NAMIC Wiki - User contributions [en]

Slicer3:Stochastic tractography

2010-06-14T23:14:28Z

Rauscha:

You can find the end-to-end tutorial and the sample dataset for running stochastic tractography here.

Tutorial ([[Media:Stochastic Tractography TutorialContestSummer2010.pdf|PDF - 1MB]])

Tutorial ([[Media:Stochastic_Tractography_TutorialContestSummer2010.ppt|PPT - 1.2MB]])

Dataset ([[Media:Stochastic_tutorial_data_TutorialContestSummer2010.zip|ZIP - 92MB]])

[http://wiki.na-mic.org/Wiki/index.php/Summer_2010_Tutorial_Contest Back to tutorial contest]

File:Stochastic tutorial data TutorialContestSummer2010.zip

2010-06-14T23:13:16Z

Rauscha: Stochastic Tractography tutorial dataset from June 2010 -Andrew Rausch rauscha@bwh.harvard.edu

Stochastic Tractography tutorial dataset from June 2010
-Andrew Rausch rauscha@bwh.harvard.edu

File:Stochastic Tractography TutorialContestSummer2010.ppt

2010-06-14T23:11:59Z

Rauscha: Stochastic Tractography tutorial from June 2010 -Andrew Rausch rauscha@bwh.harvard.edu

Stochastic Tractography tutorial from June 2010
-Andrew Rausch rauscha@bwh.harvard.edu

File:Stochastic Tractography TutorialContestSummer2010.pdf

2010-06-14T23:10:27Z

Rauscha: Stochastic Tractography tutorial from June 2010 -Andrew Rausch rauscha@bwh.harvard.edu

Stochastic Tractography tutorial from June 2010
-Andrew Rausch rauscha@bwh.harvard.edu

Slicer3:Stochastic tractography

2010-06-14T23:05:46Z

Rauscha:

You can find the end-to-end tutorial and the sample dataset for running stochastic tractography here.

Tutorial ([[Media:Stochastic_Tractography_JUN2010.pdf|PDF - 1MB]])

Tutorial ([[Media:Stochastic_Tractography_JUN2010.ppt|PPT - 1.2MB]])

Dataset ([[Media:Stochastic_tutorial_data.zip|ZIP - 92MB]])

[http://wiki.na-mic.org/Wiki/index.php/Summer_2010_Tutorial_Contest Back to tutorial contest]

File:Stochastic Tractography JUN2010.ppt

2010-06-14T23:05:38Z

Rauscha: Stochastic Tractography tutorial from June 2010

Stochastic Tractography tutorial from June 2010

File:Stochastic Tractography JUN2010.pdf

2010-06-14T23:03:55Z

Rauscha: Tutorial for Stochastic Tractography created June 2010

Tutorial for Stochastic Tractography created June 2010

Slicer3:Stochastic tractography

2010-06-14T22:41:51Z

Rauscha:

You can find the end-to-end tutorial and the sample dataset for running stochastic tractography here.

Tutorial (PDF)

Tutorial (PowerPoint)

Dataset ([[Media:Stochastic_tutorial_data.zip|ZIP - 92MB]])

[http://wiki.na-mic.org/Wiki/index.php/Summer_2010_Tutorial_Contest Back to tutorial contest]

Slicer3:Stochastic tractography

2010-06-14T22:41:35Z

Rauscha:

You can find the end-to-end tutorial and the sample dataset for running stochastic tractography here.

Tutorial (PDF)

Tutorial (PowerPoint)

Dataset ([[Media:Stochastic_tutorial_data.zip|ZIP]])

[http://wiki.na-mic.org/Wiki/index.php/Summer_2010_Tutorial_Contest Back to tutorial contest]

Slicer3:Stochastic tractography

2010-06-14T22:41:26Z

Rauscha:

You can find the end-to-end tutorial and the sample dataset for running stochastic tractography here.

Tutorial (PDF)

Tutorial (PowerPoint)

Dataset ([[Media:Stochastic_tutorial_data.zip| ZIP]])

[http://wiki.na-mic.org/Wiki/index.php/Summer_2010_Tutorial_Contest Back to tutorial contest]

Slicer3:Stochastic tractography

2010-06-14T22:41:07Z

Rauscha:

You can find the end-to-end tutorial and the sample dataset for running stochastic tractography here.

