AHM2009:PNL - Revision history

Marianna at 17:33, 17 February 2009

2009-02-17T17:33:01Z

Marianna at 16:02, 17 February 2009

2009-02-17T16:02:35Z

Kubicki: /* Team */

2009-01-08T15:31:45Z

Team

Dougt at 22:40, 7 January 2009

2009-01-07T22:40:09Z

Kubicki: /* Detailed Information about the Pipeline */

2009-01-07T22:12:55Z

Detailed Information about the Pipeline

Kubicki: /* Overview */

2009-01-07T22:09:55Z

Overview

Kubicki: /* Overview */

2009-01-07T22:05:16Z

Overview

Kubicki: /* Detailed Information about the Pipeline */

2009-01-07T21:45:29Z

Detailed Information about the Pipeline

Kubicki: /* Overview */

2009-01-07T21:03:35Z

Overview

Kubicki at 20:57, 7 January 2009

2009-01-07T20:57:47Z

@@ Line 3: / Line 3: @@
 __NOTOC__
 ==PNL Roadmap Project==
 {|
-|[[Image:Helix.png|thumb|280x150px|Stochastic Tractography on helix phantom]]
+|[[Image:Helix.png|thumb|Stochastic Tractography on helix phantom]]
-|[[Image:Wmm.png|thumb|280x150px|White Matter Mask generated from phantom]]
+|[[Image:Wmm.png|thumb|White Matter Mask generated from phantom]]
-|[[Image:STArcuate.jpg|thumb|280x150px|Stochastic Tractography of Arcuate Fasciculus]]
+|[[Image:STArcuate.png|thumb|Stochastic Tractography of Arcuate Fasciculus]]
-|[[Image:Step1.png|thumb|380x250px|Stochastic Tractography Module]]
+|[[Image:Step1.png|thumb|56px|Stochastic Tractography Module]]
-|[[Image:Step2.png|thumb|180x120px|Stochastic Tractography Module]]
+|[[Image:Step2.png|thumb|152px|Stochastic Tractography Module]]
 |}
 == Overview ==
-;* What problem does the pipeline solve?
+;*What problem does the pipeline solve?
 Most tractography methods (Deterministic/Principal Diffusion Direction) estimate fibers by tracing the maximum direction of diffusion.
@@ Line 32: / Line 30: @@
 Functionality of Python Stochastic Tractography module in Slicer 3.0 includes:
-*Preprocessing:
+;*Preprocessing
 Reading DWI and ROI files in nhdr format
@@ Line 44: / Line 42: @@
 Producing diffusion indices (FA, Mode, Trace)
-*Creating probability maps (parameters involve number of tracts per seed, tract length, step size, stopping criteria)
+;*Creating probability maps (parameters involve number of tracts per seed, tract length, step size, stopping criteria)
 Probability maps can be saved as ROIs, and either used directly, or thresholded (at certain probability, step claimed by few publications to remove noise) in slicer to mask and compute average FA, Mode, Trace for entire connection. Diffusion indices can be also weighted by the probability of connection for each voxel.
@@ Line 68: / Line 66: @@
 {|
-|[[Image:PnllogoworkNEW.png|67px]]
+|[[Image:Logo_pnl2.png|75px]]
 |valign="top"| DBP: Marek Kubicki, MD, PhD, Sylvain Bouix, PhD, Julien De Siebenthal, PhD, Doug Terry, BS,
-PNL, Department of Psychiatry, BWH, Harvard Medical School, Boston, MA
+[http://pnl.bwh.harvard.edu/ Psychiatry Neuroimaging Laboratory]
 |-
 |[[Image:csail.jpg|75px]]
-|valign="top"|Core 1: Polina Golland, MIT
+|valign="top"|Core 1: Polina Golland, [http://www.csail.mit.edu/ CSAIL MIT]
 |-
 |[[Image: Kitware.png|75px]]
-|valign="top"| Core 2: Brad Davis, Steve Pieper, Kitware
+|valign="top"| Core 2: Brad Davis, Steve Pieper, [http://www.kitware.com/ Kitware]
 |}

@@ Line 66: / Line 66: @@
 ==Team==
-[[Image:PnllogoworkNEW.png|left|50px]]
-DBP: Marek Kubicki, MD, PhD, Sylvain Bouix, PhD, Julien De Siebenthal, PhD, Doug Terry, BS
+{|
 PNL, Department of Psychiatry, BWH, Harvard Medical School, Boston, MA
+|-
+|[[Image:csail.jpg|75px]]
+|valign="top"|Core 1: Polina Golland, MIT
+|-
-[[Image:csail.jpg|left|50px]]
+|[[Image: Kitware.png|75px]]
-Core 1: Polina Golland, MIT
+|valign="top"| Core 2: Brad Davis, Steve Pieper, Kitware
+|}
 ==Outreach==

