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= DTI Processing and Analysis =
+
Back to [[Algorithm:Main|NA-MIC Algorithms]]
 +
__NOTOC__
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= Overview of Utah Algorithms (PI: Ross Whitaker) =
  
* '''Differential Geometry.''' We will provide methods for computing geodesics and distances between diffusion tensors. Several different metrics will be made available, including a simple linear metric and also a symmetric space (curved) metric. These routines are the building blocks for the routines below.
+
We are developing new methods in the areas of statistical shape analysis, MRI tissue segmentation, and diffusion tensor image processing and analysis. We are building shape analysis tools that can generate efficient statistical models appropriate for analyzing anatomical shape differences in the brain. We are developing a wide range of tools for diffusion tensor imaging, that span the entire pipeline from image processing to automatic white matter tract extraction to statistical testing of clinical hypotheses.
  
* '''Statistics''' Given a collection of diffusion tensors, compute the average and covariance statistics. This can be done using the metrics and geometry routines above. A general method for testing differences between groups is planned. The hypothesis test also depends on the underlying geometry used.
+
= Utah Projects =
  
* '''Interpolation''' Interpolation routines will be implemented as a weighted averaging of diffusion tensors in the metric framework. The metric may be chosen so that the interpolation preserves desired properties of the tensors, e.g., orientation, size, etc.
+
{| cellpadding="10" style="text-align:left;"
  
* '''Filtering''' We will provide anisotropic filtering of DTI using the full tensor data (as opposed to component-wise filtering). Filtering will also be able to use the different metrics, allowing control over what properties of the tensors are preserved in the smoothing. We have also developed methods for filtering the original diffusion weighted images (DWIs) that takes the Rician distribution of MR noise into account (see MICCAI 2006 paper below).
 
  
* '''Relation to other NA-MIC projects:'''
 
** DTI processing: filtering and interpolation are an input to further analysis, such as UNC fiber tract analysis and MGH atlas building.
 
** DTI statistics: (UNC) will be used in the analysis of tensor data along fiber tracts.
 
  
[[Image:DTIFiltering.jpg|[[Image:DTIFiltering.jpg|Image:DTIFiltering.jpg]]]]
+
|-
  
Coronal slice from a noisy diffusion tensor image (left). The same slice after applying our Rician noise DTI filtering method (right).
+
| | [[Image:sgerber_brainmanifold_oasis_manifold.png|200px]]
 +
| |
  
== Publications ==
+
== [[Projects:BrainManifold|Brain Manifold Learning]] ==
  
* Basu, S., Fletcher, P.T., Whitaker, R., "Rician Noise Removal in Diffusion Tensor MRI," presented at Medical Image Computing and Computer-Assisted Intervention, MICCAI 2006, LNCS 4190, pp. 117--125. [[Media:BasuDTIFilteringMICCAI2006.pdf| PDF of paper]]
+
This work is concerned with modeling high dimensional spaces, such as the space of brain images. Common approach for representing populations are  template or clustering based approaches. In this project we develop a data driven method to learn a manifold representation from a set of brain images. The presented approach is described and evaluated in the setting of brain MRI but generalizes to other application domains.
* Corouge, I., Fletcher, P.T., Joshi, S., Gilmore, J.H., and Gerig, G., "Fiber Tract-Oriented Statistics for Quantitative Diffusion Tensor MRI Analysis," Medical Image Analysis 10 (2006), 786--798.
 
* Corouge, I., Fletcher, P.T., Joshi, S., Gilmore J.H., and Gerig, G., "Fiber Tract-Oriented Statistics for Quantitative Diffusion Tensor MRI Analysis," in Proceedings of Medical Image Computing and Computer-Assisted Intervention (MICCAI 2005), LNCS 3749, pp. 131--138.
 
  
== Activities ==
+
S Gerber, T Tasdizen, S Joshi, R Whitaker, On the Manifold Structure of the Space of Brain Images, MICCAI 2009.
  