Tutorial (PDF)

Tutorial (PowerPoint)

Dataset ([[File:Stochastic_tutorial_data.zip| ZIP]])

[http://wiki.na-mic.org/Wiki/index.php/Summer_2010_Tutorial_Contest Back to tutorial contest]

File:Stochastic tutorial data.zip

2010-06-14T22:39:22Z

Rauscha: Dataset for the June 2010 Stochastic Tractography tutorial

Dataset for the June 2010 Stochastic Tractography tutorial

Slicer3:Stochastic tractography

2010-06-14T22:19:19Z

Rauscha: Created page with 'You can find the end-to-end tutorial and the sample dataset for running stochastic tractography here. Tutorial (PDF) Tutorial (PowerPoint) Dataset (ZIP)'

You can find the end-to-end tutorial and the sample dataset for running stochastic tractography here.

Tutorial (PDF)

Tutorial (PowerPoint)

Dataset (ZIP)

Summer 2010 Tutorial Contest

2010-06-14T22:17:29Z

Rauscha: /* List of submitted tutorials */

'''Organizational Telephone Call May 13th at 3pm Eastern Time (part of standard NA-MIC Engineering tcon)'''

=Background=

[http://www.slicer.org Slicer3] is used to perform meaningful research tasks. As part of the NA-MIC Training Core activities we are building a curated portfolio of tutorials for the basic functions and specialized functionality available in Slicer. Our current portfolio of tutorials as well as tutorials that were developed in past contests are posted on the [http://www.slicer.org/slicerWiki/index.php/Slicer3.4:Training#Software_tutorials| NA-MIC training compendium].

=Tutorial Contest Goal=
The primary purpose of this contest is to enrich the training materials that are available to end-users and developers using 3D Slicer and the NA-MIC kit. We believe contestants will be motivated to participate to enhance the dissemination of their own algorithms that they have incorporated into the Slicer3 platform and/or to enhance training of Slicer3 functionality for their own laboratory groups.

There will be three categories:
#'''END TO END SOLUTION TUTORIAL:''' In this category, the tutorial will teach a user how to solve a particular clinical problem using the NA-MIC Kit. Entries into this category will require at least:
#*materials about the scientific and application background and motivation,
#*step-by-step guides, and
#*sample data
#*Examples: [[Media:ARCTIC-Slicer3-Tutorial.pdf|‏ ARCTIC (Automatic Regional Cortical Thickness) Tutorial]] , [[IGT:ToolKit/Neurosurgical-Planning|Neurosurgical Planning for Image Guided Therapy using Slicer3]]
#'''ALGORITHM TUTORIAL:''' In this category the tutorial will teach a user how to make an algorithm work on their data. Entries into this category will require at least:
#*materials about the scientific and application background of the algorithm(s) and their use in the Slicer environment
#*step-by-step guides, and
#*at least two different sample data sets from two different institutions
#*Examples: [[media:EMSegment_TrainingTutorial.pdf| Non-human Primates Segmentation Tutorial]], [[Media:AutomaticSegmentation_SoniaPujol_Munich2008.ppt|Automatic Segmentation Tutorial ]]
# '''METHODOLOGY TUTORIAL'''. Application-level tutorials for users and developers.
<br>

=Rules=
*Tutorial must be based on the '''Slicer3.6 release'''
*To enter the contest, you must provide '''a version of the tutorial that works on at least one platform'''.
*Tutorial and all of its components (data, powerpoints/pdfs, additional modules etc.) must be released under the [http://www.slicer.org/slicerWiki/index.php/Slicer:license Slicer license]
*Tutorial must include contact information of the primary author (e-mail and phone number)
*Tutorial must follow the guidelines specified above and use the [[Media:TutorialContest_Template.ppt‎| tutorial template]].
*If applicable, the tutorial must provide clear directions for downloading and installing additional modules
*Applicants must agree to work with the NA-MIC Training and Dissemination Cores to curate their submission.

