Difference between revisions of "Projects:DiffusionTensorImageFiltering"

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  Back to [[NA-MIC_Collaborations|NA-MIC_Collaborations]]
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  Back to [[NA-MIC_Collaborations|NA-MIC_Collaborations]], [[Algorithm:Utah|Utah Algorithms]]
  
'''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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= Diffusion Tenson Image Filtering =
  
'''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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<div class="thumb tright"><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>
  
'''Key Investigators:'''
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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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= Description =
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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.
  
* Utah: Saurav Basu, Tom Fletcher, Ross Whitaker
 
* Harvard PNL: Sylvain Bouix, Doug Marchant, Adam Cohen, Marc Niethammer, Marek Kubicki, Mark Dreusicke, Martha Shenton
 
  
'''Links'''
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= Key Investigators =
  
* [[AHM_2006:ProjectsRiemmanianDTIFilters|Programming Event Project Page (January 2006)]]
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* Utah: Saurav Basu, Tom Fletcher, Ross Whitaker
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* BWH: Sylvain Bouix, Doug Markant, Adam Cohen, Marc Niethammer, Marek Kubicki, Mark Dreusicke, Martha Shenton
  
'''Representative Image and Descriptive Caption:'''
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= Links =
  
<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>
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Project Week Results: [[AHM_2006:ProjectsRiemmanianDTIFilters|Jan 2006]]

Latest revision as of 19:25, 27 November 2007

Home < Projects:DiffusionTensorImageFiltering
Back to NA-MIC_Collaborations, Utah Algorithms

Diffusion Tenson Image Filtering

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

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

Description

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
  • BWH: Sylvain Bouix, Doug Markant, Adam Cohen, Marc Niethammer, Marek Kubicki, Mark Dreusicke, Martha Shenton

Links

Project Week Results: Jan 2006