular joint osteoarthritis. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society (2011) vol. 35(5), pp. 345-352

<font color="red">'''New: '''</font> Beatriz Paniagua, Lucia Cevidanes, Hongtu Zhu, Martin Styner. Outcome quantification using SPHARM-PDM toolbox in orthognathic surgery. International journal of computer assisted radiology and surgery (2011) vol. 6 (5) pp. 617-626

<font color="red">'''New: '''</font> Jeffrey Chee Leong Looi, Mark Walterfang, Martin Styner, Leif Svensson, Olof Lindberg, Per Ostberg, Lisa Botes, Eva Orndahl, Phyllis Chua, Rajeev Kumar, Dennis Velakoulis, Lars-Olof Wahlund. Shape analysis of the neostriatum in frontotemporal lobar degeneration, Alzheimer's disease, and controls. Neuroimage (2010) vol. 51 (3) pp. 970-86

<font color="red">'''New: '''</font> Maltbie E, Bhatt K, Paniagua B, Smith RG, Graves MM, Mosconi MW, Peterson S, White S, Blocher J, El-Sayed M, Hazlett HC, Styner M. Asymmetric bias in user guided segmentations of brain structures. NeuroImage 2011 Aug. [Epub ahead of print]

<font color="red">'''New: '''</font> Datar M, Gur Y, Paniagua B, Styner M, Whitaker R. Geometric Correspondence for Ensembles of. MICCAI 2011, Part II 2011 Aug.;6892:368–375.

<font color="red">'''New: '''</font> Looi JCL, Macfarlane MD, Walterfang M, Styner M, Velakoulis D, Lätt J, van Westen D, Nilsson C. Morphometric analysis of subcortical structures in progressive supranuclear palsy: In vivo evidence of neostriatal and mesencephalic atrophy. Psychiatry Research: Neuroimaging 2011 Sep.;:1–13

|-

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

== [[Projects:LocalStatisticalAnalysisViaPermutationTests|Local Statistical Analysis via Permutation Tests]] ==

We have further developed a set of statistical testing methods that allow the analysis of local shape differences via group differences tests as well interaction tests. Resulting significance maps (both raw and corrected for multiple comparisons) are easily visualized. Additional visualization of the group tests are provided via mean difference magnitude and vector maps, as well as maps of the group covariance information. Additional visualization of the interaction tests include Pearson and Spearman correlation maps. [[Projects:LocalStatisticalAnalysisViaPermutationTests|More...]]

<font color="red">'''New: '''</font> User-friendly GUI interface and statistical result visualization via automatically generated Slicer MRML scenes

<font color="red">'''New: '''</font> Available on NITRC either [http://www.nitrc.org/projects/shape_mancova separately (ShapeAnalysisMANCOVA)] or as part of the [http://www.nitrc.org/projects/spharm-pdm SPHARM-PDM shape analysis package]

* Paniagua B., Styner M., Macenko M., Pantazis D., Niethammer M, Local Shape Analysis using MANCOVA, Insight Journal, 2009 July-December, http://hdl.handle.net/10380/3124

|-
| | [[Image:Cause07Competition.gif|200px]]
| |

== [[Projects:MethodEvaluationValidation|Evaluation and Comparison of Medical Image Analysis Methods]] ==

In this project, we want to focus on the evaluation of medical image analysis methods for specific clinical applications in respect to development of evaluation methodology and the organization of venues promoting such comparison and validation studies.

<font color="red">'''New: '''</font> [[Events:_DTI_Tractography_Challenge_MICCAI_2011 | DTI fiber tractography challenge]] at MICCAI 2011

|-

| | [[Image:UNCShape_CaudatePval_MICCAI06.png|200px]]
| |

== [[Projects:PopulationBasedCorrespondence|Population Based Correspondence]] ==

We are developing methodology to automatically find dense point correspondences between a collection of polygonal genus 0 meshes. The advantage of this method is independence from indivisual templates, as well as enhanced modeling properties. The method is based on minimizing a cost function that describes the goodness of correspondence. Apart from a cost function derived from the description length of the model, we also employ a cost function working with arbitrary local features. We extended the original methods to use surface curvature measurements, which are independent to differences of object aligment. [[Projects:PopulationBasedCorrespondence|More...]]

* Styner M., Oguz I., Heimann T., Gerig G. Minimum description length with local geometry. Proceedings of the 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro 2008; 1283-1286
* Software available as part of UNC Neurolib open source ([http://www.ia.unc.edu/dev website])

|-

|}

DBP3:Iowa

2011-11-21T21:31:58Z

Cvachet: /* DTI Processing */

Back to [[DBP3:Main|NA-MIC DBPs]] | [[Cores|NA-MIC Cores]]

Engineering Action Plan

=== Overview of the project ===
* http://www.na-mic.org/pages/DBP:HD
* [[2011_Summer_Project_Week_Iowa_Huntington%27s_disease_data_sharing | DBP Data description]]
* [[DBP:HD_Data_Collaborators | Projects using DBP:HD Data]]

=== In Progress / Completed (Reverse Chronological) ===

* (Complete: Fall 2011) DTI noise estimation, Rician noise filtering
* (Complete: Fall 2011) DTI pairwise registration (new Slicer module)
* (In Progress: Fall 2011) SPHARM-PDM Slicer3 integration/dissemination
* (In Progress: Fall 2011) Identified a bug in Mattes mutual information metric related to multithreading the was affecting BRAINSFit. Working with the ITK community to correct it. This has been worked around in BRAINS Tools by forcing MI registration metric to be single threaded until the ITK community can correct it.
* (Complete: Summer 2011) DTI estimation, property map generation
* (Complete: Summer 2011) DTIPrep improvement
* (Complete: Spring 2011) Write Tutorial for DTIPrep
* (Complete: Spring 2011) Re-deidentify data subset and post to XNAT for NA-MIC purposes. Identified data and we are actively working on creating the data set as of March 2011.
* (Complete: Spring 2011) Process data from raw DICOM data (enhanced DicomToNRRDConverter)
* (Complete: March 2011) Fixed BRAINSTools errors on Windows
* (Complete: Feb 2011) Moved all BRAINS testing data to midas.kitware.com and updated the tests to pull data from there.
* (Complete: Feb 2011) Final approval from the Predict Steering Committee to share data.
* (Complete: Jan 2011) Add testing data suite to DicomToNRRDConverter
* (Complete: Fall 2010) Over 2900 scan sessions collected.

=== Morphometric Processing ===
Goals: Have all the documentation and code in place for BRAINS3 to easily be used by the community.
* 2011-02-15 (Mark Scully): Prepare the BRAINS3 Toolkit for public use
** Improve the consistency of the command line argument processing
*** Create an XML formal Schema from the SEM model examples
*** Enhance the SEM schema with other optional tags needed for improved documentation
*** Make SEM into it's own External_Add project to ease use by other packages
*** Move internal documentation from https://www.icts.uiowa.edu/confluence/display/BRAINS/Command+line+argument+rules, to the SEM pages section
** (Hans Johnson) Improve merging strategies between BRAINS3Tools that are part of BRAINS3 and BRAINS3Tools that are part of Slicer3. A combination of git svn should ease the integration of these two platforms. -- This is not as easy as expected, and it is probably not a good use of our time to make this more automated. It will continue to be done manually.
* 2012-01-15 (Mark Scully): Create documentation for a standard morphometric analysis
* 2013-01-08 (Mark Scully): Create tutorial for full morphometric analysis suitable for basing a DWI based analysis on.

=== DTI Processing ===
Goals: Tools for a longitudinal analysis pipeline of changes measured by fiber tractography to identify white matter tracts that have strong co-morbid degenerative timelines compared to subcortical degeneration over time. Enable the use of the advanced DTI processing in NA-MIC within PREDICT HD. Specifically, adapt all NA-MIC tools to work as individual external Slicer modules (in part they already exist, but need further work), as well as combine them into a single Slicer DTI processing wizard.
* 2010-12-15 (Mark, Joy, UNC Team):
** Word doc Tutorial updated with visual inspection info
** Create Media Wiki version of documentation
** Create PPT presentation for DTIPrep.
* Process data from raw DICOM data (enhanced DicomToNRRDConverter)
** 2010-11-30 (Mark, Joy): Add testing data suite to DicomToNRRDConverter (Joy to identify, Mark to implement)
** Target 2010-12-31(Mark): CMake URL data download mechanisms (Look at Titan CMake has examples of this).
** 2010-12-31 (Clement) DicomToNRRDConverter use of B-Matrix for vb13 data DTI_THP Iowa data.
* Target 2011-05-01 (Revised: 2011-10-01) (Clement/Mahshid): DTI noise estimation, Rician noise filtering
** Review status of code to determine how to modularize into stand alone library and executable
** Include directly into DTIprep:
*** Optional Rician LMMSE image filtering - preprocessing
*** Optional Joint Rician LMMSE image filtering - before DTI creation
* 2011-06-24 (Clement/Mahshid): DTI QC and motion/eddy current correction (enhanced DTIPrep)
** Reporting standardization for easier report generation. Use of XML, extended reporting.
** Loading reports so that batch processing mode and gui processing mode allow the same user
** GUI should allow for manual individual rejection of DWI gradients
* 2011-12-31 (Clement/Mark/Mahshid): DTI estimation, property map generation (existing Slicer modules)
** Review and make command line interfaces and make a dictionary of common command line arguments across the tool sets.
** Push DTIProcess tools into full Slicer Modules
* 2011-08-15 (Revised: 2011-10-15) (Clement): DTI pairwise registration (new Slicer module)
** Develop pairwise registration module allowing:
*** Scalar feature (e.g FA) to drive the registration of a DTI image
*** Optional: Direct registration of the DTI image? (DTITK)
** Slicer3-compatible module currently available on NITRC: [http://www.nitrc.org/projects/dtireg/ DTI-Reg]
*** Registration/Warping available via BRAINS (BRAINSFit/BRAINSDemonWarp) and via ANTS (Advanced Normalization Tools)
** Investigate if both scalars and higher models can both be supported.
** Optional: Investigate DTITK inclusion Slicer3
* 2012-05-01 (Clement): DTI atlas based fiber analysis (new Slicer module)
** Tools need re-writing to make them slicer3 compatible.
* 2012-07-01 (Clement): DTI atlas computation module (new Slicer module)
** NITRC implementation FRAT needs rewrite, alternative would be open source AtlasWorks
* 2012-09-01 (Clement): DWI atlas mapping tool (new Slicer module)
** Need to move to NITRC and convert to Slicer3
* 2013-01-01 (Clement/Mark/Jeff/All): Appropriate reporting of all steps
** Reporting of tool status. Track the OpenProvenance project.
** Use XML so that we can convert of OpenProvenance latter if desired.
* 2013-08-30 (Mark): DTI processing wizard in Slicer
* 2013-08-30 (Clement): Longitudinal DTI analysis module
** Create processing pipeline for batch processing a large number of data sets
* 2013-08-30 (Steve Pieper/Kitware/Core 2 involvement): Fiber profile measurement/visualization within Slicer