* Developed prototype of DTI geometry package. This includes an abstract class for computing distances and geodesics between tensors, while derived classes can specify the particular metric to use. Current implemented subclasses are the basic linear metric and the symmetric space metric.
+
S Gerber, T Tasdizen, R Whitaker, Dimensionality Reduction and Principal Surfaces via Kernel Map, ICCV 2009.
  
* Developed prototype of DTI statistical package. A general class has been developed for computing averages and principal modes of variation of tensor data. The statistics class can use any of the metrics described above.
+
S. Gerber, T. Tasdizen, P.T. Fletcher, S. Joshi, R. Whitaker, Manifold Modeling for Brain Population Analysis, Medical Image Anal, 3, 2010.
  
* We have begun work on a general method for hypothesis testing of differences in two diffusion tensor groups (upcoming IPMI submission). This method works on the full six-dimensional tensor information, rather than derived measures. The hypothesis testing class can also use any of the different tensor metrics.
 
  
* Participated in the [[Engineering:Programmers_Week_Summer_2005|Programmer's Week]] (June 2005, Boston). During this week the DTI statistics code was developed and added to the NA-MIC toolkit. See our [[Progress_Report:Diffusion_Tensor_Statistics|Progress Report (July 2005)]]. We are also involved in the [[Engineering:Project:Feature_Analysis_Framework|Statistical Feature Analysis Framework]] project with Martin Styner (UNC) and Jim Miller (GE).
+
|-
 +
| style="width:15%" | [[Image:EPI.png|200px]]
 +
| style="width:85%" |
  
== Software ==
+
== [[Projects:EPIDistortionCorrection| Correction for Geometric Distortion in Echo Planar Images]] ==
  
The diffusion tensor statistics code is now part of the NA-MIC toolkit. To get the code, check out the NA-MIC SandBox (see instructions [[Engineering:SandBox|here]]) -- our code is in the "DiffusionTensorStatistics" directory.
+
We have developed a variational image-based approach to correct the susceptibility artifacts in the alignment of diffusion weighted and structural MRI.The correction is formulated as an optimization of a penalty that captures the intensity difference between the jacobian corrected EPI baseline images and a corresponding T2-weighted structural image.  
  
= MRI Segmentation =
+
<font color="red"></font> R Tao, P T Fletcher, S Gerber, R Whitaker, A Variational Image-Based Approach to the Correction of Susceptibility Artifacts
 +
in the Alignment of Diffusion Weighted and Structural MRI, IPMI 2009.
  
* We have implemented the MRI Tissue Classification Algorithm described in [1]. Classes for non-parametric density estimation and automatic parameter selection have been implemented as the basic framework on which we build the classification algorithm.
+
|-
  
* The stochastic non-parametric density estimation framework is very general and allows the user to change kernel types (we have coded isotropic Gaussian, but additional kernels can easily be derived from the same parent class) and sampler types (for example local vs. global image sampling as well as sampling in non-image data) as template parameters.
+
| | [[Image:pipeline.png|150px]]
 +
| |
  
* The classification class uses the stochastic non-parametric density estimation framework to implement the algorithm in [1].
+
== [[Projects:StructuralAndDWIPipeline| A Framework for Joint Analysis of Structural and Diffusion MRI]] ==
  
* An existing ITK bias correction method has been incorporated into the method.
+
This framework addresses the simultaneous alignment and filtering of DWI images to correct eddy current artifacts and the subsequent alignment of those images to structural, T1 MRI to correct for susceptibility artifacts, and this paper demonstrates the importance of performing these corrections. It also shows how a T1-based, group specific atlas can be used to generate grey-matter regions of interest that can drive subsequent connectivity analyses. The result is a system that can be combined with a variety of tools for MRI analysis for tissue classification, morphometry, and cortical parcellation.
  
* Currently, we are registering atlas images to our data using the stand-alone LandmarkInitializedMutualInformationRegistration application. Ideally, we'd like to incorporate an exiting registration algorithm into our code so that classification can be carried out in one step. The initialization to the registration can be provided as command line arguments.
+
<font color="red"></font> Ran Tao, P. Thomas Fletcher, Ross T. Whitaker, in MICCAI 2008 on Computational Diffusion MRI.
  