=Submission Dead-line and Presentation=

* '''<span style="background-color: yellow"> Submission dead-line: Monday June 14, 2010'''</span>
* Presentation: all tutorials will be presented by the authors during the Summer 2010 Project Week, on '''Tuesday June 22 from 3:00 pm to 5:30 pm'''. Each tutorial presentation should be 10 minutes long.
* If you wish to participate in the contest, please follow the three steps below:
**1. Create a wiki page for your tutorial
**2. Upload your slides and tutorial dataset. Your tutorial and data must be named as 'TutorialName_TutorialContestSummer2010.pdf' and 'TutorialData_TutorialContestSummer2010.zip'
**3. Add a link to the uploaded tutorial and datasets on your tutorial page.
**4. Add a link to your tutorial page in the list below.

= List of submitted tutorials=

[[Slicer3:Fiducials_TutorialContestSummer2010 | Fiducials]]

[[Slicer3:RSS_TutorialContestSummer2010 | RSS (Robust Statistics Segmenter)]]

[[Slicer3:Automatic SPHARM Shape Analysis in 3D Slicer_TutorialContestSummer2010 | Automatic SPHARM Shape Analysis in 3D Slicer]]

[[Slicer3:LabelFusion_TutorialContestSummer2010| Atlas Label Fusion & Surface Registration]]

[[Slicer3:Stochastic_tractography| Stochastic Tractography]]

DBP2:Harvard:Brain Segmentation Roadmap

2010-02-05T16:24:05Z

Rauscha: /* Module */

Back to [[NA-MIC_Internal_Collaborations|NA-MIC Collaborations]], [[DBP2:Harvard|Harvard DBP 2]]
__NOTOC__
=Stochastic Tractography for VCFS=
== Roadmap ==

The main goal of this project is to develop end-to-end application that would be used to characterize anatomical connectivity abnormalities in the brain of patients with velocardiofacial syndrome (VCFS), and to link this information with deficits in schizophrenia. This page describes the technology roadmap for stochastic tractography, using newly acquired 3T data, NAMIC tools and slicer 3.

== Algorithm ==

[[Image:IC_sto_new.png|thumb|right|200px|<font size=1> Figure 1: Comparison of deterministic and stochastic tractography algorithms</font>]]
; A-Description
* Most tractography methods 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. To address this uncertainty, stochastic tractography methods have been developed to quantify the uncertainty associated with estimated fibers (Bjornemo et al., 2002). Method uses 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. The method utilizes principles of a statistical Monte Carlo method called Sequential Importance Sampling and Resampling (SISR). 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. A comparison of the algorithms can be seen here. (Figure 1)

[[Image:StochasticPic.PNG|thumb|right|200px|<font size=1>Figure 2: Stochastic tractography of uncinate fasciculis on anatomical data (left) and cingulum bungle on fMRI scan (right)</font>]]
; B-Possible Applications
* Since diffusion direction uncertainty within the gray matter is quite significant; principal diffusion direction approaches usually do not work for tracking between two gray matter regions. Thus if one requires finding connections between a priori selected anatomical gray matter regions, defined either by anatomical segmentations (in case of using structural ROI data), or functional activations (in case of megring DTI with fMRI), stochastic tractography seems to be the method of choice. Here is an example of this application to anatomical data (Figure 2, left image) and to fMRI data (Figure 2, right image).

* 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). Example of such application to internal capsule. (Figure 3)

[[Image:IC-comp-new.png|thumb|right|200px|<font size=1>Figure 3: Streamline vs. stochastic tractography of the Internal Capsule</font>]]
; C-References

* [http://lmi.bwh.harvard.edu/papers/pdfs/2002/bjornemoMICCAI02.pdf Björnemo M, Brun A, Kikinis R, Westin CF. Regularized stochastic white matter tractography using diffusion tensor MRI. In Fifth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'02). Tokyo, Japan, 2002;435-442.]
* [http://lmi.bwh.harvard.edu/papers/pdfs/2006/frimanTMI06.pdf Friman, O., Farneback, G., Westin CF. A Bayesian Approach for Stochastic White Matter Tractography. IEEE Transactions on Medical Imaging, Vol 25, No. 8, Aug. 2006]

[[Image:StochasticGUI1.PNG|thumb|right|200px|<font size=1> Figure 4: Python Stochastic Tractography GUI </font>]]