=== Shape Analysis ===
Goals: Tools for a longitudinal shape analysis pipeline to identify localized changes in basal ganglia tracts that have strong co-morbid degenerative timelines. Enable the use of the advanced SPHARM & particle shape analysis processing in NA-MIC within PREDICT HD.
* 2011-04-01 (Slicer Team/Core2):Sun Grid Engine compatibility
* 2011-04-01 (Clement): Clean up existing tools for deployment at Iowa
** Individual SPHARM tools
** SPHARM shape summary tool
** Statistical Shape Analysis Tools
*** CSV files as part of Slicer standards (Paths, variables, group associations, etc)
* 2012-01-01 (Mark): Analyze 225 subjects with Cross sectional tools
** With between 3-6 longitudinally collected scans.
** With Caudate/Putamen/Thalamus
* 2012-08-01 (Clement/Bea): Add support for particle shape analysis as part of shape pipeline
** Target of 2011-01-14: have feasibility shape analysis done to identify outstanding work that needs to be done.

=== BatchMake Processing and Grid Wizard ===
Goals: Create a distributed batch processing pipeline that pulls data from and reports data to XNAT while distributing jobs across a cluster.
* 2011-01-14 (Marco): Contact BatchMake developers and determine what needs to be done in order to distribute computational load for the analysis of this project.
** 2011-06-24 (Marco): Distribute from local data repositories.
** 2012-06-24 (Marco/Kevin Archie/Tim Olsen): Distribute from XNAT
** 2011-01-14 (Marco/Kevin Archie): Define mechanism for deploying distributed computations with GridWizard as a backend tool that the end-user never sees.

=== Data ===
Goals: Share a meaningful, fully anonymized subset of the sMRI, fMRI, and DWI Huntington's data.
* (Target date of 2010-12-10) Identified the FMRI_024 data (77 subjects, 2-3 years longitudinal) as a good candidate data set for collaborative algorithm development platform. This data set has 3 71direction+8B0 DTI data sets, 2 1.0^3 T1 data sets, and a 0.56x0.56x1.4 T2 data set.
** Will likely need to collect clinical data for shape analysis work that includes: (Age, Gender, Dx, Burdon Score, Motor Score).
** Will need to re-de-identify all the data to be used here.
** Will need to run auto-workup for generating Caudate/putamen/hippocampus/thalmus masks.
** Data exchange will be done through XNAT. including derived data.
* (Target date of 2011-01-14): Share all DTI_THP to wide NAMIC group for validation of tools being developed.

=== Outreach ===
Goals: Inform community of the existence of tools and data sets as well as train them on their use.
*Tutorial(s) on the wiki
*Presentations at HBM, Euro-HD.net, HDSA (The Huntington's Disease Society of America) in year 2
*Hands-on teaching event for the DBP scientific community for year 3

===Who===
*DBP: Hans Johnson, DBP engineer Mark Scully
*Algo: Martin Styner, DBP engineer Clement Vachet, Mahshid Farzinfar (DTIPrep), Beatriz Paniagua (Shape)
*Eng: Dan Marcus, Jeff Grethe, Marco Ruiz

DBP3:Iowa

2011-11-21T21:29:31Z

Cvachet: /* In Progress / Completed (Reverse Chronological) */

Back to [[DBP3:Main|NA-MIC DBPs]] | [[Cores|NA-MIC Cores]]

Engineering Action Plan

=== Overview of the project ===
* http://www.na-mic.org/pages/DBP:HD
* [[2011_Summer_Project_Week_Iowa_Huntington%27s_disease_data_sharing | DBP Data description]]
* [[DBP:HD_Data_Collaborators | Projects using DBP:HD Data]]

=== In Progress / Completed (Reverse Chronological) ===

* (Complete: Fall 2011) DTI noise estimation, Rician noise filtering
* (Complete: Fall 2011) DTI pairwise registration (new Slicer module)
* (In Progress: Fall 2011) SPHARM-PDM Slicer3 integration/dissemination
* (In Progress: Fall 2011) Identified a bug in Mattes mutual information metric related to multithreading the was affecting BRAINSFit. Working with the ITK community to correct it. This has been worked around in BRAINS Tools by forcing MI registration metric to be single threaded until the ITK community can correct it.
* (Complete: Summer 2011) DTI estimation, property map generation
* (Complete: Summer 2011) DTIPrep improvement
* (Complete: Spring 2011) Write Tutorial for DTIPrep
* (Complete: Spring 2011) Re-deidentify data subset and post to XNAT for NA-MIC purposes. Identified data and we are actively working on creating the data set as of March 2011.
* (Complete: Spring 2011) Process data from raw DICOM data (enhanced DicomToNRRDConverter)
* (Complete: March 2011) Fixed BRAINSTools errors on Windows
* (Complete: Feb 2011) Moved all BRAINS testing data to midas.kitware.com and updated the tests to pull data from there.
* (Complete: Feb 2011) Final approval from the Predict Steering Committee to share data.
* (Complete: Jan 2011) Add testing data suite to DicomToNRRDConverter
* (Complete: Fall 2010) Over 2900 scan sessions collected.

=== Morphometric Processing ===
Goals: Have all the documentation and code in place for BRAINS3 to easily be used by the community.
* 2011-02-15 (Mark Scully): Prepare the BRAINS3 Toolkit for public use
** Improve the consistency of the command line argument processing
*** Create an XML formal Schema from the SEM model examples
*** Enhance the SEM schema with other optional tags needed for improved documentation
*** Make SEM into it's own External_Add project to ease use by other packages
*** Move internal documentation from https://www.icts.uiowa.edu/confluence/display/BRAINS/Command+line+argument+rules, to the SEM pages section
** (Hans Johnson) Improve merging strategies between BRAINS3Tools that are part of BRAINS3 and BRAINS3Tools that are part of Slicer3. A combination of git svn should ease the integration of these two platforms. -- This is not as easy as expected, and it is probably not a good use of our time to make this more automated. It will continue to be done manually.
* 2012-01-15 (Mark Scully): Create documentation for a standard morphometric analysis
* 2013-01-08 (Mark Scully): Create tutorial for full morphometric analysis suitable for basing a DWI based analysis on.