== Publications ==
 
  
* Tolga Tasdizen, Suyash Awate, Ross Whitaker and Norman Foster, "MRI Tissue Classification with Neighborhood Statistics: A Nonparametric, Entropy-Minimizing Approach," Proceedings of MICCAI'05, Vol 2, pp. 517-525
+
|-
  
= Shape Modeling and Analysis =  
+
| | [[Image:Sulcaldepth.png|200px]]
This research is a new method for constructing compact
+
| |
statistical point-based models of ensembles of similar shapes that does
+
 
not rely on any specific surface parameterization. The method requires
+
== [[Projects:CorticalCorrespondenceWithParticleSystem|Cortical Correspondence using Particle System]] ==
very little preprocessing or parameter tuning, and is applicable to a wider
+
 
range of problems than existing methods, including nonmanifold surfaces
+
In this project, we want to compute cortical correspondence on populations, using various features such as cortical structure, DTI connectivity, vascular structure, and functional data (fMRI). This presents a challenge because of the highly convoluted surface of the cortex, as well as because of the different properties of the data features we want to incorporate together. [[Projects:CorticalCorrespondenceWithParticleSystem|More...]]
and objects of arbitrary topology. The proposed method is to construct
+
 
a point-based sampling of the shape ensemble that simultaneously maximizes
+
<font color="red"></font> Oguz I, Niethammer M, Cates J, Whitaker R, Fletcher T, Vachet C, Styner M. “Cortical Correspondence with Probabilistic Fiber Connectivity”. Proc. Information Processing in Medical Imaging, 2009.
both the geometric accuracy and the statistical simplicity of the
+
 
model. Surface point samples, which also define the shape-to-shape correspondences,
+
|-
are modeled as sets of dynamic particles that are constrained
+
 
to lie on a set of implicit surfaces. Sample positions are optimized by gradient
+
| | [[Image:CatesNamicFigure3.png|200px]]
descent on an energy function that balances the negative entropy
+
| |
of the distribution on each shape with the positive entropy of the ensemble
+
 
of shapes. We also extend the method with a curvature-adaptive
+
== [[Projects:ParticlesForShapesAndComplexes|Adaptive, Particle-Based Sampling for Shapes and Complexes]] ==
sampling strategy in order to better approximate the geometry of the
+
 
objects. We have developed code based on ITK for computation of correspondence-based
+
This research is a new method for constructing compact statistical point-based models of ensembles of similar shapes that does not rely on any specific surface parameterization. The method requires very little preprocessing or parameter tuning, and is applicable to a wider range of problems than existing methods, including nonmanifold surfaces and objects of arbitrary topology. [[Projects:ParticlesForShapesAndComplexes|More...]]
models, and have validated out method in several papers against several synthetic and real
+
 
examples in two and three dimensions, including application to the statistical
+
<font color="red"></font> Particle-Based Shape Analysis of Multi-object Complexes.  Cates J., Fletcher P.T., Styner M., Hazlett H.C., Whitaker R. Int Conf Med Image Comput Comput Assist Interv. 2008;11(Pt 1):477-485.
shape analysis of brain structures. Our most recent work is in the application of the
+
 
particle method to mult-object shape complexes.
+
|-
== Publications ==
+
 
* J. Cates, P. Thomas Fletcher, R. Whitaker, M. Styner, M. Shenton.  Shape Modeling and Analysis with Entropy-Based Particle Systems. IPMI 2007, accepted.
+
| | [[Image:UNCShape_OverviewAnalysis_MICCAI06.gif|200px]]
* J. Cates, P. Thomas Fletcher, R. Whitaker. Entropy-Based Particle Systems for Shape Corresopndence.  Mathematical Foundations of Computational Anatomy Workshop, MICCAI 2006. pp. 90-99 Oct 2006.
+
| |
 +
 
 +
== [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|Shape Analysis Framework using SPHARM-PDM]] ==
 +
 
