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| − | = DTI Processing and Analysis =
| + | Back to [[Algorithm:Main|NA-MIC Algorithms]] |
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| − | * '''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.
| + | = Overview of Utah Algorithms = |
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| − | * '''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.
| + | A brief overview of the Utah's algorithms goes here. This should not be much longer than a paragraph. Remember that people visiting your site want to be able to understand very quickly what you're all about and then they want to jump into your site's projects. The projects below are organized into a two column table: the left column is for representative images and the right column is for project overviews. The number of rows corresponds to the number of projects at your site. Put the most interesting and relevant projects at the top of the table. You do not need to organize the table according to subject matter (i.e. do not group all segmentation projects together and all DWI projects together). |
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| − | * '''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.
| + | = Utah Projects = |
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| − | * '''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).
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| − | {|
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| − | |[[Image:DTIFiltering.jpg|thumb|512px|Coronal slice from a noisy DTI (left). The same slice after applying our Rician noise DTI filtering method (right).]]
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| − | == Volumetric White Matter Connectivity == | |
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| − | 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. This work, [http://www.na-mic.org/Special:PubDB_Finishsubmission?dspaceid=634 "A volumetric approach to quantifying region-to-region white matter connectivity in diffusion tensor MRI"] has been presented at IPMI 2007.
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| − | |[[Image:FiberTracts-top.jpg|thumb|256px|Five extracted fiber tracts (top view)]] | + | | style="width:10%" | [[Image:Meanviews.png|thumb|left|200px]] |
| − | |[[Image:FiberTracts-angle.jpg|thumb|256px|Five extracted fiber tracts (side angle view)]] | + | | style="width:90%" | |
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| − | '''Effecient GPU implementation:''' We have recently implemented a fast solver for the volumetric white matter connectivity using graphics hardware, i.e., the Graphics Processing Unit (GPU). This method takes advantage of the massively parallel nature of modern GPUs and runs 50-100 times faster than a standard implementation on the CPU. The fast solver allows interactive visualization of white matter pathways. We have developed a user interface in which a user can select two endpoint regions for the white matter tract of interest, which is typically computed and displayed within 1-3 seconds. This work has been submitted to VIS 2007.
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| + | == [[Projects:ParticlesForShapesAndComplexes|Adaptive, Particle-Based Sampling for Shapes and Complexes]] == |
| − | |[[Image:GPU-tract.jpg|thumb|320px|A screen shot from the interactive white matter connectivity solver. Shown are two selected endpoint regions and the resulting white matter pathway.]]
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| − | |}
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| − | * '''Relation to other NA-MIC projects:'''
| + | Here we write a very brief description of Project 2. Notice a representative image is chosen from the project and places in the left column. [[Projects:ParticlesForShapesAndComplexes|More...]] |
| − | ** DTI processing: filtering and interpolation are an input to further analysis, such as UNC fiber tract analysis and MGH atlas building.
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| − | ** DTI statistics: (UNC) will be used in the analysis of tensor data along fiber tracts.
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| − | == Publications == | + | <font color="red">'''New: '''</font> Here we give something new and exciting about the project. The most recent publication on this project is very appropriate for this slot. |
| − | * Jeong, W.-K., Fletcher, P.T., Tao, R., Whitaker, R.T., "Interactive Visualization of Volumetric White Matter Connectivity in Diffusion Tensor MRI Using a Parallel-Hardware Hamilton-Jacobi Solver," under review IEEE Visualization Conference (VIS) 2007.
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| − | * Fletcher, P.T., Tao, R., Jeong, W.-K., Whitaker, R.T., "A Volumetric Approach to Quantifying Region-to-Region White Matter Connectivity in Diffusion Tensor MRI," to appear Information Processing in Medical Imaging (IPMI) 2007.
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| − | * Fletcher, P.T., Joshi, S. "Riemannian Geometry for the Statistical Analysis of Diffusion Tensor Data". Signal Processing, vol. 87, no. 2, February 2007, pp. 250-262.
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| − | * 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]]
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| − | * 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.
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| − | * 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.
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| − | == Activities ==
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| − | * Developed a Slicer module for our DT-MRI Rician noise removal during the [[2007_Project_Half_Week|2007 Project Half Week]]. Also enhanced the method by including an automatic method for determining the noise sigma in the image.
| + | | style="width:10%" | [[Image:DTIFiltering.jpg|thumb|left|200px]] |
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| − | * 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.
| + | == [[Projects:DTIVolumetricWhiteMatterConnectivity|DTI Volumetric White Matter Connectivity]] == |
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| − | * 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.
| + | Here we write a very brief description of Project 1. Notice a representative image is chosen from the project and places in the left column. [[Projects:DTIVolumetricWhiteMatterConnectivity|More...]] |
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| − | * We have begun work on a general method for hypothesis testing of differences in two diffusion tensor groups. This method works on the full six-dimensional tensor information, rather than derived measures. The hypothesis testing class can also use any of the different tensor metrics.
| + | <font color="red">'''New: '''</font> Here we give something new and exciting about the project. The most recent publication on this project is very appropriate for this slot. |
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| − | * 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).