=== DTI Processing ===
Goals: Tools for a longitudinal analysis pipeline of changes measured by fiber tractography to identify white matter tracts that have strong co-morbid degenerative timelines compared to subcortical degeneration over time. Enable the use of the advanced DTI processing in NA-MIC within PREDICT HD. Specifically, adapt all NA-MIC tools to work as individual external Slicer modules (in part they already exist, but need further work), as well as combine them into a single Slicer DTI processing wizard.
* 2010-12-15 (Mark, Joy, UNC Team):
** Word doc Tutorial updated with visual inspection info
** Create Media Wiki version of documentation
** Create PPT presentation for DTIPrep.
* Process data from raw DICOM data (enhanced DicomToNRRDConverter)
** 2010-11-30 (Mark, Joy): Add testing data suite to DicomToNRRDConverter (Joy to identify, Mark to implement)
** Target 2010-12-31(Mark): CMake URL data download mechanisms (Look at Titan CMake has examples of this).
** 2010-12-31 (Clement) DicomToNRRDConverter use of B-Matrix for vb13 data DTI_THP Iowa data.
* Target 2011-05-01 (Revised: 2011-10-01) (Clement/Mahshid): DTI noise estimation, Rician noise filtering
** Review status of code to determine how to modularize into stand alone library and executable
** Include directly into DTIprep:
*** Optional Rician LMMSE image filtering - preprocessing
*** Optional Joint Rician LMMSE image filtering - before DTI creation
* 2011-06-24 (Clement/Mahshid): DTI QC and motion/eddy current correction (enhanced DTIPrep)
** Reporting standardization for easier report generation. Use of XML, extended reporting.
** Loading reports so that batch processing mode and gui processing mode allow the same user
** GUI should allow for manual individual rejection of DWI gradients
* 2011-12-31 (Clement/Mark/Mahshid): DTI estimation, property map generation (existing Slicer modules)
** Review and make command line interfaces and make a dictionary of common command line arguments across the tool sets.
** Push DTIProcess tools into full Slicer Modules
* 2011-08-15 (Revised: 2011-10-15) (Clement): DTI pairwise registration (new Slicer module)
** Develop pairwise registration module allowing:
*** Scalar feature (e.g FA) to drive the registration of a DTI image
*** Direct registration of the DTI image (DTITK?)
** First release of Slicer3-compatible module currently available on NITRC: [http://www.nitrc.org/projects/dtireg/ DTI-Reg]
** Investigate if both scalars and higher models can both be supported.
** Investigate DTITK inclusion Slicer3
* 2012-05-01 (Clement): DTI atlas based fiber analysis (new Slicer module)
** Tools need re-writing to make them slicer3 compatible.
* 2012-07-01 (Clement): DTI atlas computation module (new Slicer module)
** NITRC implementation FRAT needs rewrite, alternative would be open source AtlasWorks
* 2012-09-01 (Clement): DWI atlas mapping tool (new Slicer module)
** Need to move to NITRC and convert to Slicer3
* 2013-01-01 (Clement/Mark/Jeff/All): Appropriate reporting of all steps
** Reporting of tool status. Track the OpenProvenance project.
** Use XML so that we can convert of OpenProvenance latter if desired.
* 2013-08-30 (Mark): DTI processing wizard in Slicer
* 2013-08-30 (Clement): Longitudinal DTI analysis module
** Create processing pipeline for batch processing a large number of data sets
* 2013-08-30 (Steve Pieper/Kitware/Core 2 involvement): Fiber profile measurement/visualization within Slicer

=== Shape Analysis ===
Goals: Tools for a longitudinal shape analysis pipeline to identify localized changes in basal ganglia tracts that have strong co-morbid degenerative timelines. Enable the use of the advanced SPHARM & particle shape analysis processing in NA-MIC within PREDICT HD.
* 2011-04-01 (Slicer Team/Core2):Sun Grid Engine compatibility
* 2011-04-01 (Clement): Clean up existing tools for deployment at Iowa
** Individual SPHARM tools
** SPHARM shape summary tool
** Statistical Shape Analysis Tools
*** CSV files as part of Slicer standards (Paths, variables, group associations, etc)
* 2012-01-01 (Mark): Analyze 225 subjects with Cross sectional tools
** With between 3-6 longitudinally collected scans.
** With Caudate/Putamen/Thalamus
* 2012-08-01 (Clement/Bea): Add support for particle shape analysis as part of shape pipeline
** Target of 2011-01-14: have feasibility shape analysis done to identify outstanding work that needs to be done.

=== BatchMake Processing and Grid Wizard ===
Goals: Create a distributed batch processing pipeline that pulls data from and reports data to XNAT while distributing jobs across a cluster.
* 2011-01-14 (Marco): Contact BatchMake developers and determine what needs to be done in order to distribute computational load for the analysis of this project.
** 2011-06-24 (Marco): Distribute from local data repositories.
** 2012-06-24 (Marco/Kevin Archie/Tim Olsen): Distribute from XNAT
** 2011-01-14 (Marco/Kevin Archie): Define mechanism for deploying distributed computations with GridWizard as a backend tool that the end-user never sees.

=== Data ===
Goals: Share a meaningful, fully anonymized subset of the sMRI, fMRI, and DWI Huntington's data.
* (Target date of 2010-12-10) Identified the FMRI_024 data (77 subjects, 2-3 years longitudinal) as a good candidate data set for collaborative algorithm development platform. This data set has 3 71direction+8B0 DTI data sets, 2 1.0^3 T1 data sets, and a 0.56x0.56x1.4 T2 data set.
** Will likely need to collect clinical data for shape analysis work that includes: (Age, Gender, Dx, Burdon Score, Motor Score).
** Will need to re-de-identify all the data to be used here.
** Will need to run auto-workup for generating Caudate/putamen/hippocampus/thalmus masks.
** Data exchange will be done through XNAT. including derived data.
* (Target date of 2011-01-14): Share all DTI_THP to wide NAMIC group for validation of tools being developed.

=== Outreach ===
Goals: Inform community of the existence of tools and data sets as well as train them on their use.
*Tutorial(s) on the wiki
*Presentations at HBM, Euro-HD.net, HDSA (The Huntington's Disease Society of America) in year 2
*Hands-on teaching event for the DBP scientific community for year 3

===Who===
*DBP: Hans Johnson, DBP engineer Mark Scully
*Algo: Martin Styner, DBP engineer Clement Vachet, Mahshid Farzinfar (DTIPrep), Beatriz Paniagua (Shape)
*Eng: Dan Marcus, Jeff Grethe, Marco Ruiz

DBP3:Iowa

2011-11-16T19:24:13Z

Cvachet: /* DTI Processing */

Back to [[DBP3:Main|NA-MIC DBPs]] | [[Cores|NA-MIC Cores]]

Engineering Action Plan

=== Overview of the project ===
* http://www.na-mic.org/pages/DBP:HD
* [[2011_Summer_Project_Week_Iowa_Huntington%27s_disease_data_sharing | DBP Data description]]
* [[DBP:HD_Data_Collaborators | Projects using DBP:HD Data]]

=== In Progress / Completed (Reverse Chronological) ===

* (Complete: Fall 2011) DTI noise estimation, Rician noise filtering
* (In Progress: Fall 2011) DTI pairwise registration (new Slicer module)
* (In Progress: Fall 2011) SPHARM-PDM Slicer3 integration/dissemination
* (In Progress: Fall 2011) Identified a bug in Mattes mutual information metric related to multithreading the was affecting BRAINSFit. Working with the ITK community to correct it. This has been worked around in BRAINS Tools by forcing MI registration metric to be single threaded until the ITK community can correct it.
* (Complete: Summer 2011) DTI estimation, property map generation
* (Complete: Summer 2011) DTIPrep improvement
* (Complete: Spring 2011) Write Tutorial for DTIPrep
* (Complete: Spring 2011) Re-deidentify data subset and post to XNAT for NA-MIC purposes. Identified data and we are actively working on creating the data set as of March 2011.
* (Complete: Spring 2011) Process data from raw DICOM data (enhanced DicomToNRRDConverter)
* (Complete: March 2011) Fixed BRAINSTools errors on Windows
* (Complete: Feb 2011) Moved all BRAINS testing data to midas.kitware.com and updated the tests to pull data from there.
* (Complete: Feb 2011) Final approval from the Predict Steering Committee to share data.
* (Complete: Jan 2011) Add testing data suite to DicomToNRRDConverter
* (Complete: Fall 2010) Over 2900 scan sessions collected.

=== Morphometric Processing ===
Goals: Have all the documentation and code in place for BRAINS3 to easily be used by the community.
* 2011-02-15 (Mark Scully): Prepare the BRAINS3 Toolkit for public use
** Improve the consistency of the command line argument processing
*** Create an XML formal Schema from the SEM model examples
*** Enhance the SEM schema with other optional tags needed for improved documentation
*** Make SEM into it's own External_Add project to ease use by other packages
*** Move internal documentation from https://www.icts.uiowa.edu/confluence/display/BRAINS/Command+line+argument+rules, to the SEM pages section
** (Hans Johnson) Improve merging strategies between BRAINS3Tools that are part of BRAINS3 and BRAINS3Tools that are part of Slicer3. A combination of git svn should ease the integration of these two platforms. -- This is not as easy as expected, and it is probably not a good use of our time to make this more automated. It will continue to be done manually.
* 2012-01-15 (Mark Scully): Create documentation for a standard morphometric analysis
* 2013-01-08 (Mark Scully): Create tutorial for full morphometric analysis suitable for basing a DWI based analysis on.

