User:Inorton/Slicer4:DTMRI Thoughts - Revision history

Inorton: Blanked the page

2018-07-13T20:16:59Z

Blanked the page

Inorton: /* Workflow */

2011-02-04T17:05:13Z

Workflow

Inorton: /* Workflow */

2011-02-03T20:53:42Z

Workflow

Inorton: /* Clinical perspective */

2011-02-03T20:34:24Z

Clinical perspective

Inorton: /* Clinical perspective */

2011-02-03T20:30:45Z

Clinical perspective

Inorton: /* Audiences */

2011-02-02T00:31:37Z

Audiences

Inorton: /* Workflow */

2011-01-24T03:48:37Z

Workflow

Inorton: /* Slicer improvement areas */

2011-01-11T18:18:55Z

Slicer improvement areas

Inorton: /* Audiences */

2011-01-11T18:10:57Z

Audiences

Inorton: /* Slicer disadvantages */

2011-01-11T18:04:17Z

Slicer disadvantages

← Older revision		Revision as of 20:16, 13 July 2018
Line 1:		Line 1:
−	~~=Clinical perspective=~~
−	We are using DTI in the context of 2-4 Neurosurgery planning cases per week. We use Slicer FiducialSeeding, tractography selection on a commercial neuronavigation program, and occasionally other Slicer DTI tools as case and surgeon requests dictate. We have used the Slicer3 FiducialSeeding functionality in the OR (with OpenIGTLink/BioImageSuite/BrainLab driving the seeding location).
−	~~==Workflow==~~
−	* Scan and push images to clinical PACS and our clinical fMRI processing workstation (has DICOM SCP and database capability)
−	* Pull/copy images from clinical PACS and processing workstation to research laptop/workstation
−	* Load all images:
−	** T1, T2, (sometimes CT, PET, CBV from perfusion MR, etc.)
−	** 2-4 thresholded fMRI activation volumes (coregistered and resliced to structural in SPM)
−	** DTI
−	* Preprocess DTI
−	** convert to NRRD (module: DicomToNRRDConverter)
−	** estimate tensors (module: Diffusion Tensor Estimation)
−	* Coregister all images (BrainsFit)
−	** DTI Baseline -> T2 (affine first, then rigid)
−	** T2 -> T1 (rigid)
−	** additional images as available
−	* Segment tumor or pathology region
−	** manually in editor with levelset selection or drawing tools
−	** semi-automatically in editor using GrowCutSegment
−	* Bring to clinician to use FiducialSeeding to explore area around tumor.
−	* (if requested, re-load DICOMs on clinical navigation suite and re-select tracts identified in Slicer)

