Difference between revisions of "NA"

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  Back to [[NA-MIC_Collaborations|NA-MIC_Collaborations]]
 
  Back to [[NA-MIC_Collaborations|NA-MIC_Collaborations]]
  
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'''Objective:''' We are developing a software infrastructure within ITK for DTI analysis. This includes algorithms for statistical analysis of diffusion tensors, fiber tractography, image filtering and registration.
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'''Objective:''' We are developing new denoising methods for diffusion tensor MRI. These methods are based on physical noise models in DT-MRI.
  
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'''Progress:'''
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'''Progress:''' We have implemented several filtering methods for DT-MRI, including our new method and also several methods from the literature. One goal is to determine whether it is best to filter the estimated tensor fields or the original diffusion weighted images. The method that we developed filters the original diffusion weighted images and takes into account the physical properties of the imaging noise. We are comparing this method with others in the literature, including methods that filter the estimated tensor fields. Our preliminary findings are that it is advantageous to filter the DWIs and to include a physical model of the noise.
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* ''Geometric tools for diffusion tensor analysis.'' We have built a framework for geometric computations on tensors. This includes vector space operations on tensors as well as nonlinear operations that preserve the positive eigenvalues of the tensors. These geometric methods are the foundation for many of the tools below.
 
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* ''Statistical tools for diffusion tensor analysis.'' We have implemented methods for computing averages, covariances, and statistical group comparisons of diffusion tensor data.
 
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* ''Fiber tractography.'' We have developed software for tracking and viewing white matter fiber tracts. This tool has been combined with the statistical methods above for analysis and group comparisons of diffusion tensor data along white matter tracts.
 
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* ''Diffusion Tensor image filtering.'' We are building a framework within ITK for filtering diffusion tensor images. This includes several methods from the literature and both methods that filter estimated tensor fields and those that filter the original diffusion weighted data.
 
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* ''Diffusion Tensor image registration.'' We are building a framework within ITK for registration of diffusion tensor images. This requires image match metrics, interpolation of tensor images, and tensor image transformations.
 
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''Reference:'' Corouge, I., Fletcher, P.T., Joshi, S., Gilmore, J.H., Gerig, G. Fiber Tract-Oriented Statistics for Quantitative Diffusion Tensor MRI Analysis, presented at MICCAI 2005.
 
  
 
'''Key Investigators:'''
 
'''Key Investigators:'''
  
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* Utah: Tom Fletcher, Ross Whitaker
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* Utah: Saurav Basu, Tom Fletcher, Ross Whitaker
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* UNC: Isabelle Corouge, Casey Goodlett, Guido Gerig
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* Harvard PNL: Sylvain Bouix, Doug Marchant, Adam Cohen, Marc Niethammer, Marek Kubicki, Mark Dreusicke, Martha Shenton
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* GE: Jim Miller
 
  
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'''Links:'''
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'''Links'''
  
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* [[Progress_Report:Diffusion_Tensor_Statistics|Diffusion Tensor Statistics Progress Report]]
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* [[AHM_2006:ProjectsRiemmanianDTIFilters|Programming Event Project Page (January 2006)]]
  
 
'''Representative Image and Descriptive Caption:'''
 
'''Representative Image and Descriptive Caption:'''
  
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<div class="thumb tleft"><div style="width: 822px">[[Image:Tensor_interp.jpg|[[Image:Tensor_interp.jpg|Interpolation from a coronal slice of a DTI using the nonlinear averaging. Original data is on the left; the right image is created by up-sampling the image by two.]]]]<div class="thumbcaption"><div class="magnify" style="float: right">[[Image:Tensor_interp.jpg|[[Image:magnify-clip.png|Enlarge]]]]</div>Interpolation from a coronal slice of a DTI using the nonlinear averaging. Original data is on the left; the right image is created by up-sampling the image by two.</div></div></div>
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<div class="thumb tleft"><div style="width: 602px">[[Image:DTIFiltering.jpg|[[Image:DTIFiltering.jpg|Coronal slice from a noisy diffusion tensor image (left). The same slice after applying our DTI filtering method (right).]]]]<div class="thumbcaption"><div class="magnify" style="float: right">[[Image:DTIFiltering.jpg|[[Image:magnify-clip.png|Enlarge]]]]</div>Coronal slice from a noisy diffusion tensor image (left). The same slice after applying our DTI filtering method (right).</div></div></div>

Revision as of 13:29, 18 December 2006

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Back to NA-MIC_Collaborations

Objective: We are developing new denoising methods for diffusion tensor MRI. These methods are based on physical noise models in DT-MRI.

Progress: We have implemented several filtering methods for DT-MRI, including our new method and also several methods from the literature. One goal is to determine whether it is best to filter the estimated tensor fields or the original diffusion weighted images. The method that we developed filters the original diffusion weighted images and takes into account the physical properties of the imaging noise. We are comparing this method with others in the literature, including methods that filter the estimated tensor fields. Our preliminary findings are that it is advantageous to filter the DWIs and to include a physical model of the noise.

Key Investigators:

  • Utah: Saurav Basu, Tom Fletcher, Ross Whitaker
  • Harvard PNL: Sylvain Bouix, Doug Marchant, Adam Cohen, Marc Niethammer, Marek Kubicki, Mark Dreusicke, Martha Shenton

Links

Representative Image and Descriptive Caption:

Enlarge
Coronal slice from a noisy diffusion tensor image (left). The same slice after applying our DTI filtering method (right).
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