=== DTI Processing ===
Goals: Tools for a longitudinal analysis pipeline of changes measured by fiber tractography to identify white matter tracts that have strong co-morbid degenerative timelines compared to subcortical degeneration over time. Enable the use of the advanced DTI processing in NA-MIC within PREDICT HD. Specifically, adapt all NA-MIC tools to work as individual external Slicer modules (in part they already exist, but need further work), as well as combine them into a single Slicer DTI processing wizard.
* 2010-12-15 (Mark, Joy, UNC Team):
** Word doc Tutorial updated with visual inspection info
** Create Media Wiki version of documentation
** Create PPT presentation for DTIPrep.
* Process data from raw DICOM data (enhanced DicomToNRRDConverter)
** 2010-11-30 (Mark, Joy): Add testing data suite to DicomToNRRDConverter (Joy to identify, Mark to implement)
** Target 2010-12-31(Mark): CMake URL data download mechanisms (Look at Titan CMake has examples of this).
** 2010-12-31 (Clement) DicomToNRRDConverter use of B-Matrix for vb13 data DTI_THP Iowa data.
* Target 2011-05-01 (Revised: 2011-10-01) (Clement/Mahshid): DTI noise estimation, Rician noise filtering
** Review status of code to determine how to modularize into stand alone library and executable
** Include directly into DTIprep:
*** Optional Rician LMMSE image filtering - preprocessing
*** Optional Joint Rician LMMSE image filtering - before DTI creation
* 2011-06-24 (Clement/Mahshid): DTI QC and motion/eddy current correction (enhanced DTIPrep)
** Reporting standardization for easier report generation. Use of XML, extended reporting.
** Loading reports so that batch processing mode and gui processing mode allow the same user
** GUI should allow for manual individual rejection of DWI gradients
* 2011-12-31 (Clement/Mark/Mahshid): DTI estimation, property map generation (existing Slicer modules)
** Review and make command line interfaces and make a dictionary of common command line arguments across the tool sets.
** Push DTIProcess tools into full Slicer Modules
* 2011-08-15 (Revised: 2011-10-15) (Clement): DTI pairwise registration (new Slicer module)
** Develop pairwise registration module allowing:
*** Scalar feature (e.g FA) to drive the registration of a DTI image
*** Direct registration of the DTI image (DTITK?)
** First release of Slicer3-compatible module currently available on NITRC: [http://www.nitrc.org/projects/dtireg/ DTI-Reg]
** Investigate if both scalars and higher models can both be supported.
** Investigate DTITK inclusion Slicer3
* 2012-05-01 (Clement): DTI atlas based fiber analysis (new Slicer module)
** Tools need re-writing to make them slicer3 compatible.
* 2012-07-01 (Clement): DTI atlas computation module (new Slicer module)
** NITRC implementation FRAT needs rewrite, alternative would be open source AtlasWorks
* 2012-09-01 (Clement): DWI atlas mapping tool (new Slicer module)
** Need to move to NITRC and convert to Slicer3
* 2013-01-01 (Clement/Mark/Jeff/All): Appropriate reporting of all steps
** Reporting of tool status. Track the OpenProvenance project.
** Use XML so that we can convert of OpenProvenance latter if desired.
* 2013-08-30 (Mark): DTI processing wizard in Slicer
* 2013-08-30 (Clement): Longitudinal DTI analysis module
** Create processing pipeline for batch processing a large number of data sets
* 2013-08-30 (Steve Pieper/Kitware/Core 2 involvement): Fiber profile measurement/visualization within Slicer

=== Shape Analysis ===
Goals: Tools for a longitudinal shape analysis pipeline to identify localized changes in basal ganglia tracts that have strong co-morbid degenerative timelines. Enable the use of the advanced SPHARM & particle shape analysis processing in NA-MIC within PREDICT HD.
* 2011-04-01 (Slicer Team/Core2):Sun Grid Engine compatibility
* 2011-04-01 (Clement): Clean up existing tools for deployment at Iowa
** Individual SPHARM tools
** SPHARM shape summary tool
** Statistical Shape Analysis Tools
*** CSV files as part of Slicer standards (Paths, variables, group associations, etc)
* 2012-01-01 (Mark): Analyze 225 subjects with Cross sectional tools
** With between 3-6 longitudinally collected scans.
** With Caudate/Putamen/Thalamus
* 2012-08-01 (Clement/Bea): Add support for particle shape analysis as part of shape pipeline
** Target of 2011-01-14: have feasibility shape analysis done to identify outstanding work that needs to be done.

=== BatchMake Processing and Grid Wizard ===
Goals: Create a distributed batch processing pipeline that pulls data from and reports data to XNAT while distributing jobs across a cluster.
* 2011-01-14 (Marco): Contact BatchMake developers and determine what needs to be done in order to distribute computational load for the analysis of this project.
** 2011-06-24 (Marco): Distribute from local data repositories.
** 2012-06-24 (Marco/Kevin Archie/Tim Olsen): Distribute from XNAT
** 2011-01-14 (Marco/Kevin Archie): Define mechanism for deploying distributed computations with GridWizard as a backend tool that the end-user never sees.

=== Data ===
Goals: Share a meaningful, fully anonymized subset of the sMRI, fMRI, and DWI Huntington's data.
* (Target date of 2010-12-10) Identified the FMRI_024 data (77 subjects, 2-3 years longitudinal) as a good candidate data set for collaborative algorithm development platform. This data set has 3 71direction+8B0 DTI data sets, 2 1.0^3 T1 data sets, and a 0.56x0.56x1.4 T2 data set.
** Will likely need to collect clinical data for shape analysis work that includes: (Age, Gender, Dx, Burdon Score, Motor Score).
** Will need to re-de-identify all the data to be used here.
** Will need to run auto-workup for generating Caudate/putamen/hippocampus/thalmus masks.
** Data exchange will be done through XNAT. including derived data.
* (Target date of 2011-01-14): Share all DTI_THP to wide NAMIC group for validation of tools being developed.

=== Outreach ===
Goals: Inform community of the existence of tools and data sets as well as train them on their use.
*Tutorial(s) on the wiki
*Presentations at HBM, Euro-HD.net, HDSA (The Huntington's Disease Society of America) in year 2
*Hands-on teaching event for the DBP scientific community for year 3

===Who===
*DBP: Hans Johnson, DBP engineer Mark Scully
*Algo: Martin Styner, DBP engineer Clement Vachet, Mahshid Farzinfar (DTIPrep), Beatriz Paniagua (Shape)
*Eng: Dan Marcus, Jeff Grethe, Marco Ruiz

2012 Winter Project Week:FVLight

2011-11-16T18:25:36Z

Cvachet:

__NOTOC__
<gallery>
Image:PW-SLC2012.png|[[2012_Winter_Project_Week#Projects|Projects List]]
Image:FV1.jpg|FiberViewerLight - Top Screen
Image:FV2.jpg|FiberViewerLight - Length Screen with a plane
</gallery>

==Key Investigators==
* UNC: Jean-Baptiste Berger, Clement Vachet, Martin Styner

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>
The main objective is to develop a light version of the existing tool FiberViewer. We will focus on the clustering part of Fiber Viewer such as Length, Gravity, Hausdorff, and Mean methods, but also a Normalized Cut algorithm. FiberViewerLight also provides 3D fibers visualization and 3D plane selection, for future FA analysis along fibers.

</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>
Our plan for the project week is to continue FiberVieweLight's development and improve its computing speed since it is a light version.

</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>

</div>
</div>

<div style="width: 97%; float: left;">

==Delivery Mechanism==

This work will be delivered to the NA-MIC Kit as a

#Slicer Module
##Extension -- commandline
##Extension -- loadable

==References==

</div>

2012 Winter Project Week:DTIAFA

2011-11-16T18:25:19Z

Cvachet:

__NOTOC__
<gallery>
Image:PW-SLC2012.png|[[2012_Winter_Project_Week#Projects|Projects List]]
Image:DTIAFA1.jpg|DTIAtlasFiberAnalyzer - Top Screen
Image:DTIAFA2.jpg|DTIAtlasFiberAnalyzer - Plot Window
</gallery>

==Key Investigators==
* UNC: Jean-Baptiste Berger, Benjamin Yvernault, Yundi Shi, Clement Vachet, Martin Styner

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>
DTIAtlasFiberAnalyzer aims to map atlas fiber bundles to individual subjects, extract corresponding fiber profiles, gather and plot all related information (average FA map along fibers...). DTIAtlasFiberAnalyzer calls external applications, corresponding to individual steps or the framework, such as: dtitractstat, fiberprocess, and mergestatwithfiber. Once information are computed, each merged statistics on the fiber bundle can be displayed on 3D Slicer.

</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>

Our plan for the project week is to test this tool on small datasets and meet with interested parties.

</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>

</div>
</div>

<div style="width: 97%; float: left;">

==Delivery Mechanism==

This work will be delivered to the NA-MIC Kit as a

#Slicer Module
##Extension -- commandline
##Extension -- loadable

==References==

</div>

2012 Winter Project Week:DTIAFA

2011-11-16T18:24:31Z

Cvachet:

__NOTOC__
<gallery>
Image:PW-SLC2011.png|[[2011_Winter_Project_Week#Projects|Projects List]]
Image:DTIAFA1.jpg|DTIAtlasFiberAnalyzer - Top Screen
Image:DTIAFA2.jpg|DTIAtlasFiberAnalyzer - Plot Window
</gallery>

==Key Investigators==
* UNC: Jean-Baptiste Berger, Benjamin Yvernault, Yundi Shi, Clement Vachet, Martin Styner

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>
DTIAtlasFiberAnalyzer aims to map atlas fiber bundles to individual subjects, extract corresponding fiber profiles, gather and plot all related information (average FA map along fibers...). DTIAtlasFiberAnalyzer calls external applications, corresponding to individual steps or the framework, such as: dtitractstat, fiberprocess, and mergestatwithfiber. Once information are computed, each merged statistics on the fiber bundle can be displayed on 3D Slicer.

</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>

Our plan for the project week is to test this tool on small datasets and meet with interested parties.

</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>

</div>
</div>

<div style="width: 97%; float: left;">

==Delivery Mechanism==

This work will be delivered to the NA-MIC Kit as a

#Slicer Module
##Extension -- commandline
##Extension -- loadable

==References==

</div>

2012 Winter Project Week:DTIAFA

2011-11-16T18:23:19Z

Cvachet:

__NOTOC__
<gallery>
Image:PW-SLC2011.png|[[2011_Winter_Project_Week#Projects|Projects List]]
Image:DTIAFA1.jpg|DTIAtlasFiberAnalyzer - Top Screen
Image:DTIAFA2.jpg|DTIAtlasFiberAnalyzer - Plot Window
</gallery>

==Key Investigators==
* UNC: Jean-Baptiste Berger, Yundi Shi, Clement Vachet, Martin Styner

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>
DTIAtlasFiberAnalyzer aims to map atlas fiber bundles to individual subjects, extract corresponding fiber profiles, gather and plot all related information (average FA map along fibers...). DTIAtlasFiberAnalyzer calls external applications, corresponding to individual steps or the framework, such as: dtitractstat, fiberprocess, and mergestatwithfiber. Once information are computed, each merged statistics on the fiber bundle can be displayed on 3D Slicer.