−	~~=Background Thoughts=~~
−	~~==Audiences==~~
−	* Clinical/research end users: need simple, efficient, relatively intuitive workflow to generate tractography and perform selection and statistics operations.
−	* Clinical developers: integrate DTI functionality for domain-specific purposes (neurosurgery, neurology, etc.)
−	* Pipeline developers: the underlying implementations need to be abstracted sufficiently to allow creation of pipeline tools for large-study purposes.
−	* DTI Researchers:
−	** Can use Slicer+ipython+numpy+... instead of matlab and custom code. Advantages: image conversion (DICOM) and visualization taken care of by Slicer. Challenges: learning curve; the python suite is less integrated than matlab, but it's getting better; pure matlab is relatively more stable (mex stuff can introduce problems).
−	** Implementation of new algorithms in Slicer opens up larger potential userbase.
−
−	~~==Slicer advantages==~~
−	~~See big list of DTI software here: [[User:inorton/DTI_Software_List]].~~
−
−	~~There are several excellent DTI-centric applications. What advantages does Slicer have for DTI work?~~
−
−	* More user-friendly data loading: TrackVis requires command line preprocessing; MedInria and TrackVis require manual gradient entry; DTI studio is limited to ROI exploration only (as far as I know)
−	* Many segmentation options already available - no external tool (TrackVis, DTI Studio) or separate interface (MedInria) required.
−	* Already integrated with intra-operative systems via OpenIGTLink functionality
−	* Open-source license (TrackVis closed, MedInria non-commercial, DTI Studio closed)
−	* DicomToNRRDConverter test suite: validate DICOM loading from many different scanner types, with special emphasis on DTI private header information.
−
−	~~==Slicer improvement areas==~~
−	~~(this is referring to Slicer3 interactive DTMRI tools: these areas need improvement in Slicer 4)~~
−
−	* FiducialSeeding is too slow with high-density seeding, low step-values, or large seeding regions. This is probably intrinsic. Possible improvements include using a multi-threaded seeding filter, or using the GPU (?). But that may be superfluous: with good selection tools, dense pre-seeding in whole-brain or at least extended area of interest may be a better option.
−	* Current fiber data model is inefficient for interactive use on large sets (tens of thousands) of fiber tracts.
−	* Missing good interactive ROI selection, clustering, and editing capability for pre-computed fibersets.
−	* Need subset selection and coloring.
−	* Need interactive, user-friendly tract/bundle statistics generation.
−	* Labelmap seeding is not multi-threaded so whole-brain tractography takes.. longer than I would like.
−	* Disjointed interface: no one-stop integrated GUI for full DICOMs->tracts->measurements workflow.
−
−	~~=Existing NA-MIC resources=~~
−
−	* teem: the Slicer3 interactive DTI implementation uses teem through the vtkTeem libraries.
−	** http://teem.sourceforge.net/
−	* GTRACT: command-line tools for DTI pipeline processing
−	** http://www.nitrc.org/projects/vmagnotta/
−	** http://wiki.slicer.org/slicerWiki/index.php/GTRACT_V4
−	* UNC DTI Tools (FiberViewer):
−	** http://www.niral.unc.edu/
−	** http://www.ia.unc.edu/dev/download/index.htm
−
−	~~=Existing open-source external resources=~~
−	~~See big list of DTI software here: [[User:inorton/DTI_Software_List]]~~
−	* (BSD-like licenses)
−	* DiPy: diffusion imaging in python. Currently pre/alpha but in active development. (BSD license)
−	** http://nipy.sourceforge.net/dipy/
−	* DTI-query and CINCH: (BSD license)
−	** http://graphics.stanford.edu/projects/dti/

@@ Line 9: / Line 9: @@
 ** DTI
 * Preprocess DTI
-** convert to NRRD
+** convert to NRRD (module: DicomToNRRDConverter)
-** estimate tensors
+** estimate tensors (module: Diffusion Tensor Estimation)
-* Coregister all images using BrainsFit
+* Coregister all images (BrainsFit)
-** DTI Baseline -> T2
+** DTI Baseline -> T2 (affine first, then rigid)
-** T2 -> T1
+** T2 -> T1 (rigid)
 ** additional images as available
 * Segment tumor or pathology region

@@ Line 2: / Line 2: @@
 We are using DTI in the context of 2-4 Neurosurgery planning cases per week. We use Slicer FiducialSeeding, tractography selection on a commercial neuronavigation program, and occasionally other Slicer DTI tools as case and surgeon requests dictate. We have used the Slicer3 FiducialSeeding functionality in the OR (with OpenIGTLink/BioImageSuite/BrainLab driving the seeding location).
 ==Workflow==
-* Scan and push images to clinical PACS and our clinical fMRI processing workstation (has SCP and dicom db capability)
+* Scan and push images to clinical PACS and our clinical fMRI processing workstation (has DICOM SCP and database capability)
 * Pull/copy images from clinical PACS and processing workstation to research laptop/workstation
 * Load all images:
@@ Line 8: / Line 8: @@
 ** 2-4 thresholded fMRI activation volumes (coregistered and resliced to structural in SPM)
 ** DTI
-* Preprocess DTI (convert to NRRD, estimate tensors)
+* Preprocess DTI
 * Coregister all images using BrainsFit
 * Segment tumor or pathology region
 * Bring to clinician to use FiducialSeeding to explore area around tumor.
 * (if requested, re-load DICOMs on clinical navigation suite and re-select tracts identified in Slicer)