 +
The UNC shape analysis is based on an analysis framework of objects with spherical topology, described mainly by sampled spherical harmonics SPHARM-PDM. The input of the shape analysis framework is a set of binary segmentations of a single brain structure, such as the hippocampus or caudate. These segmentations are converted into a shape description (SPHARM) with correspondence and analyzed via Hotelling T^2 two sample metric. [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|More...]]
 +
 
 +
<font color="red"></font> Zhao Z., Taylor W., Styner M., Steffens D., Krishnan R., Macfall J. , Hippocampus shape analysis and late-life depression. PLoS ONE. 2008 Mar 19;3(3):e1837.
 +
 
 +
|-
 +
 
 +
| | [[Image:HeadRegressionResult.png|200px]]
 +
| |
 +
 
 +
== [[Projects:ShapeRegression|Particle Based Shape Regression]] ==
 +
 
 +
Shape regression promises to be an important tool to study the relationship between anatomy and underlying clinical or biological parameters, such as age. We propose a new method to building shape models that incorporates regression analysis in the process of optimizing correspondences on a set of open surfaces. The method is applied to provide new results on clinical MRI data related to early development of the human head.
 +
 
 +
M Datar, J Cates, P T Fletcher, S Gouttard, G Gerig, R Whitaker, Particle Based Shape Regression of Open Surfaces with Applications to Developmental Neuroimaging, MICCAI 2009.
 +
 
 +
|-
 +
 
 +
| | [[Image:NonRegularSurfCorres.png|200px]]
 +
| |
 +
 
 +
== [[Projects:NonRegularSurfCorres|Geometric Correspondence for Nonregular Surfaces]] ==
 +
 
 +
To resolve the challenges posed by highly nonregular surfaces, we have proposed an efficient method which incorporates Geodesic distances and an entropy based on surface normals to improve correspondences.
 +
 
 +
M Datar, Y Gur, B Paniagua, M Styner, R Whitaker,Geometric Correspondence for Ensembles of Nonregular Shapes, MICCAI 2011.
 +
 
 +
|-
 +
 
 +
| | [[Image:MixedEffectsShape.png|200px]]
 +
| |
 +
 
 +
== [[Projects:MixedEffectsShape|Mixed-Effects Shape Models for Longitudinal Analysis]] ==
 +
 
 +
Longitudinal shape changes in anatomy are characterized using a new method that combines point correspondences across shapes with the statistical modeling of individual and population trends via the linear mixed-effects model. This method helps us examine and contrast population trends with individual growth trajectories.
 +
 
 +
<font color="red">'''New: '''</font>  M Datar, P Muralidharan, A Kumar, S Gouttard, J Piven, G Gerig, RT Whitaker, PT Fletcher,Mixed-Effects Shape Models for Estimating Longitudinal Changes in Anatomy, STIA 2012
 +
 
 +
|-
 +
 
 +
| | [[Image:CamFAIAnalysis.png|200px]]
 +
| |
 +
 
 +
== Statistical Shape Analysis of Cam-FAI ==
 +
 
 +
Cam femoroacetabular impingement (FAI) is characterized by a malformed femoral head that may lead to early hip osteoarthritis. Radiographic measurements are used to diagnose cam FAI and often assume the femur shape to be spherical. Statistical shape modeling (SSM) can be used to compare complex 3D morphology without the need to assume ideal geometry and quantify morphologic differences between control and FAI femurs.
 +
 
 +
<font color="red">'''New: '''</font> MD Harris, M Datar, E Jurrus, CL Peters, RT Whitaker, AE Anderson, [http://www.cs.utah.edu/~manasi/pubs/CM165P.pdf|Statistical Shape Modeling of CAM-type Femoroacetabular Impingement], CMBBE 2012
 +
 
 +
|-
 +
 
 +
| | [[Image:MiceMOAnalysis.png|200px]]
 +
| |
 +
 
 +
== Understanding Short Bone Phenotype in Multiple Osteochondromas ==
 +
 
 +
Novel statistical methods were developed to study the 'steal phenomenon' caused by multiple osteochondromas in mouse models. Bone lengths and volumes were compared. Metaphyseal volume deviations from normal, as a measure of osteochondroma volumetric growth, were correlated with length deviations.
 +
 