</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>

Our plan for the project week is to test this tool on small datasets and meet with interested parties.

</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>

</div>
</div>

<div style="width: 97%; float: left;">

==Delivery Mechanism==

This work will be delivered to the NA-MIC Kit as a

#Slicer Module
##Extension -- commandline
##Extension -- loadable

==References==

</div>

2012 Winter Project Week:DTIAFA

2011-11-16T18:22:53Z

Cvachet: /* Key Investigators */

__NOTOC__
<gallery>
Image:PW-SLC2011.png|[[2011_Winter_Project_Week#Projects|Projects List]]
Image:DTIAFA1.jpg|DTIAtlasFiberAnalyzer - Top Screen
Image:DTIAFA2.jpg|DTIAtlasFiberAnalyzer - Plot Window
</gallery>

==Key Investigators==
* UNC: Jean-Baptiste Berger, Jean-Baptiste Berger, Yundi Shi, Clement Vachet, Martin Styner

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>
DTIAtlasFiberAnalyzer aims to map atlas fiber bundles to individual subjects, extract corresponding fiber profiles, gather and plot all related information (average FA map along fibers...). DTIAtlasFiberAnalyzer calls external applications, corresponding to individual steps or the framework, such as: dtitractstat, fiberprocess, and mergestatwithfiber. Once information are computed, each merged statistics on the fiber bundle can be displayed on 3D Slicer.

</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>

Our plan for the project week is to test this tool on small datasets and meet with interested parties.

</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>

</div>
</div>

<div style="width: 97%; float: left;">

==Delivery Mechanism==

This work will be delivered to the NA-MIC Kit as a

#Slicer Module
##Extension -- commandline
##Extension -- loadable

==References==

</div>

2012 Winter Project Week:DTIAFA

2011-11-16T18:22:23Z

Cvachet:

__NOTOC__
<gallery>
Image:PW-SLC2011.png|[[2011_Winter_Project_Week#Projects|Projects List]]
Image:DTIAFA1.jpg|DTIAtlasFiberAnalyzer - Top Screen
Image:DTIAFA2.jpg|DTIAtlasFiberAnalyzer - Plot Window
</gallery>

==Key Investigators==
* UNC: Jean-Baptiste Berger, Jean-Baptiste Berger, Yundi Shi, Clement Vachet, Martin Styner

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>
DTIAtlasFiberAnalyzer aims to map atlas fiber bundles to individual subjects, extract corresponding fiber profiles, gather and plot all related information (average FA map along fibers...). DTIAtlasFiberAnalyzer calls external applications, corresponding to individual steps or the frwamework, such as: dtitractstat, fiberprocess, and mergestatwithfiber. Once information are computed, each merged statistics on the fiber bundle can be displayed on 3D Slicer.

</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>

Our plan for the project week is to test this tool on specific datasets and meet with interested parties.

</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>

</div>
</div>

<div style="width: 97%; float: left;">

==Delivery Mechanism==

This work will be delivered to the NA-MIC Kit as a

#Slicer Module
##Extension -- commandline
##Extension -- loadable

==References==

</div>

2012 Winter Project Week:FVLight

2011-11-16T17:15:16Z

Cvachet:

__NOTOC__
<gallery>
Image:PW-SLC2011.png|[[2011_Winter_Project_Week#Projects|Projects List]]
Image:FV1.jpg|FiberViewerLight - Top Screen
Image:FV2.jpg|FiberViewerLight - Length Screen with a plane
</gallery>

==Key Investigators==
* UNC: Jean-Baptiste Berger, Clement Vachet, Martin Styner

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>
The main objective is to develop a light version of the existing tool FiberViewer. We will focus on the clustering part of Fiber Viewer such as Length, Gravity, Hausdorff, and Mean methods, but also a Normalized Cut algorithm. FiberViewerLight also provides 3D fibers visualization and 3D plane selection, for future FA analysis along fibers.

</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>
Our plan for the project week is to continue FiberVieweLight's development and improve its computing speed since it is a light version.

</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>

</div>
</div>

<div style="width: 97%; float: left;">

==Delivery Mechanism==

This work will be delivered to the NA-MIC Kit as a

#Slicer Module
##Extension -- commandline
##Extension -- loadable

==References==

</div>

2012 Winter Project Week:PairWiseDTIRegistration

2011-11-10T20:11:46Z

Cvachet: /* Key Investigators */

__NOTOC__
<gallery>
Image:PW-SLC2012.png|[[2012_Winter_Project_Week#Projects|Projects List]]
Image:DTI-Reg_UI.jpg| DTI-Reg user interface
</gallery>

==DTI registration/processing pipeline in Slicer3==

==Key Investigators==
* UNC: Clement Vachet, Martin Styner
* IOWA: Mark Scully, Hans Johnson

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>
We focus on enabling the use of advanced DTI processing within NA-MIC and on automatizing steps to perform atlas-based DTI population analysis. One component is pair-wise DTI registration which can be directly performed by DTI-Reg, a C++ application developed in that regard.

</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>

DTI-Reg is an open-source C++ application that performs pair-wise DTI registration.
Individual steps of the pair-wise registration pipeline are performed via external applications, called via BatchMake. Starting with two input DTI images, scalar FA maps are generated via dtiprocess. Registration is then performed between these FA maps, via BRAINSFit or BRAINSDemonWarp, which provide different registration schemes: rigid, affine, BSpline, diffeomorphic, logDemons. The final deformation is then applied to the source DTI image via resampleDTI.

</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>

</div>
</div>

<div style="width: 97%; float: left;">

==Delivery Mechanism==

This work will be delivered to the NA-MIC Kit as a Slicer extension

</div>

2012 Winter Project Week

2011-11-10T19:56:39Z

Cvachet: /* Predict Huntington's Disease DBP */

2012 Winter Project Week

2011-11-10T19:56:08Z

Cvachet: /* Predict Huntington's Disease DBP */

2012 Winter Project Week

2011-11-10T19:45:11Z

Cvachet: /* Predict Huntington's Disease DBP */

2012 Winter Project Week:PairWiseDTIRegistration

2011-11-10T19:43:10Z

Cvachet:

__NOTOC__
<gallery>
Image:PW-SLC2012.png|[[2012_Winter_Project_Week#Projects|Projects List]]
Image:DTI-Reg_UI.jpg| DTI-Reg user interface
</gallery>

==DTI registration/processing pipeline in Slicer3==

==Key Investigators==
* UNC: Clement Vachet, Martin Styner
* IOWA: Mark Scully, Hans Johnson

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>
We focus on enabling the use of advanced DTI processing within NA-MIC and on automatizing steps to perform atlas-based DTI population analysis. One component is pair-wise DTI registration which can be directly performed by DTI-Reg, a C++ application developed in that regard.

</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>

DTI-Reg is an open-source C++ application that performs pair-wise DTI registration.
Individual steps of the pair-wise registration pipeline are performed via external applications. Starting with two input DTI images, scalar FA maps are generated via dtiprocess. Registration is then performed between these FA maps, via BRAINSFit or BRAINSDemonWarp, which provide different registration schemes: rigid, affine, BSpline, diffeomorphic, logDemons. The final deformation is then applied to the source DTI image via resampleDTI.

</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>

</div>
</div>

<div style="width: 97%; float: left;">

==Delivery Mechanism==

This work will be delivered to the NA-MIC Kit as a Slicer extension

</div>

File:DTI-Reg UI.jpg

2011-11-10T19:42:11Z

Cvachet: DTI-Reg user interface - Pairwise DTI registration module

DTI-Reg user interface - Pairwise DTI registration module

2012 Winter Project Week:PairWiseDTIRegistration

2011-11-10T19:31:08Z

Cvachet: Created page with '__NOTOC__ <gallery> Image:PW-SLC2012.png|Projects List </gallery> ==DTI registration/processing pipeline in Slicer3== ==Key Investigator…'

__NOTOC__
<gallery>
Image:PW-SLC2012.png|[[2012_Winter_Project_Week#Projects|Projects List]]
</gallery>

==DTI registration/processing pipeline in Slicer3==

==Key Investigators==
* UNC: Clement Vachet, Martin Styner
* IOWA: Mark Scully, Hans Johnson

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>
We focus on enabling the use of advanced DTI processing within NA-MIC and on automatizing steps to perform atlas-based DTI population analysis. One component is pair-wise DTI registration which can be directly performed by DTI-Reg, a C++ application developed in that regard.

</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>

DTI-Reg is an open-source C++ application that performs pair-wise DTI registration.
Individual steps of the pair-wise registration pipeline are performed via external applications. Starting with two input DTI images, scalar FA maps are generated via dtiprocess. Registration is then performed between these FA maps, via BRAINSFit or BRAINSDemonWarp, which provide different registration schemes: rigid, affine, BSpline, diffeomorphic, logDemons. The final deformation is then applied to the source DTI image via resampleDTI.

