Projects:DiffusionTensorImageFiltering - Revision history

Melonakos at 19:25, 27 November 2007

2007-11-27T19:25:18Z

Melonakos at 14:13, 4 September 2007

2007-09-04T14:13:50Z

Melonakos at 14:13, 4 September 2007

2007-09-04T14:13:03Z

Melonakos at 14:12, 4 September 2007

2007-09-04T14:12:22Z

Melonakos at 13:22, 4 September 2007

2007-09-04T13:22:24Z

Melonakos at 18:56, 3 September 2007

2007-09-03T18:56:13Z

Melonakos: NA-MIC/Projects/Diffusion Image Analysis/Diffusion tensor image filtering moved to Projects:DiffusionTensorImageFiltering

2007-08-21T03:52:42Z

NA-MIC/Projects/Diffusion Image Analysis/Diffusion tensor image filtering moved to Projects:DiffusionTensorImageFiltering

Tkapur at 16:21, 3 May 2007

2007-05-03T16:21:19Z

Dougm at 13:30, 19 April 2007

2007-04-19T13:30:38Z

Andy: Update from Wiki

2006-12-18T19:40:33Z

Update from Wiki

New page

Back to [[NA-MIC_Collaborations|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'''

* [[AHM_2006:ProjectsRiemmanianDTIFilters|Programming Event Project Page (January 2006)]]

'''Representative Image and Descriptive Caption:'''

<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>

@@ Line 1: / Line 1: @@
 = Diffusion Tenson Image Filtering =
 <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>
 We are developing new denoising methods for diffusion tensor MRI. These methods are based on physical noise models in DT-MRI.
-'''Progress'''
+= 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'''
+= 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'''
+= Links =
   Project Week Results: [[AHM_2006:ProjectsRiemmanianDTIFilters|Jan 2006]]

@@ Line 2: / Line 2: @@
   Back to [[NA-MIC_Collaborations|NA-MIC_Collaborations]], [[Algorithm:Utah|Utah Algorithms]]
 '''Objectives'''
@@ Line 10: / Line 12: @@
 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.
 ''References''

@@ Line 10: / Line 10: @@
 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.
 ''References''
@@ Line 21: / Line 25: @@
   Project Week Results: [[AHM_2006:ProjectsRiemmanianDTIFilters|Jan 2006]]

@@ Line 3: / Line 3: @@
   Back to [[NA-MIC_Collaborations|NA-MIC_Collaborations]], [[Algorithm:Utah|Utah Algorithms]]
-'''Objective'''
+'''Objectives'''
 We are developing new denoising methods for diffusion tensor MRI. These methods are based on physical noise models in DT-MRI.
@@ Line 10: / Line 10: @@
 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'''
@@ Line 18: / Line 20: @@
 '''Links'''
-* [[AHM_2006:ProjectsRiemmanianDTIFilters|Programming Event Project Page (January 2006)]]
+ Project Week Results: [[AHM_2006:ProjectsRiemmanianDTIFilters|Jan 2006]]
 ''Representative Image and Descriptive Caption''
 <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>

@@ Line 1: / Line 1: @@
 = Diffusion Tenson Image Filtering =
-  Back to [[NA-MIC_Collaborations|NA-MIC_Collaborations]]
+  Back to [[NA-MIC_Collaborations|NA-MIC_Collaborations]], [[Algorithm:Utah|Utah Algorithms]]
 '''Objective'''

← Older revision	Revision as of 03:52, 21 August 2007
(No difference)

@@ Line 8: / Line 8: @@
 * Utah: Saurav Basu, Tom Fletcher, Ross Whitaker
-* Harvard PNL: Sylvain Bouix, Doug Marchant, Adam Cohen, Marc Niethammer, Marek Kubicki, Mark Dreusicke, Martha Shenton
+* Harvard PNL: Sylvain Bouix, Doug Markant, Adam Cohen, Marc Niethammer, Marek Kubicki, Mark Dreusicke, Martha Shenton
 '''Links'''