Projects:MultiTensorTractography - Revision history

Ann Adams: /* Publications */

2010-05-14T10:58:22Z

Publications

Ann Adams: /* Multi Tensor Tractography */

2010-05-14T10:56:42Z

Multi Tensor Tractography

Yrathi: /* Publications */

2010-05-13T17:35:18Z

Publications

Yrathi: /* Key Investigators */

2010-05-13T17:29:21Z

Key Investigators

Yrathi: /* Publications */

2010-05-13T17:28:36Z

Publications

Yrathi: /* Multi Tensor Tractography */

2010-05-13T17:28:13Z

Multi Tensor Tractography

Yrathi at 17:19, 13 May 2010

2010-05-13T17:19:53Z

Yrathi at 17:18, 13 May 2010

2010-05-13T17:18:12Z

Yrathi: /* Multi Tensor Tractography */

2010-05-13T17:17:41Z

Multi Tensor Tractography

Yrathi: Created page with '= Multi Tensor Tractography = We describe a unified framework to simultaneously estimate multiple fibers at each location and perform tractography. Existing techniques estimate …'

2010-05-13T17:15:29Z

Created page with '= Multi Tensor Tractography = We describe a unified framework to simultaneously estimate multiple fibers at each location and perform tractography. Existing techniques estimate …'

New page

= Multi Tensor Tractography =

We describe a unified framework to simultaneously estimate multiple fibers at
each location and perform tractography. Existing techniques estimate the local fiber orientation at each voxel
independently so there is no running knowledge of confidence in the
estimated fiber model. We formulate fiber tracking as recursive estimation: at each step of tracing
the fiber, the current estimate is guided by the previous.

To do this we model the signal as either a weighted mixture of Gaussian tensors or
Watson directional functions and
perform tractography within a filter framework. Starting from a seed point,
each fiber is traced to its termination using an unscented Kalman filter to
simultaneously fit the local model and propagate in the most consistent
direction. Further, we modify the Kalman filter to enforce model
constraints, i.e., positive eigenvalues and convex weights. Despite the
presence of noise and uncertainty, this provides a causal estimate of the
local structure at each point along the fiber.

Synthetic experiments demonstrate that this approach significantly improves
the angular resolution at crossings and branchings while consistently
estimating the mixture weights. ''In vivo'' experiments confirm the
ability to trace out fibers in areas known to contain such crossing and
branching while providing inherent path regularization.

@@ Line 35: / Line 35: @@
 * J. Malcolm, M. E. Shenton, Y. Rathi. Two-Tensor Tractography Using a Constrained Filter. MICCAI, Pages 894-902, 2009  [http://pnl.bwh.harvard.edu/pub/papers_html/Malcolm-MICCAI2009.html (see here for the reference)].
-* J. Malcolm, M. Kubicki, M. E. Shenton, Y. Rathi. The Effect of local fiber model on population studies. Workshop on Diffusion modeling (MICCAI), Pages 33-40, 2009  [http://jgmalcolm.com/ (see here for the reference)].
+* J. Malcolm, M. Kubicki, M. E. Shenton, Y. Rathi. The effect of local fiber model on population studies. Workshop on Diffusion modeling (MICCAI), Pages 33-40, 2009  [http://jgmalcolm.com/ (see here for the reference)].
-* J. Malcolm, O. Michailovich, S. Bouix, C.F. Westin, M. E. Shenton, Y. Rathi   A filtered approach to neural tractography using the Watson directional function. Med Image Analysis, Volume 14, Pages 58-69 2010   [http://pnl.bwh.harvard.edu/pub/papers_html/MalcolmMedImage10.html (see here for the reference)].
+* J. Malcolm, O. Michailovich, S. Bouix, C.F. Westin, M. E. Shenton, Y. Rathi   A filtered approach to neural tractography using the Watson directional function. Med Image Analysis, Volume 14, Pages 58-69 2010   [http://pnl.bwh.harvard.edu/pub/papers_html/MalcolmMedImage10.html (see here for the reference)]

← Older revision		Revision as of 17:35, 13 May 2010
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	* J. Malcolm, M. E. Shenton, Y. Rathi. Two-Tensor Tractography Using a Constrained Filter. MICCAI, Pages 894-902, 2009 [http://pnl.bwh.harvard.edu/pub/papers_html/Malcolm-MICCAI2009.html (see here for the reference)].		* J. Malcolm, M. E. Shenton, Y. Rathi. Two-Tensor Tractography Using a Constrained Filter. MICCAI, Pages 894-902, 2009 [http://pnl.bwh.harvard.edu/pub/papers_html/Malcolm-MICCAI2009.html (see here for the reference)].
		+
		+	* J. Malcolm, M. Kubicki, M. E. Shenton, Y. Rathi. The Effect of local fiber model on population studies. Workshop on Diffusion modeling (MICCAI), Pages 33-40, 2009 [http://jgmalcolm.com/ (see here for the reference)].

	* J. Malcolm, O. Michailovich, S. Bouix, C.F. Westin, M. E. Shenton, Y. Rathi A filtered approach to neural tractography using the Watson directional function. Med Image Analysis, Volume 14, Pages 58-69 2010 [http://pnl.bwh.harvard.edu/pub/papers_html/MalcolmMedImage10.html (see here for the reference)].		* J. Malcolm, O. Michailovich, S. Bouix, C.F. Westin, M. E. Shenton, Y. Rathi A filtered approach to neural tractography using the Watson directional function. Med Image Analysis, Volume 14, Pages 58-69 2010 [http://pnl.bwh.harvard.edu/pub/papers_html/MalcolmMedImage10.html (see here for the reference)].