</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>

</div>
</div>

<div style="width: 97%; float: left;">

==Delivery Mechanism==

This work will be delivered to the NA-MIC Kit as a Slicer extension

</div>

2012 Winter Project Week

2011-11-10T18:42:46Z

Cvachet: /* Predict Huntington's Disease DBP */

DBP3:Iowa

2011-11-01T20:35:10Z

Cvachet: /* In Progress / Completed (Reverse Chronological) */

Back to [[DBP3:Main|NA-MIC DBPs]] | [[Cores|NA-MIC Cores]]

Engineering Action Plan

=== Overview of the project ===
* http://www.na-mic.org/pages/DBP:HD
* [[2011_Summer_Project_Week_Iowa_Huntington%27s_disease_data_sharing | DBP Data description]]

=== In Progress / Completed (Reverse Chronological) ===

* (Complete: Fall 2011) DTI noise estimation, Rician noise filtering
* (In Progress: Fall 2011) DTI pairwise registration (new Slicer module)
* (In Progress: Fall 2011) SPHARM-PDM Slicer3 integration/dissemination
* (In Progress: Fall 2011) Identified a bug in Mattes mutual information metric related to multithreading the was affecting BRAINSFit. Working with the ITK community to correct it. This has been worked around in BRAINS Tools by forcing MI registration metric to be single threaded until the ITK community can correct it.
* (Complete: Summer 2011) DTI estimation, property map generation
* (Complete: Summer 2011) DTIPrep improvement
* (Complete: Spring 2011) Write Tutorial for DTIPrep
* (Complete: Spring 2011) Re-deidentify data subset and post to XNAT for NA-MIC purposes. Identified data and we are actively working on creating the data set as of March 2011.
* (Complete: Spring 2011) Process data from raw DICOM data (enhanced DicomToNRRDConverter)
* (Complete: March 2011) Fixed BRAINSTools errors on Windows
* (Complete: Feb 2011) Moved all BRAINS testing data to midas.kitware.com and updated the tests to pull data from there.
* (Complete: Feb 2011) Final approval from the Predict Steering Committee to share data.
* (Complete: Jan 2011) Add testing data suite to DicomToNRRDConverter
* (Complete: Fall 2010) Over 2900 scan sessions collected.

=== Morphometric Processing ===
Goals: Have all the documentation and code in place for BRAINS3 to easily be used by the community.
* 2011-02-15 (Mark Scully): Prepare the BRAINS3 Toolkit for public use
** Improve the consistency of the command line argument processing
*** Create an XML formal Schema from the SEM model examples
*** Enhance the SEM schema with other optional tags needed for improved documentation
*** Make SEM into it's own External_Add project to ease use by other packages
*** Move internal documentation from https://www.icts.uiowa.edu/confluence/display/BRAINS/Command+line+argument+rules, to the SEM pages section
** (Hans Johnson) Improve merging strategies between BRAINS3Tools that are part of BRAINS3 and BRAINS3Tools that are part of Slicer3. A combination of git svn should ease the integration of these two platforms. -- This is not as easy as expected, and it is probably not a good use of our time to make this more automated. It will continue to be done manually.
* 2012-01-15 (Mark Scully): Create documentation for a standard morphometric analysis
* 2013-01-08 (Mark Scully): Create tutorial for full morphometric analysis suitable for basing a DWI based analysis on.

=== DTI Processing ===
Goals: Tools for a longitudinal analysis pipeline of changes measured by fiber tractography to identify white matter tracts that have strong co-morbid degenerative timelines compared to subcortical degeneration over time. Enable the use of the advanced DTI processing in NA-MIC within PREDICT HD. Specifically, adapt all NA-MIC tools to work as individual external Slicer modules (in part they already exist, but need further work), as well as combine them into a single Slicer DTI processing wizard.
* 2010-12-15 (Mark, Joy, UNC Team):
** Word doc Tutorial updated with visual inspection info
** Create Media Wiki version of documentation
** Create PPT presentation for DTIPrep.
* Process data from raw DICOM data (enhanced DicomToNRRDConverter)
** 2010-11-30 (Mark, Joy): Add testing data suite to DicomToNRRDConverter (Joy to identify, Mark to implement)
** Target 2010-12-31(Mark): CMake URL data download mechanisms (Look at Titan CMake has examples of this).
** 2010-12-31 (Clement) DicomToNRRDConverter use of B-Matrix for vb13 data DTI_THP Iowa data.
* Target 2011-05-01 (Revised: 2011-10-01) (Clement/Mahshid): DTI noise estimation, Rician noise filtering
** Review status of code to determine how to modularize into stand alone library and executable
** Include directly into DTIprep:
*** Optional Rician LMMSE image filtering - preprocessing
*** Optional Joint Rician LMMSE image filtering - before DTI creation
* 2011-06-24 (Clement/Mahshid): DTI QC and motion/eddy current correction (enhanced DTIPrep)
** Reporting standardization for easier report generation. Use of XML, extended reporting.
** Loading reports so that batch processing mode and gui processing mode allow the same user
** GUI should allow for manual individual rejection of DWI gradients
* 2011-12-31 (Clement/Mark/Mahshid): DTI estimation, property map generation (existing Slicer modules)
** Review and make command line interfaces and make a dictionary of common command line arguments across the tool sets.
** Push DTIProcess tools into full Slicer Modules
* 2011-08-15 (Revised: 2011-10-15) (Clement): DTI pairwise registration (new Slicer module)
** Develop pairwise registration module allowing:
*** Scalar feature (e.g FA) to drive the registration of a DTI image
*** Direct registration of the DTI image (DTITK?)
** First release of Slicer3-compatible module currently available on github: [https://github.com/clementsan/DTI-Reg DTI-Reg]
** Investigate if both scalars and higher models can both be supported.
** Investigate DTITK inclusion Slicer3
* 2012-05-01 (Clement): DTI atlas based fiber analysis (new Slicer module)
** Tools need re-writing to make them slicer3 compatible.
* 2012-07-01 (Clement): DTI atlas computation module (new Slicer module)
** NITRC implementation FRAT needs rewrite, alternative would be open source AtlasWorks
* 2012-09-01 (Clement): DWI atlas mapping tool (new Slicer module)
** Need to move to NITRC and convert to Slicer3
* 2013-01-01 (Clement/Mark/Jeff/All): Appropriate reporting of all steps
** Reporting of tool status. Track the OpenProvenance project.
** Use XML so that we can convert of OpenProvenance latter if desired.
* 2013-08-30 (Mark): DTI processing wizard in Slicer
* 2013-08-30 (Clement): Longitudinal DTI analysis module
** Create processing pipeline for batch processing a large number of data sets
* 2013-08-30 (Steve Pieper/Kitware/Core 2 involvement): Fiber profile measurement/visualization within Slicer

=== Shape Analysis ===
Goals: Tools for a longitudinal shape analysis pipeline to identify localized changes in basal ganglia tracts that have strong co-morbid degenerative timelines. Enable the use of the advanced SPHARM & particle shape analysis processing in NA-MIC within PREDICT HD.
* 2011-04-01 (Slicer Team/Core2):Sun Grid Engine compatibility
* 2011-04-01 (Clement): Clean up existing tools for deployment at Iowa
** Individual SPHARM tools
** SPHARM shape summary tool
** Statistical Shape Analysis Tools
*** CSV files as part of Slicer standards (Paths, variables, group associations, etc)
* 2012-01-01 (Mark): Analyze 225 subjects with Cross sectional tools
** With between 3-6 longitudinally collected scans.
** With Caudate/Putamen/Thalamus
* 2012-08-01 (Clement/Bea): Add support for particle shape analysis as part of shape pipeline
** Target of 2011-01-14: have feasibility shape analysis done to identify outstanding work that needs to be done.

=== BatchMake Processing and Grid Wizard ===
Goals: Create a distributed batch processing pipeline that pulls data from and reports data to XNAT while distributing jobs across a cluster.
* 2011-01-14 (Marco): Contact BatchMake developers and determine what needs to be done in order to distribute computational load for the analysis of this project.
** 2011-06-24 (Marco): Distribute from local data repositories.
** 2012-06-24 (Marco/Kevin Archie/Tim Olsen): Distribute from XNAT
** 2011-01-14 (Marco/Kevin Archie): Define mechanism for deploying distributed computations with GridWizard as a backend tool that the end-user never sees.

=== Data ===
Goals: Share a meaningful, fully anonymized subset of the sMRI, fMRI, and DWI Huntington's data.
* (Target date of 2010-12-10) Identified the FMRI_024 data (77 subjects, 2-3 years longitudinal) as a good candidate data set for collaborative algorithm development platform. This data set has 3 71direction+8B0 DTI data sets, 2 1.0^3 T1 data sets, and a 0.56x0.56x1.4 T2 data set.
** Will likely need to collect clinical data for shape analysis work that includes: (Age, Gender, Dx, Burdon Score, Motor Score).
** Will need to re-de-identify all the data to be used here.
** Will need to run auto-workup for generating Caudate/putamen/hippocampus/thalmus masks.
** Data exchange will be done through XNAT. including derived data.
* (Target date of 2011-01-14): Share all DTI_THP to wide NAMIC group for validation of tools being developed.