==Module==
Can be found in: MODULES > PYTHON MODULES > PYTHON STOCHASTIC TRACTOGRAPHY
;Functionality of Python Stochastic Tractography module in Slicer 3.0
* IO:
Module reads files (DWI and ROIs) in nhdr format.
* Smoothing:
One can smooth the DWI data (only Gausian smoothing is supported at this time). We recommend it if the data is noisy.
* Brain Mask:
The Brain mask defines the volume in which the tensor will be computed and the tracts evaluated. If Enabled, will use threshold values on the baseline instead of WM Mask defined in IO panel.
*IJK/RAS Switch
Chooses the way the nhdrs are read.
*Diffusion Tensor:
This step allows output of the tensor image and can output anisotropy indices (FA/Mode/Trace)
*Tractography:
Parameters that need to be adjusted:
:1. Total Tracts: The amount of tracts that will be seeded from each voxel (we recommend between 500 and 1000 tracts, depending on the workstation power- 1000 tracts per voxel seeded within the large ROI for high resolution DWI can take a long time to compute).
:2. Maximum tract length: (in mm) This can eliminate long, unwanted tracts if the regions for which connection is measured are located close to each other
:3. Step Size(mm): distance between each re-estimation of tensors, usually between 0.5 and 1 mm. Adjust to make sure step size is not larger than the voxel spacing in any direction, which would allow voxels to be "jumped over."
:4. Stopping criteria: This can be used on the top of WM mask to terminate tracts when FA drops below supplied threshold (in case they frequently travel through CSF, for example).
:5. Use Basic Method: switches between Friman and McGraw algorithms.
*Connectivity Map:
This step creates output probability maps.
:1. binary: each voxel is counted only once if at least one fiber pass through it
:2. cumulative: tracts are summed by voxel independently
:3. weighted: tracts are summed by voxel depending on their length
*Length Based:
This step will output only either the shortest 1/3, middle 1/3, or longest 1/3 of the tracts.
*Threshold
This step will reject tracts whose endpoints are lower than the threshold value.
*Spherical ROI vicinity
This will make the ROI a sphere based on the ROI’s center of gravity (with the sphere’s radius being the distance from the center to the ROI’s furthest point). This sphere can be inflated by raising the Vicinity level to the number of steps you’d like to increase the ROI’s size by.
*Vicinity
This step traces back n number of steps from tract endpoint to check if track crosses target ROI. If so, tract is included.

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.

:* Module documentation can be found here:
:**[[Media:IJdata.tar.gz|Training Dataset]]
:**[[Media:Stochastic_June09_1.ppt|Training Presentation]]
:**[[Media:Helix.zip|Sample Helix Dataset]]

==Work Accomplished==
[[Image:helix_withsmoothing.png|thumb|right|200px|<font size=1>Figure 6: Stochastic tractography from a single ROI on helix phantom</font>]]
; A - Optimization and testing of stochastic tractography algorithm :
:* Original methodological paper, as well as our first attempts to use the algorithm (CC+ and matlab scripts) have been done on old "NAMIC" 1.5T LSDI data ([https://portal.nbirn.net:443/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=gotoDirectory&CPq_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FHarvard%2Farchive_morph&up=CPq&JavaScript=enabled Structural MRI] and [https://portal.nbirn.net:443/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=gotoDirectory&up=7li&7li_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FHarvard%2Farchive_diffusion&JavaScript=enabled DTI data]).
:* Tri worked hard on making sure algorithm works on new high resolution 3T data (available here: [https://portal.nbirn.net/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=moveUpDir&gv7_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FPNL%2F3T_strct_dti_fmri%2Fcase01017&up=gv7&JavaScript=enabled|PNL 3T Data]).
:* Tests have been done also on the spiral diffusion phantom, to make sure diffusion directions and scanner coordinates are handled properly by the algorithm (Figure 6).

; B - Clinical Applications

:* Algorithm was used to trace and analyze anterior limb of the internal capsule on 1.5T data. It generated reacher representation of frontal fiber projections, it also turned out to be more sensitive to group differences in white matter integrity that conventional deterministic tractography (see Figure 6).
:* Algorithm was also used to trace smaller white matter fiber tracts, such as Cingulum, Fornix, Uncinate Fasciculus, Arcuate Fasciculus on 3T "Santa Fe" dataset (http://www.na-mic.org/Wiki/index.php/SanteFe.Tractography.Conference)