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| − | == Software == | + | | style="width:10%" | [[Image:brain.png|thumb|left|200px]] |
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| − | 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.
| + | == [[Projects:TissueClassificationWithNeighborhoodStatistics| Tissue Classification with Neighborhood Statistics]] == |
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| − | = MRI Segmentation =
| + | Here we write a very brief description of Project 3. Notice a representative image is chosen from the project and places in the left column. [[Projects:TissueClassificationWithNeighborhoodStatistics|More...]] |
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| − | * 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.
| + | <font color="red">'''New: '''</font> Here we give something new and exciting about the project. The most recent publication on this project is very appropriate for this slot. |
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| − | * 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.
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| − | * The classification class uses the stochastic non-parametric density estimation framework to implement the algorithm in [1].
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| − | * An existing ITK bias correction method has been incorporated into the method.
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| − | * 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.
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| − | == Publications ==
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| − | * 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
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| − | = Shape Modeling and Analysis =
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| − | This research is a new method for constructing compact
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| − | statistical point-based models of ensembles of similar shapes that does
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| − | not rely on any specific surface parameterization. The method requires
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| − | very little preprocessing or parameter tuning, and is applicable to a wider
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| − | range of problems than existing methods, including nonmanifold surfaces
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| − | and objects of arbitrary topology. The proposed method is to construct
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| − | a point-based sampling of the shape ensemble that simultaneously maximizes
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| − | both the geometric accuracy and the statistical simplicity of the
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| − | model. Surface point samples, which also define the shape-to-shape correspondences, are modeled as sets of dynamic particles that are constrained
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| − | to lie on a set of implicit surfaces. Sample positions are optimized by gradient
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| − | descent on an energy function that balances the negative entropy
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| − | of the distribution on each shape with the positive entropy of the ensemble
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| − | of shapes. We also extend the method with a curvature-adaptive
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| − | sampling strategy in order to better approximate the geometry of the
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| − | objects. We have developed code based on ITK for computation of correspondence-based 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 shape analysis of brain structures. We used hippocampus data from a schizo-typal personality disorder (SPD) study funded by
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| − | the Stanley Foundation and UNC-MHNCRC (MH33127), and caudate data from a schizophrenia
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| − | study funded by NIMH R01 MH 50740 (Shenton), NIH K05 MH 01110 (Shenton), NIMH
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| − | R01 MH 52807 (McCarley), and a VA Merit Award (Shenton).
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| − | Figure 1,2 illustrates results of hypothesis testing for group differences from the control population for the left/right hippocampus and the left/right caudate. Raw and FDR-corrected p-values are given. Areas of significant
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| − | group differences ($p <= 0.05$) are shown as dark regions. Areas with insignificant
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| − | group differences ($p > 0.05$) are shown as light regions. Our results correlate with with other published hypothesis testing results on this data.
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| − | Our most recent work is in the application of the particle method to multi-object shape complexes. We have developed a novel method for computing surface point correspondences of multi-object anatomy that
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| − | is a straightforward extension of the particle method for single-object anatomy. The correspondences take advantage of the statistical structure of an ensemble of complexes, and thus they are suitable for joint statistical analyses of shape and relative pose. The proposed method uses a dynamic particle system to optimize correspondence point positions across all structures in a complex
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| − | simultaneously, in order to create a compact model of ensemble statistics.
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| − | It is a different approach from previous methods for dealing with shape
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| − | complexes, because to date researchers have considered the correspondence
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| − | problem only for each structure independently, and have ignored intermodel
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| − | correlations in the shape parameterization. These correlations are
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| − | particularly important when the correspondences are constructed in order to
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| − | reduce or minimize the information content of the ensemble.
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| − | [[Image:Lcomb-grayscale.png|thumbnail|Figure 1]]
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| − | [[Image:Rcomb-grayscale.png|thumbnail|Figure 2]]
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| − | We have developed a formulation of the multi-object correspondence optimization, and have applied it to a proof-of-concept application to the analysis of brain structure complexes from a longitudinal study of pediatric autism that is underway at UNC Chapel Hill. This work is in conjunction with Martin Styner, Heather Cody Hazlett, and Joe Piven. Figure 3 shows the raw p-values from hypothesis testing for group differences as color-maps on mean shapes of the autism group (top row) and the normal control group (bottom row). Red indicates areas where significant group differences were found (p-values $< 0.05$), with blue elsewhere (p-values$>= 0.05$). The top row shows the results when relative geometric scale is included, and the bottom shows relative scale removed. Structures are shown in the their mean orientations, positions, and scale in the global coordinate frame. We computed the average orientation for each structure using methods for averaging in
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| − | curved spaces.
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| − | [[Image:meanviews.png|thumbnail|Figure 3]]
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| − | == Publications ==
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| − | * J. Cates, P.T. Fletcher, M. Styner, M. Shenton, R. Whitaker. Shape Modeling and Analysis with Entropy-Based Particle Systems. IPMI 2007, accepted.
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| − | * J. Cates, P.T. Fletcher, R. Whitaker. Entropy-Based Particle Systems for Shape Corresopndence. Mathematical Foundations of Computational Anatomy Workshop, MICCAI 2006. pp. 90-99 Oct 2006.
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| − | * J Cates, PT Fletcher, M Styner, H Hazlett, J Piven, R Whitaker. Particle-Based Shape Analysis of Multi-Object Complexes. Under review MICCAI 2007.
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