=== Outreach ===
Goals: Inform community of the existence of tools and data sets as well as train them on their use.
*Tutorial(s) on the wiki
*Presentations at HBM, Euro-HD.net, HDSA (The Huntington's Disease Society of America) in year 2
*Hands-on teaching event for the DBP scientific community for year 3

===Who===
*DBP: Hans Johnson, DBP engineer Mark Scully
*Algo: Martin Styner, DBP engineer Clement Vachet, Mahshid Farzinfar (DTIPrep), Beatriz Paniagua (Shape)
*Eng: Dan Marcus, Jeff Grethe, Marco Ruiz

DBP3:Iowa

2011-10-19T14:42:36Z

Cvachet: /* DTI Processing */

Back to [[DBP3:Main|NA-MIC DBPs]] | [[Cores|NA-MIC Cores]]

Engineering Action Plan

=== Overview of the project ===
* http://www.na-mic.org/pages/DBP:HD
* [[2011_Summer_Project_Week_Iowa_Huntington%27s_disease_data_sharing | DBP Data description]]

=== In Progress / Completed (Reverse Chronological) ===

* (In Progress: Fall 2011) DTI noise estimation, Rician noise filtering
* (In Progress: Fall 2011) DTI pairwise registration (new Slicer module)
* (In Progress: Fall 2011) SPHARM-PDM Slicer3 integration/dissemination
* (In Progress: Fall 2011) Identified a bug in Mattes mutual information metric related to multithreading the was affecting BRAINSFit. Working with the ITK community to correct it. This has been worked around in BRAINS Tools by forcing MI registration metric to be single threaded until the ITK community can correct it.
* (Complete: Summer 2011) DTI estimation, property map generation
* (Complete: Summer 2011) DTIPrep improvement
* (Complete: Spring 2011) Write Tutorial for DTIPrep
* (Complete: Spring 2011) Re-deidentify data subset and post to XNAT for NA-MIC purposes. Identified data and we are actively working on creating the data set as of March 2011.
* (Complete: Spring 2011) Process data from raw DICOM data (enhanced DicomToNRRDConverter)
* (Complete: March 2011) Fixed BRAINSTools errors on Windows
* (Complete: Feb 2011) Moved all BRAINS testing data to midas.kitware.com and updated the tests to pull data from there.
* (Complete: Feb 2011) Final approval from the Predict Steering Committee to share data.
* (Complete: Jan 2011) Add testing data suite to DicomToNRRDConverter
* (Complete: Fall 2010) Over 2900 scan sessions collected.

=== Morphometric Processing ===
Goals: Have all the documentation and code in place for BRAINS3 to easily be used by the community.
* 2011-02-15 (Mark Scully): Prepare the BRAINS3 Toolkit for public use
** Improve the consistency of the command line argument processing
*** Create an XML formal Schema from the SEM model examples
*** Enhance the SEM schema with other optional tags needed for improved documentation
*** Make SEM into it's own External_Add project to ease use by other packages
*** Move internal documentation from https://www.icts.uiowa.edu/confluence/display/BRAINS/Command+line+argument+rules, to the SEM pages section
** (Hans Johnson) Improve merging strategies between BRAINS3Tools that are part of BRAINS3 and BRAINS3Tools that are part of Slicer3. A combination of git svn should ease the integration of these two platforms. -- This is not as easy as expected, and it is probably not a good use of our time to make this more automated. It will continue to be done manually.
* 2012-01-15 (Mark Scully): Create documentation for a standard morphometric analysis
* 2013-01-08 (Mark Scully): Create tutorial for full morphometric analysis suitable for basing a DWI based analysis on.

=== DTI Processing ===
Goals: Tools for a longitudinal analysis pipeline of changes measured by fiber tractography to identify white matter tracts that have strong co-morbid degenerative timelines compared to subcortical degeneration over time. Enable the use of the advanced DTI processing in NA-MIC within PREDICT HD. Specifically, adapt all NA-MIC tools to work as individual external Slicer modules (in part they already exist, but need further work), as well as combine them into a single Slicer DTI processing wizard.
* 2010-12-15 (Mark, Joy, UNC Team):
** Word doc Tutorial updated with visual inspection info
** Create Media Wiki version of documentation
** Create PPT presentation for DTIPrep.
* Process data from raw DICOM data (enhanced DicomToNRRDConverter)
** 2010-11-30 (Mark, Joy): Add testing data suite to DicomToNRRDConverter (Joy to identify, Mark to implement)
** Target 2010-12-31(Mark): CMake URL data download mechanisms (Look at Titan CMake has examples of this).
** 2010-12-31 (Clement) DicomToNRRDConverter use of B-Matrix for vb13 data DTI_THP Iowa data.
* Target 2011-05-01 (Revised: 2011-10-01) (Clement/Mahshid): DTI noise estimation, Rician noise filtering
** Review status of code to determine how to modularize into stand alone library and executable
** Include directly into DTIprep:
*** Optional Rician LMMSE image filtering - preprocessing
*** Optional Joint Rician LMMSE image filtering - before DTI creation
* 2011-06-24 (Clement/Mahshid): DTI QC and motion/eddy current correction (enhanced DTIPrep)
** Reporting standardization for easier report generation. Use of XML, extended reporting.
** Loading reports so that batch processing mode and gui processing mode allow the same user
** GUI should allow for manual individual rejection of DWI gradients
* 2011-12-31 (Clement/Mark/Mahshid): DTI estimation, property map generation (existing Slicer modules)
** Review and make command line interfaces and make a dictionary of common command line arguments across the tool sets.
** Push DTIProcess tools into full Slicer Modules
* 2011-08-15 (Revised: 2011-10-15) (Clement): DTI pairwise registration (new Slicer module)
** Develop pairwise registration module allowing:
*** Scalar feature (e.g FA) to drive the registration of a DTI image
*** Direct registration of the DTI image (DTITK?)
** First release of Slicer3-compatible module currently available on github: [https://github.com/clementsan/DTI-Reg DTI-Reg]
** Investigate if both scalars and higher models can both be supported.
** Investigate DTITK inclusion Slicer3
* 2012-05-01 (Clement): DTI atlas based fiber analysis (new Slicer module)
** Tools need re-writing to make them slicer3 compatible.
* 2012-07-01 (Clement): DTI atlas computation module (new Slicer module)
** NITRC implementation FRAT needs rewrite, alternative would be open source AtlasWorks
* 2012-09-01 (Clement): DWI atlas mapping tool (new Slicer module)
** Need to move to NITRC and convert to Slicer3
* 2013-01-01 (Clement/Mark/Jeff/All): Appropriate reporting of all steps
** Reporting of tool status. Track the OpenProvenance project.
** Use XML so that we can convert of OpenProvenance latter if desired.
* 2013-08-30 (Mark): DTI processing wizard in Slicer
* 2013-08-30 (Clement): Longitudinal DTI analysis module
** Create processing pipeline for batch processing a large number of data sets
* 2013-08-30 (Steve Pieper/Kitware/Core 2 involvement): Fiber profile measurement/visualization within Slicer

=== Shape Analysis ===
Goals: Tools for a longitudinal shape analysis pipeline to identify localized changes in basal ganglia tracts that have strong co-morbid degenerative timelines. Enable the use of the advanced SPHARM & particle shape analysis processing in NA-MIC within PREDICT HD.
* 2011-04-01 (Slicer Team/Core2):Sun Grid Engine compatibility
* 2011-04-01 (Clement): Clean up existing tools for deployment at Iowa
** Individual SPHARM tools
** SPHARM shape summary tool
** Statistical Shape Analysis Tools
*** CSV files as part of Slicer standards (Paths, variables, group associations, etc)
* 2012-01-01 (Mark): Analyze 225 subjects with Cross sectional tools
** With between 3-6 longitudinally collected scans.
** With Caudate/Putamen/Thalamus
* 2012-08-01 (Clement/Bea): Add support for particle shape analysis as part of shape pipeline
** Target of 2011-01-14: have feasibility shape analysis done to identify outstanding work that needs to be done.

=== BatchMake Processing and Grid Wizard ===
Goals: Create a distributed batch processing pipeline that pulls data from and reports data to XNAT while distributing jobs across a cluster.
* 2011-01-14 (Marco): Contact BatchMake developers and determine what needs to be done in order to distribute computational load for the analysis of this project.
** 2011-06-24 (Marco): Distribute from local data repositories.
** 2012-06-24 (Marco/Kevin Archie/Tim Olsen): Distribute from XNAT
** 2011-01-14 (Marco/Kevin Archie): Define mechanism for deploying distributed computations with GridWizard as a backend tool that the end-user never sees.

=== Data ===
Goals: Share a meaningful, fully anonymized subset of the sMRI, fMRI, and DWI Huntington's data.
* (Target date of 2010-12-10) Identified the FMRI_024 data (77 subjects, 2-3 years longitudinal) as a good candidate data set for collaborative algorithm development platform. This data set has 3 71direction+8B0 DTI data sets, 2 1.0^3 T1 data sets, and a 0.56x0.56x1.4 T2 data set.
** Will likely need to collect clinical data for shape analysis work that includes: (Age, Gender, Dx, Burdon Score, Motor Score).
** Will need to re-de-identify all the data to be used here.
** Will need to run auto-workup for generating Caudate/putamen/hippocampus/thalmus masks.
** Data exchange will be done through XNAT. including derived data.
* (Target date of 2011-01-14): Share all DTI_THP to wide NAMIC group for validation of tools being developed.