 +
<font color="red">'''New: '''</font>  KB Jones, M Datar, S Ravichandran, H Jin, E Jurrus, RT Whitaker, MR Capecchi, Toward an Understanding of the Short Bone Phenotype Associated with Multiple Osteochondromas, JOR 2012 (to appear)
 +
 
 +
|-
 +
 
 +
| | [[Image:FiberTracts-angle.jpg|200px]]
 +
| |
 +
 
 +
== [[Projects:DTIVolumetricWhiteMatterConnectivity|DTI Volumetric White Matter Connectivity]] ==
 +
 
 +
We have developed a PDE-based approach to white matter connectivity from DTI that is founded on the principal of minimal paths through the tensor volume. Our method computes a volumetric representation of a white matter tract given two endpoint regions. We have also developed statistical methods for quantifying the full tensor data along these pathways, which should be useful in clinical studies using DT-MRI. [[Projects:DTIVolumetricWhiteMatterConnectivity|More...]]
 +
 
 +
|-
 +
| style="width:15%" | [[Image:DTIFiltering.jpg|200px]]
 +
| style="width:85%" |
 +
 
 +
== [[Projects:DTIProcessingTools|DTI Processing and Statistics Tools]] ==
 +
 
 +
We implement the diffusion weighted image (DWI) registration model from the paper of G.K.Rohde et al. Patient head motion and eddy currents distortion cause artifacts in maps of diffusion parameters computer from DWI. This model corrects these two distortions at the same time including brightness correction.
 +
 
 +
|-
 +
 
 +
| | [[Image:Brain-seg-utah.png|200px]]
 +
| |
 +
 
 +
== [[Projects:TissueClassificationWithNeighborhoodStatistics| Tissue Classification with Neighborhood Statistics]] ==
 +
 
 +
We have implemented an MRI tissue classification algorithm based on unsupervised non-parametric density estimation of tissue intensity classes.
 +
[[Projects:TissueClassificationWithNeighborhoodStatistics|More...]]
 +
 
 +
 
 +
|-
 +
 
 +
| | [[Image:combined_50_seg_labeled.png|200px]]
 +
| |
 +
 
 +
== [[Projects:AtlasBasedBrainSegmentation| Atlas-Based Brain Segmentation]] ==
 +
 
 +
We have implemented an Multi-Atlases based brain MRI tissue segmentation method. This method using a fast,shape-based hierarchical matching approach to find the kNNs for a target brain in a brain dataset with known segmentations/labels, and then fuse the labels of the kNNs instead of the labels of all images in the dataset, which can give an accurate atlas for the target.
 +
 
 +
<font color="red"></font> P. Zhu, S.P. Awate, S. Gerber, R. Whitaker. Fast Shape-Based Nearest-Neighbor Search for Brain MRIs Using Hierarchical Feature Matching, MICCAI 2011.
 +
 
 +
|}

Latest revision as of 06:22, 11 April 2023

Home < Algorithm:Utah
Back to NA-MIC Algorithms

Overview of Utah Algorithms (PI: Ross Whitaker)

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

Utah Projects

Sgerber brainmanifold oasis manifold.png

Brain Manifold Learning

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

S Gerber, T Tasdizen, S Joshi, R Whitaker, On the Manifold Structure of the Space of Brain Images, MICCAI 2009.

S Gerber, T Tasdizen, R Whitaker, Dimensionality Reduction and Principal Surfaces via Kernel Map, ICCV 2009.

S. Gerber, T. Tasdizen, P.T. Fletcher, S. Joshi, R. Whitaker, Manifold Modeling for Brain Population Analysis, Medical Image Anal, 3, 2010.


EPI.png

Correction for Geometric Distortion in Echo Planar Images

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

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

Pipeline.png

A Framework for Joint Analysis of Structural and Diffusion MRI

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

Ran Tao, P. Thomas Fletcher, Ross T. Whitaker, in MICCAI 2008 on Computational Diffusion MRI.


Sulcaldepth.png

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. More...