@@ Line 1: / Line 1: @@
 =Clinical perspective=
-We are using DTI in the context of 2-4 Neurosurgery planning cases per week. We use Slicer FiducialSeeding, BrainLab tractography selection, and occasionally other Slicer DTI tools as case and surgeon requests dictate. We have used the Slicer3 FiducialSeeding functionality in the OR (with OpenIGTLink/BioImageSuite/BrainLab driving the seeding location).
+We are using DTI in the context of 2-4 Neurosurgery planning cases per week. We use Slicer FiducialSeeding, tractography selection on a commercial neuronavigation program, and occasionally other Slicer DTI tools as case and surgeon requests dictate. We have used the Slicer3 FiducialSeeding functionality in the OR (with OpenIGTLink/BioImageSuite/BrainLab driving the seeding location).
 ==Workflow==
 * Scan and push images to clinical PACS and our clinical fMRI processing workstation (has SCP and dicom db capability)

@@ Line 1: / Line 1: @@
 =Clinical perspective=
-We are using Slicer3 for 2-4 Neurosurgery planning cases per week. We have used the Slicer3 FiducialSeeding functionality in the OR (with OpenIGTLink/BioImageSuite/BrainLab driving the seeding location).
+We are using DTI in the context of 2-4 Neurosurgery planning cases per week. We use Slicer FiducialSeeding, BrainLab tractography selection, and occasionally other Slicer DTI tools as case and surgeon requests dictate. We have used the Slicer3 FiducialSeeding functionality in the OR (with OpenIGTLink/BioImageSuite/BrainLab driving the seeding location).
 ==Workflow==
 * Scan and push images to clinical PACS and our clinical fMRI processing workstation (has SCP and dicom db capability)

@@ Line 20: / Line 20: @@
 * Pipeline developers: the underlying implementations need to be abstracted sufficiently to allow creation of pipeline tools for large-study purposes.
 * DTI Researchers:
-** Could use Slicer+ipython+numpy+... instead of matlab and custom code. Advantages: data-reading and visualization boilerplate code already exists. Challenges: learning curve; the python suite is less integrated than matlab, but it's getting better; pure matlab is relatively more stable (mex stuff can introduce problems).
+** Can use Slicer+ipython+numpy+... instead of matlab and custom code. Advantages: image conversion (DICOM) and visualization taken care of by Slicer. Challenges: learning curve; the python suite is less integrated than matlab, but it's getting better; pure matlab is relatively more stable (mex stuff can introduce problems).
 ** Implementation of new algorithms in Slicer opens up larger potential userbase.

@@ Line 41: / Line 41: @@
 * Missing good interactive ROI selection, clustering, and editing capability for pre-computed fibersets.
 * Need subset selection and coloring.
 * Labelmap seeding is not multi-threaded so whole-brain tractography takes.. longer than I would like.
 * Disjointed interface: no one-stop integrated GUI for full DICOMs->tracts->measurements workflow.

@@ Line 17: / Line 17: @@
 ==Audiences==
 * Clinical/research end users: need simple, efficient, relatively intuitive workflow to generate tractography and perform selection and statistics operations.
 * Clinical developers: integrate DTI functionality for domain-specific purposes (neurosurgery, neurology, etc.)
 * DTI Researchers:
-** Could use Slicer+ipython+numpy+... instead of matlab and custom code. Advantages: data-reading and visualization boilerplate code already exists. Challenges: learning curve; the python suite is less integrated than matlab, but it's getting better; relative stability: matlab rarely crashes.
+** Could use Slicer+ipython+numpy+... instead of matlab and custom code. Advantages: data-reading and visualization boilerplate code already exists. Challenges: learning curve; the python suite is less integrated than matlab, but it's getting better; pure matlab is relatively more stable (mex stuff can introduce problems).
 ** Implementation of new algorithms in Slicer opens up larger potential userbase.

@@ Line 34: / Line 34: @@
 * DicomToNRRDConverter test suite: validate DICOM loading from many different scanner types, with special emphasis on DTI private header information.
-==Slicer disadvantages==
+==Slicer improvement areas==
 (this is referring to Slicer3 interactive DTMRI tools: these areas need improvement in Slicer 4)
 * Current fiber data model is inefficient for interactive use on large sets (tens of thousands) of fiber tracts.
 * Missing good interactive ROI selection, clustering, and editing capability for pre-computed fibersets.
 * Need subset selection and coloring.
-* Labelmap seeding is not multi-threaded so whole-brain tractography takes forever.
+* Labelmap seeding is not multi-threaded so whole-brain tractography takes.. longer than I would like.
 * Disjointed interface: no one-stop integrated GUI for full DICOMs->tracts->measurements workflow.