; C - References

:* [http://www.na-mic.org/Wiki/index.php/Image:IC_posternew.png Shenton, M.E., Ngo, T., Rosenberger, G., Westin, C.F., Levitt, J.J., McCarley, R.W., Kubicki, M. Study of Thalamo-Cortical White Matter Fiber Tract Projections in Schizophrenia Using Diffusion Stochastic Tractography. Poster presented at the 46th Meeting of the American College of Neuropsychopharmacology, Boca Raton, FL, December 2007.]
:* [http://www.na-mic.org/Wiki/index.php/Media:dougt_poster.pdf Terry DP, Rausch AC, Alvarado JL, Melonakos ED, Markant D, Westin CF, Kikinis R, de Siebenthal J, Shenton ME, Kubicki M. White Matter Properties of Emotion Related Connections in Schizophrenia. Poster presented at the 2009 Mysell Poster Day, Dept. of Psychiatry, Harvard Medical School, April 2009]
:* [http://www.na-mic.org/Wiki/index.php/Media:Jorge_poster.pdf Alvarado JL, Terry DP, Markant D, Ngo T, Kikinis R, Westin CF, McCarley RW, Shenton ME, Kubicki M. Study of Language-Related White Matter Tract Connections in Schizophrenia using Diffusion Stochastic Tractography. Poster presented at the 2009 Mysell Poster Day, Dept. of Psychiatry, Harvard Medical School, April 2009]
:* Melonakos ED, Shenton ME, Markant D, Alvarado J, Westin CF, Kubicki M. White Matter Properties of Orbitofrontal Connections in Schizophrenia. Poster being presented at the 64th Meeting of the Society of Biological Psychiatry. Vancouver, BC. May 2009.
:* Kubicki, M. Khan, U., Bobrow, L., O'Donnell, L. Pieper, S. Westin, CF., Shenton, ME. New Methods for Assessing Whole Brain DTI Abnormalities in Schizophrenia. Presentation given at the International Congress of World Psychiatric Association. Florence, Italy. April 2009.
:* Kubicki, M., Markant, D., Ngo, T., Westin, CF., McCarley, RW., Shenton, ME. Study of Language Related White Matter Fiber Tract Projections in Schizophrenia Using Diffusion Stochastic Tractography. Presentation given at the International Congress of World Psychiatric Association. Florence, Italy. April 2009.

== Work in Progress ==

; A - Optimization and Testing of stochastic tractography module :
:* Julien is testing now separate components of work flow, including the masks, and impact of their precision on stochastic output, as well as impact of number of seeding points on tractography results.
:* At the same time, we are testing the module on a Max Plank dataset of Anna Rotarska-Jagiela by looking at the connections through the corpus callosum of the left auditory cortex to the right auditory cortex as defined by fMRI activation maps as well as running this data for the tractography comparison project.
:* We are discussing possibility of adding project specific functionality to module, such as third ROI to guide tractography, nonlinear registration button for merging fMRI and DTI data.

; B - Related Clinical Projects

;* Arcuate Fasciculus Extraction Project
[[Image:STArcuate.jpg|thumb|right|200px|<font size=1>Figure 7: The arcuate fasciculus including seed, midpoint and target ROI's.</font>]]
We have started the project of investigating Arcuate Fasciculus using Stochastic Tractography (Figure 7). This structure is especially important in both VCFS and schizophrenia, as it connects language related areas (Brocka and Wernicke's), and is involved in language processing quite disturbed in schizophrenia patients. It also can not be reliably traced using deterministic tractography.
:Project involves:
:* Whole brain segmentation, and automatic extraction of regions interconnected by Arcuate Fasciculus (Inferior frontal and Superior Temporal Gyri).
:* White matter segmentation, in order to prevent algorithm from traveling through the ventricles, where diffusivity is high.
:* Non-linear registration of labelmaps to the DTI space.
:* Seeding tracts. We have piloted it using 5000 seeds per voxel, however it is quite time consuming running it on even most powerful computers in the lab, so we have experimented with smaller number of seeds per voxel. We tested 1000 seeds, which gave virtually identical results.
:* Extracting path of interest, and calculating FA along the path for group comparison. Presentation of previous results for 7 schizophrenics and 12 control subjects, can be found here: [[Media:NAMIC_AHM_Arcuate.ppt|Progress Report Presentation]].
:* Results presentation and paper submission. Abstract was accepted and results presented at the World Biological Psychiatry Symposium in Venice, Italy. Paper is in preparation.