Oguz I, Niethammer M, Cates J, Whitaker R, Fletcher T, Vachet C, Styner M. “Cortical Correspondence with Probabilistic Fiber Connectivity”. Proc. Information Processing in Medical Imaging, 2009.

CatesNamicFigure3.png

Adaptive, Particle-Based Sampling for Shapes and Complexes

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

Particle-Based Shape Analysis of Multi-object Complexes. Cates J., Fletcher P.T., Styner M., Hazlett H.C., Whitaker R. Int Conf Med Image Comput Comput Assist Interv. 2008;11(Pt 1):477-485.

UNCShape OverviewAnalysis MICCAI06.gif

Shape Analysis Framework using SPHARM-PDM

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

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

HeadRegressionResult.png

Particle Based Shape Regression

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

M Datar, J Cates, P T Fletcher, S Gouttard, G Gerig, R Whitaker, Particle Based Shape Regression of Open Surfaces with Applications to Developmental Neuroimaging, MICCAI 2009.

NonRegularSurfCorres.png

Geometric Correspondence for Nonregular Surfaces

To resolve the challenges posed by highly nonregular surfaces, we have proposed an efficient method which incorporates Geodesic distances and an entropy based on surface normals to improve correspondences.

M Datar, Y Gur, B Paniagua, M Styner, R Whitaker,Geometric Correspondence for Ensembles of Nonregular Shapes, MICCAI 2011.

MixedEffectsShape.png

Mixed-Effects Shape Models for Longitudinal Analysis

Longitudinal shape changes in anatomy are characterized using a new method that combines point correspondences across shapes with the statistical modeling of individual and population trends via the linear mixed-effects model. This method helps us examine and contrast population trends with individual growth trajectories.

New: M Datar, P Muralidharan, A Kumar, S Gouttard, J Piven, G Gerig, RT Whitaker, PT Fletcher,Mixed-Effects Shape Models for Estimating Longitudinal Changes in Anatomy, STIA 2012

CamFAIAnalysis.png

Statistical Shape Analysis of Cam-FAI

Cam femoroacetabular impingement (FAI) is characterized by a malformed femoral head that may lead to early hip osteoarthritis. Radiographic measurements are used to diagnose cam FAI and often assume the femur shape to be spherical. Statistical shape modeling (SSM) can be used to compare complex 3D morphology without the need to assume ideal geometry and quantify morphologic differences between control and FAI femurs.

New: MD Harris, M Datar, E Jurrus, CL Peters, RT Whitaker, AE Anderson, Shape Modeling of CAM-type Femoroacetabular Impingement, CMBBE 2012

MiceMOAnalysis.png

Understanding Short Bone Phenotype in Multiple Osteochondromas

Novel statistical methods were developed to study the 'steal phenomenon' caused by multiple osteochondromas in mouse models. Bone lengths and volumes were compared. Metaphyseal volume deviations from normal, as a measure of osteochondroma volumetric growth, were correlated with length deviations.

New: KB Jones, M Datar, S Ravichandran, H Jin, E Jurrus, RT Whitaker, MR Capecchi, Toward an Understanding of the Short Bone Phenotype Associated with Multiple Osteochondromas, JOR 2012 (to appear)

FiberTracts-angle.jpg

DTI Volumetric White Matter Connectivity

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

DTIFiltering.jpg

DTI Processing and Statistics Tools

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

Brain-seg-utah.png

Tissue Classification with Neighborhood Statistics

We have implemented an MRI tissue classification algorithm based on unsupervised non-parametric density estimation of tissue intensity classes. More...


Combined 50 seg labeled.png

Atlas-Based Brain Segmentation

We have implemented an Multi-Atlases based brain MRI tissue segmentation method. This method using a fast,shape-based hierarchical matching approach to find the kNNs for a target brain in a brain dataset with known segmentations/labels, and then fuse the labels of the kNNs instead of the labels of all images in the dataset, which can give an accurate atlas for the target.

P. Zhu, S.P. Awate, S. Gerber, R. Whitaker. Fast Shape-Based Nearest-Neighbor Search for Brain MRIs Using Hierarchical Feature Matching, MICCAI 2011.