← Older revision		Revision as of 17:29, 13 May 2010
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	* Brigham & Women's Hospital: Y. Rathi, J. Malcolm, Sylvain Bouix, Marek Kubicki, Martha E. Shenton, Carl-Fredrik Westin.		* Brigham & Women's Hospital: Y. Rathi, J. Malcolm, Sylvain Bouix, Marek Kubicki, Martha E. Shenton, Carl-Fredrik Westin.
		+	* University of Waterloo: O. Michailovich

	= Publications =		= Publications =

@@ Line 32: / Line 32: @@
 * J. Malcolm, M. E. Shenton, Y. Rathi. Neural tractography using an unscented Kalman filter. Inf Process Med Imaging,Volume 21, Pages 126-138, 2009  [http://pnl.bwh.harvard.edu/pub/papers_html/MalcolmInfProc09.html (see here for the reference)].
-* J. Malcolm, M. E. Shenton, Y. Rathi. Two-Tensor Tractography Using a Constrained Filter. Medical Image Computing and Computer Assisted Intervention
+* J. Malcolm, M. E. Shenton, Y. Rathi. Two-Tensor Tractography Using a Constrained Filter. MICCAI, Pages 894-902, 2009  [http://pnl.bwh.harvard.edu/pub/papers_html/Malcolm-MICCAI2009.html (see here for the reference)].
 * J. Malcolm, O. Michailovich, S. Bouix, C.F. Westin, M. E. Shenton, Y. Rathi   A filtered approach to neural tractography using the Watson directional function. Med Image Analysis, Volume 14, Pages 58-69 2010   [http://pnl.bwh.harvard.edu/pub/papers_html/MalcolmMedImage10.html (see here for the reference)].

← Older revision		Revision as of 17:28, 13 May 2010
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	[[Image:case01045_2T_tc.png\|700px\|thumb\|left\|]]		[[Image:case01045_2T_tc.png\|700px\|thumb\|left\|]]
		+
		+	= Key Investigators =
		+
		+	* Brigham & Women's Hospital: Y. Rathi, J. Malcolm, Sylvain Bouix, Marek Kubicki, Martha E. Shenton, Carl-Fredrik Westin.
		+
		+	= Publications =
		+	* J. Malcolm, M. E. Shenton, Y. Rathi. Neural tractography using an unscented Kalman filter. Inf Process Med Imaging,Volume 21, Pages 126-138, 2009 [http://pnl.bwh.harvard.edu/pub/papers_html/MalcolmInfProc09.html (see here for the reference)].
		+
		+	* J. Malcolm, M. E. Shenton, Y. Rathi. Two-Tensor Tractography Using a Constrained Filter. Medical Image Computing and Computer Assisted Intervention
		+	MICCAI, Pages 894-902, 2009 [http://pnl.bwh.harvard.edu/pub/papers_html/Malcolm-MICCAI2009.html (see here for the reference)].
		+
		+	* J. Malcolm, O. Michailovich, S. Bouix, C.F. Westin, M. E. Shenton, Y. Rathi A filtered approach to neural tractography using the Watson directional function. Med Image Analysis, Volume 14, Pages 58-69 2010 [http://pnl.bwh.harvard.edu/pub/papers_html/MalcolmMedImage10.html (see here for the reference)].

@@ Line 3: / Line 3: @@
 We describe a unified framework to simultaneously estimate multiple fibers at
 each location and perform tractography. Existing techniques estimate the local fiber orientation at each voxel
-independently so there is no running knowledge of confidence in the
+independently. Consequently, there is no running knowledge of confidence in the
-estimated fiber model. We formulate fiber tracking as recursive estimation: at each step of tracing
+estimated fiber model. We formulate fiber tracking as a recursive estimation: At each step of tracing
 the fiber, the current estimate is guided by the previous.
 To do this we model the signal as either a weighted mixture of Gaussian tensors or
 Watson directional functions and
-perform tractography within a filter framework.  Starting from a seed point,
+perform tractography within a filter framework. Starting from a seed point, the
 fiber is traced to its termination using an unscented Kalman filter to
 simultaneously fit the local model and propagate in the most consistent
-direction.  Further, we modify the Kalman filter to enforce model, i.e., positive eigenvalues and convex weights.  Despite the
+direction.  Further, we modify the Kalman filter to enforce the model, i.e., positive eigenvalues and convex weights.  Despite the
 presence of noise and uncertainty, this provides a causal estimate of the
 local structure at each point along the fiber.
@@ Line 18: / Line 18: @@
 Synthetic experiments demonstrate that this approach significantly improves
 the angular resolution at crossings and branchings while consistently
-estimating the mixture weights.  ''In vivo'' experiments confirm the
+estimating the mixture weights. ''In vivo'' experiments confirm the
 ability to trace out fibers in areas known to contain such crossing and
 branching while providing inherent path regularization.

← Older revision		Revision as of 17:19, 13 May 2010
Line 21:		Line 21:
	ability to trace out fibers in areas known to contain such crossing and		ability to trace out fibers in areas known to contain such crossing and
	branching while providing inherent path regularization.		branching while providing inherent path regularization.
		+
		+
		+	[[Image:case01045_2T_tc.png\|700px\|thumb\|left\|]]