[[Image:connectivity1.jpg|thumb|right|200px|<font size=1>Figure 8: Maps of inferior frontal cortex (Broca) connectivity in controls and patients with schizophrenia.</font>]]

;* Semantic Network Connectivity Project
We use combination of fMRI and DTI data to define and characterize functional and anatomical connectivity within the semantic processing network in schizophrenia.
:Project involves:
:* fMRI data analysis and identification of functional nodes involved in semantic processing in healthy controls and seubjects with schizophrenia
:* Analysis of functional connectivity (using FSL) between nodes of semantic network
:* Whole brain Voxel Based analysis of DTI data in same population
:* Use of stochastic tractography to identify connections between functional nodes
:* Correlational analysis involving anatomical and functional connectivity data.
:* Results presentation and paper submission. Data was presented at HBM conference in 2007, paper has been submitted to HBM.
:* <font color="red">'''New: '''</font>June 10th 2009, paper accepted for publication in Human Brain Mapping: "Functional and Anatomical Connectivity Abnormalities in Left Inferior Frontal Gyrus in Schizophrenia" by Jeong, Wible, Hashimoto and Kubicki, HBM in Press

[[Image:ScreenshotFreeSurferDeepMatterSagitalView-vcase1-2009-06-12.jpg|thumb|right|200px|<font size=1>Figure 9: Brain automatic segmentation of subject with VCFS.</font>]]

;* Anatomical Connectivity Abnormalities in VCFS
We use combination of structural MRI and DTI to investigate anatomical abnormalities that would characterize patients with VCFS, and relationship of these abnormalities to those observed in schizophrenia.
:Project involves:
:* Whole brain, automatic segmentation of brain images obtained from patients with VCFS in order to identify structures involved in this disease.
:* Registration between anatomical and DTI scans (manual skull stripping followed by linear followed by nonlinear registration of SPGRs to DTI space).
:* Use of stochastic tractography to identify connections between gray matter regions identified in the disease.
:* Extracting paths of interest, and calculating FA along the paths for group comparison.
:* Correlational analysis involving anatomical and connectivity data, clinical information and genetic data.
:* Results presentation and paper submission. First step of data analysis has been already accomplished, we are in the process of registering structural and DTI data and preparing DTI data for stochastic tractography analysis. We plan to submit an abstract with study results for Biological Psychiatry symposium (dedline December 2009).

[[Image:Anna.png|thumb|right|200px|<font size=1>Figure 10: Max Plank data showing tracts through the corpus connecting 2 cortical ROIs defined by fMRI activations.</font>]]

;* Study of Default Network
We have started collaboration with Department of Neurophysiology Max Planck Institute in Frankfurt (Anna Rotarska contact person). They have dataset containing DTI and resting state fMRI in schizophrenia, and want to use stochastic tractography to measure integrity of anatomical connections within the default network in schizophrenia. Their fMRI data has been co-registered with anatomical scans, and has been put it into DTI space. Figure 8 shows pilot data of the white matter connections through the corpus callosum between the left and the right auditory cortex ROIs as defined by fMRI data. This data will be also used to test robustness of our module, since data was collected on a different scanner (3T Philips).

;* Tractography Comparison Project
We are also working on a [http://www.na-mic.org/Wiki/index.php/SanteFe.Tractography.Conference tractography comparison project]dataset, where we apply stochastic tractography to phantom, as well as test dataset.

===Staffing Plan===

* Sylvain and Yogesh are the DBP resources charged with adapting the tools in the NA-MIC Kit to the DBP needs
* Doug Markant, our NAMIC RA has left the lab, and now Doug Terry, is a new NAMIC RA.
* Julien is our new NAMIC software engineer. He is responsible for improving the STM (Stochastic Tractography Module), and making sure software works with STM compliant datasets.
[[Link Progress| Development Progress]]
* Polina is the algorithm core contact
* Brad is the engineering core contact