← Older revision		Revision as of 15:31, 8 January 2009
Line 74:		Line 74:

	[[Image:csail.jpg\|left\|50px]]		[[Image:csail.jpg\|left\|50px]]
−	Core 1: Polina ~~Gollard~~, MIT	+	Core 1: Polina Golland, MIT

@@ Line 53: / Line 53: @@
 ==Software & documentation==
 * Download Slicer 3.0 [http://www.slicer.org/pages/Downloads here].
 *[[Media:IJdata.tar.gz|Training Dataset]]
-*[[Media:Slicer3STModule.ppt|Training Presentation]]
+:* Module documentation can be found here:
-*[[Media:SlicerSTtutorial.ogg|Training Webcast]] ([http://www.theora.org Theora format])
+:**[[Media:IJdata.tar.gz|Training Dataset]]
 ==Team==

@@ Line 33: / Line 33: @@
 Functionality of Python Stochastic Tractography module in Slicer 3.0 includes:
 *Preprocessing:
 Reading DWI and ROI files in nhdr format
 Smoothing DWI data
 Creating brain and white mater masks
 Removing artifacts in WM masks by comparing this mask with FA map
 Producing diffusion indices (FA, Mode, Trace)
 *Creating probability maps (parameters involve number of tracts per seed, tract length, step size, stopping criteria)

@@ Line 16: / Line 16: @@
 ;* What problem does the pipeline solve?
 Most tractography methods (Deterministic/Principal Diffusion Direction) estimate fibers by tracing the maximum direction of diffusion. A limitation of this approach is that, in practice, several factors introduce uncertainty in the tracking procedure, including, noise, splitting and crossing fibers, head motion and image artifacts.
 Stochastic tractography attempts to quantify the uncertainty associated with estimated fibers, by using a propagation model based on stochastics and regularization, which allows paths originating at one point to branch and return a probability distribution of possible paths.
 Based on probability functions, using a sequential importance sampling technique ([http://lmi.bwh.harvard.edu/papers/pdfs/2002/bjornemoMICCAI02.pdf Bjornemo et al., 2002]), one can generate thousands of fibers starting in the same point by sequentially drawing random step directions. This gives a very rich model of the fiber distribution, as contrasted with single fibers produced by conventional tractography methods. Moreover, from a large number of sampled paths, probability maps can be generated, providing better estimates of connectivity between several anatomical locations.
 ;* Who is the targeted user?

@@ Line 25: / Line 25: @@
 ==Detailed Information about the Pipeline==
 Stochastic Tractography pipeline has been written in Python, and is part of current Slicer3 release.
-Functionality of Python Stochastic Tractography module in Slicer 3.0
+Functionality of Python Stochastic Tractography module in Slicer 3.0 includes:
-* Loading volumes:
+*Preprocessing:
-Module reads files (DWI and ROIs) in nhdr format.
+Reading DWI and ROI files in nhdr format
-* Smoothing:
+Smoothing DWI data
-One can smooth the DWI data (only Gausian smoothing is supported at this time). We recommend it if the data is noisy.
+Creating brain and white mater masks
-* Masks:
+Removing artifacts in WM masks by comparing this mask with FA map
-Masks are removing some of the unwanted tracts, same way principal diffusion direction tractography uses stopping criteria for tractography termination. Three different methods for masking are provided.
+Producing diffusion indices (FA, Mode, Trace)
-:1. Otsu Mask- a thresholding method that is based on T2 intensity
+*Creating probability maps (parameters involve number of tracts per seed, tract length, step size, stopping criteria)
-Then, probability maps can be saved as ROIs, and either used directly, or thresholded (at certain probability, step claimed by few publications to remove noise) in slicer to mask and compute average FA, Mode, Trace for entire connection. Diffusion indices can be also weighted by the probability of connection for each voxel.
+Probability maps can be saved as ROIs, and either used directly, or thresholded (at certain probability, step claimed by few publications to remove noise) in slicer to mask and compute average FA, Mode, Trace for entire connection. Diffusion indices can be also weighted by the probability of connection for each voxel.
 ;* Parameters/steps that need to be adjusted using someone else's data
 ==Software & documentation==

@@ Line 22: / Line 22: @@
 Stochastic Tractography is also comparable, if not better, in defining large white matter fiber bundles, especially those traveling through white matter regions characterized by increased diffusion uncertainty (fiber crossings).
 ;* How does the pipeline compare to state of the art?
 To date, there are two popular toolboxes that include probabilistic/stochastic tractography modules. Functionality of both modules (FSL and PICO) is limited, and software is not open source.
 ==Detailed Information about the Pipeline==