=== Outreach ===
Goals: Inform community of the existence of tools and data sets as well as train them on their use.
*Tutorial(s) on the wiki
*Presentations at HBM, Euro-HD.net, HDSA (The Huntington's Disease Society of America) in year 2
*Hands-on teaching event for the DBP scientific community for year 3

===Who===
*DBP: Hans Johnson, DBP engineer Mark Scully
*Algo: Martin Styner, DBP engineer Clement Vachet, Mahshid Farzinfar (DTIPrep), Beatriz Paniagua (Shape)
*Eng: Dan Marcus, Jeff Grethe, Marco Ruiz

DBP3:Iowa

2011-09-01T21:01:52Z

Cvachet: /* DTI Processing */

Engineering Action Plan

=== Overview of the project ===
http://www.na-mic.org/pages/DBP:HD

=== In Progress / Completed (Reverse Chronological) ===

* (In Progress: Fall 2011) DTI noise estimation, Rician noise filtering
* (In Progress: Fall 2011) DTI pairwise registration (new Slicer module)
* (In Progress: Fall 2011) SPHARM-PDM Slicer3 integration/dissemination
* (In Progress: Fall 2011) Identified a bug in Mattes mutual information metric related to multithreading the was affecting BRAINSFit. Working with the ITK community to correct it. This has been worked around in BRAINS Tools by forcing MI registration metric to be single threaded until the ITK community can correct it.
* (Complete: Summer 2011) DTI estimation, property map generation
* (Complete: Summer 2011) DTIPrep improvement
* (Complete: Spring 2011) Write Tutorial for DTIPrep
* (Complete: Spring 2011) Re-deidentify data subset and post to XNAT for NA-MIC purposes. Identified data and we are actively working on creating the data set as of March 2011.
* (Complete: Spring 2011) Process data from raw DICOM data (enhanced DicomToNRRDConverter)
* (Complete: March 2011) Fixed BRAINSTools errors on Windows
* (Complete: Feb 2011) Moved all BRAINS testing data to midas.kitware.com and updated the tests to pull data from there.
* (Complete: Feb 2011) Final approval from the Predict Steering Committee to share data.
* (Complete: Jan 2011) Add testing data suite to DicomToNRRDConverter
* (Complete: Fall 2010) Over 2900 scan sessions collected.

=== Morphometric Processing ===
Goals: Have all the documentation and code in place for BRAINS3 to easily be used by the community.
* 2011-02-15 (Mark Scully): Prepare the BRAINS3 Toolkit for public use
** Improve the consistency of the command line argument processing
*** Create an XML formal Schema from the SEM model examples
*** Enhance the SEM schema with other optional tags needed for improved documentation
*** Make SEM into it's own External_Add project to ease use by other packages
*** Move internal documentation from https://www.icts.uiowa.edu/confluence/display/BRAINS/Command+line+argument+rules, to the SEM pages section
** (Hans Johnson) Improve merging strategies between BRAINS3Tools that are part of BRAINS3 and BRAINS3Tools that are part of Slicer3. A combination of git svn should ease the integration of these two platforms. -- This is not as easy as expected, and it is probably not a good use of our time to make this more automated. It will continue to be done manually.
* 2012-01-15 (Mark Scully): Create documentation for a standard morphometric analysis
* 2013-01-08 (Mark Scully): Create tutorial for full morphometric analysis suitable for basing a DWI based analysis on.

=== DTI Processing ===
Goals: Tools for a longitudinal analysis pipeline of changes measured by fiber tractography to identify white matter tracts that have strong co-morbid degenerative timelines compared to subcortical degeneration over time. Enable the use of the advanced DTI processing in NA-MIC within PREDICT HD. Specifically, adapt all NA-MIC tools to work as individual external Slicer modules (in part they already exist, but need further work), as well as combine them into a single Slicer DTI processing wizard.
* 2010-12-15 (Mark, Joy, UNC Team):
** Word doc Tutorial updated with visual inspection info
** Create Media Wiki version of documentation
** Create PPT presentation for DTIPrep.
* Process data from raw DICOM data (enhanced DicomToNRRDConverter)
** 2010-11-30 (Mark, Joy): Add testing data suite to DicomToNRRDConverter (Joy to identify, Mark to implement)
** Target 2010-12-31(Mark): CMake URL data download mechanisms (Look at Titan CMake has examples of this).
** 2010-12-31 (Clement) DicomToNRRDConverter use of B-Matrix for vb13 data DTI_THP Iowa data.
* Target 2011-05-01 (Revised: 2011-10-01) (Clement/Mahshid): DTI noise estimation, Rician noise filtering
** Review status of code to determine how to modularize into stand alone library and executable
** Include directly into DTIprep:
*** Optional Rician LMMSE image filtering - preprocessing
*** Optional Joint Rician LMMSE image filtering - before DTI creation
* 2011-06-24 (Clement/Mahshid): DTI QC and motion/eddy current correction (enhanced DTIPrep)
** Reporting standardization for easier report generation. Use of XML, extended reporting.
** Loading reports so that batch processing mode and gui processing mode allow the same user
** GUI should allow for manual individual rejection of DWI gradients
* 2011-12-31 (Clement/Mark/Mahshid): DTI estimation, property map generation (existing Slicer modules)
** Review and make command line interfaces and make a dictionary of common command line arguments across the tool sets.
** Push DTIProcess tools into full Slicer Modules
* 2011-08-15 (Revised: 2011-10-15) (Clement): DTI pairwise registration (new Slicer module)
** Develop pairwise registration module allowing:
*** Scalar feature (e.g FA) to drive the registration of a DTI image
*** Direct registration of the DTI image (DTITK?)
** Investigate if both scalars and higher models can both be supported.
** Investigate DTITK inclusion Slicer3
* 2012-05-01 (Clement): DTI atlas based fiber analysis (new Slicer module)
** Tools need re-writing to make them slicer3 compatible.
* 2012-07-01 (Clement): DTI atlas computation module (new Slicer module)
** NITRC implementation FRAT needs rewrite, alternative would be open source AtlasWorks
* 2012-09-01 (Clement): DWI atlas mapping tool (new Slicer module)
** Need to move to NITRC and convert to Slicer3
* 2013-01-01 (Clement/Mark/Jeff/All): Appropriate reporting of all steps
** Reporting of tool status. Track the OpenProvenance project.
** Use XML so that we can convert of OpenProvenance latter if desired.
* 2013-08-30 (Mark): DTI processing wizard in Slicer
* 2013-08-30 (Clement): Longitudinal DTI analysis module
** Create processing pipeline for batch processing a large number of data sets
* 2013-08-30 (Steve Pieper/Kitware/Core 2 involvement): Fiber profile measurement/visualization within Slicer

=== Shape Analysis ===
Goals: Tools for a longitudinal shape analysis pipeline to identify localized changes in basal ganglia tracts that have strong co-morbid degenerative timelines. Enable the use of the advanced SPHARM & particle shape analysis processing in NA-MIC within PREDICT HD.
* 2011-04-01 (Slicer Team/Core2):Sun Grid Engine compatibility
* 2011-04-01 (Clement): Clean up existing tools for deployment at Iowa
** Individual SPHARM tools
** SPHARM shape summary tool
** Statistical Shape Analysis Tools
*** CSV files as part of Slicer standards (Paths, variables, group associations, etc)
* 2012-01-01 (Mark): Analyze 225 subjects with Cross sectional tools
** With between 3-6 longitudinally collected scans.
** With Caudate/Putamen/Thalamus
* 2012-08-01 (Clement/Bea): Add support for particle shape analysis as part of shape pipeline
** Target of 2011-01-14: have feasibility shape analysis done to identify outstanding work that needs to be done.

=== BatchMake Processing and Grid Wizard ===
Goals: Create a distributed batch processing pipeline that pulls data from and reports data to XNAT while distributing jobs across a cluster.
* 2011-01-14 (Marco): Contact BatchMake developers and determine what needs to be done in order to distribute computational load for the analysis of this project.
** 2011-06-24 (Marco): Distribute from local data repositories.
** 2012-06-24 (Marco/Kevin Archie/Tim Olsen): Distribute from XNAT
** 2011-01-14 (Marco/Kevin Archie): Define mechanism for deploying distributed computations with GridWizard as a backend tool that the end-user never sees.

=== Data ===
Goals: Share a meaningful, fully anonymized subset of the sMRI, fMRI, and DWI Huntington's data.
* (Target date of 2010-12-10) Identified the FMRI_024 data (77 subjects, 2-3 years longitudinal) as a good candidate data set for collaborative algorithm development platform. This data set has 3 71direction+8B0 DTI data sets, 2 1.0^3 T1 data sets, and a 0.56x0.56x1.4 T2 data set.
** Will likely need to collect clinical data for shape analysis work that includes: (Age, Gender, Dx, Burdon Score, Motor Score).
** Will need to re-de-identify all the data to be used here.
** Will need to run auto-workup for generating Caudate/putamen/hippocampus/thalmus masks.
** Data exchange will be done through XNAT. including derived data.
* (Target date of 2011-01-14): Share all DTI_THP to wide NAMIC group for validation of tools being developed.

=== Outreach ===
Goals: Inform community of the existence of tools and data sets as well as train them on their use.
*Tutorial(s) on the wiki
*Presentations at HBM, Euro-HD.net, HDSA (The Huntington's Disease Society of America) in year 2
*Hands-on teaching event for the DBP scientific community for year 3

===Who===
*DBP: Hans Johnson, DBP engineer Mark Scully
*Algo: Martin Styner, DBP engineer Clement Vachet, Mahshid Farzinfar (DTIPrep), Beatriz Paniagua (Shape)
*Eng: Dan Marcus, Jeff Grethe, Marco Ruiz