===Schedule===

* '''10/2007''' - Optimization of Stochastic Tractography algorythm for 1.5T data.
* '''10/2007''' - Algorythm testing on Santa Fe data set and diffusion phantom.
* '''06/2008''' - Optimization of Stochastic Tractography algorythm for 3T data.
* '''11/2008''' - Slicer 3 module prototype using python.
* '''12/2008''' - Slicer 3 module official release
* '''12/2008''' - Documentation and packaging for dissemination.
* '''12/2008''' - First clinical application of stochastic tractography module.
* '''01/2009''' - First draft of the clinical paper.
* '''05/2009''' - Distortion correction and nonlinear registration added to the module
* '''05/2009''' - Symposium on tractography, including stochastic methods at World Biological Psychiatry Symposium in Florence, Italy.
* '''05/2009''' - Presentation of Arcuate Fasciculus findings at World Biological Psychiatry Symposium in Florence, Italy.
* '''07/2009''' - Summer Programming week- work on optimizing and speeding up data processing, releasing second generation of software that includes preprocessing pipeline.
* '''07/2009''' - Continue working on clinical collaborative studies using stochastic tractography module.
* '''12/2009''' - Submission of abstracts reporting findings of several clinical studies involving stochastic tractography, including anatomical connectivity abnormalities in patients with VCFS and anatomical and functional connectivity abnormalities in schizophrenia.

===Team and Institute===
*PI: Marek Kubicki (kubicki at bwh.harvard.edu)
*DBP2 Investigators: Sylvain Bouix, Yogesh Rathi, Julien de Siebenthal
*NA-MIC Engineering Contact: Brad Davis, Kitware
*NA-MIC Algorithms Contact: Polina Gollard, MIT

===Publications===

''In print''

*[http://www.na-mic.org/pages/Special:Publications?text=Kubicki+AND+Westin+AND+DTI&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database - Clinical Applications]

*[http://www.na-mic.org/pages/Special:Publications?text=Ngo+AND+Golland&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database - Algorithms Development]

[[Category: Schizophrenia]] [[Category: Diffusion MRI]] [[Category: Segmentation]]

DBP2:Harvard:Brain Segmentation Roadmap

2010-02-05T16:05:59Z

Rauscha: /* Module */

Back to [[NA-MIC_Internal_Collaborations|NA-MIC Collaborations]], [[DBP2:Harvard|Harvard DBP 2]]
__NOTOC__
=Stochastic Tractography for VCFS=
== Roadmap ==

The main goal of this project is to develop end-to-end application that would be used to characterize anatomical connectivity abnormalities in the brain of patients with velocardiofacial syndrome (VCFS), and to link this information with deficits in schizophrenia. This page describes the technology roadmap for stochastic tractography, using newly acquired 3T data, NAMIC tools and slicer 3.

== Algorithm ==

[[Image:IC_sto_new.png|thumb|right|200px|<font size=1> Figure 1: Comparison of deterministic and stochastic tractography algorithms</font>]]
; A-Description
* Most tractography methods 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. To address this uncertainty, stochastic tractography methods have been developed to quantify the uncertainty associated with estimated fibers (Bjornemo et al., 2002). Method uses 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. The method utilizes principles of a statistical Monte Carlo method called Sequential Importance Sampling and Resampling (SISR). 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. A comparison of the algorithms can be seen here. (Figure 1)

[[Image:StochasticPic.PNG|thumb|right|200px|<font size=1>Figure 2: Stochastic tractography of uncinate fasciculis on anatomical data (left) and cingulum bungle on fMRI scan (right)</font>]]
; B-Possible Applications
* Since diffusion direction uncertainty within the gray matter is quite significant; principal diffusion direction approaches usually do not work for tracking between two gray matter regions. Thus if one requires finding connections between a priori selected anatomical gray matter regions, defined either by anatomical segmentations (in case of using structural ROI data), or functional activations (in case of megring DTI with fMRI), stochastic tractography seems to be the method of choice. Here is an example of this application to anatomical data (Figure 2, left image) and to fMRI data (Figure 2, right image).

* 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). Example of such application to internal capsule. (Figure 3)

[[Image:IC-comp-new.png|thumb|right|200px|<font size=1>Figure 3: Streamline vs. stochastic tractography of the Internal Capsule</font>]]
; C-References

* [http://lmi.bwh.harvard.edu/papers/pdfs/2002/bjornemoMICCAI02.pdf Björnemo M, Brun A, Kikinis R, Westin CF. Regularized stochastic white matter tractography using diffusion tensor MRI. In Fifth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'02). Tokyo, Japan, 2002;435-442.]
* [http://lmi.bwh.harvard.edu/papers/pdfs/2006/frimanTMI06.pdf Friman, O., Farneback, G., Westin CF. A Bayesian Approach for Stochastic White Matter Tractography. IEEE Transactions on Medical Imaging, Vol 25, No. 8, Aug. 2006]

[[Image:StochasticGUI1.PNG|thumb|right|200px|<font size=1> Figure 4: Python Stochastic Tractography GUI </font>]]

==Module==
Can be found in: MODULES > PYTHON MODULES > PYTHON STOCHASTIC TRACTOGRAPHY
;Functionality of Python Stochastic Tractography module in Slicer 3.0
* IO:
Module reads files (DWI and ROIs) in nhdr format.
* Smoothing:
One can smooth the DWI data (only Gausian smoothing is supported at this time). We recommend it if the data is noisy.
* Brain Mask:
The Brain mask defines the volume in which the tensor will be computed and the tracts evaluated. If Enabled, will use threshold values on the baseline instead of WM Mask defined in IO panel.
*Diffusion Tensor:
This step calculates the tensor and can output anisotropy indices (FA/Mode/Trace)
*Tractography:
Parameters that need to be adjusted:
:1. The amount of tracts that will be seeded from each voxel (we recommend between 500 and 1000 tracts, depending on the workstation power- 1000 tracts per voxel seeded within the large ROI for high resolution DWI can take a long time to compute).
:2. Maximum tract length (in mm), this can eliminate long, unwanted tracts if the regions for which connection is measured are located close to each other
:3. Step Size: distance between each re-estimation of tensors, usually between 0.5 and 1 mm
:4. Stopping criteria. This can be used on the top of WM mask, to terminate tracts (in case they really want to travel through the CSF, for example).
*Connectivity Map:
This step creates output probability maps.
:1. binary: each voxel is counted only once if at least one fiber pass through it
:2. cumulative: tracts are summed by voxel independently
:3. weighted: tracts are summed by voxel depending on their length
*Length Based:
This step will output only either the shortest 1/3, middle 1/3, or longest 1/3 of the tracts.
*Vicinity
This step traces back n number of steps from tract endpoint to check if track crosses target ROI. If so, tract is included.
*Threshold
This step will reject tracts whose endpoints are lower than the threshold value).
*Spherical ROI vicinity
This will make the ROI a sphere based on the ROI’s center of gravity (with the sphere’s radius being the distance from the center to the ROI’s furthest point). This sphere can be inflated by raising the Vicinity level to the number of steps you’d like to increase the ROI’s size by.

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.

:* Module documentation can be found here:
:**[[Media:IJdata.tar.gz|Training Dataset]]
:**[[Media:Stochastic_June09_1.ppt|Training Presentation]]
:**[[Media:Helix.zip|Sample Helix Dataset]]

==Work Accomplished==
[[Image:helix_withsmoothing.png|thumb|right|200px|<font size=1>Figure 6: Stochastic tractography from a single ROI on helix phantom</font>]]
; A - Optimization and testing of stochastic tractography algorithm :
:* Original methodological paper, as well as our first attempts to use the algorithm (CC+ and matlab scripts) have been done on old "NAMIC" 1.5T LSDI data ([https://portal.nbirn.net:443/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=gotoDirectory&CPq_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FHarvard%2Farchive_morph&up=CPq&JavaScript=enabled Structural MRI] and [https://portal.nbirn.net:443/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=gotoDirectory&up=7li&7li_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FHarvard%2Farchive_diffusion&JavaScript=enabled DTI data]).
:* Tri worked hard on making sure algorithm works on new high resolution 3T data (available here: [https://portal.nbirn.net/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=moveUpDir&gv7_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FPNL%2F3T_strct_dti_fmri%2Fcase01017&up=gv7&JavaScript=enabled|PNL 3T Data]).
:* Tests have been done also on the spiral diffusion phantom, to make sure diffusion directions and scanner coordinates are handled properly by the algorithm (Figure 6).

; B - Clinical Applications

:* Algorithm was used to trace and analyze anterior limb of the internal capsule on 1.5T data. It generated reacher representation of frontal fiber projections, it also turned out to be more sensitive to group differences in white matter integrity that conventional deterministic tractography (see Figure 6).
:* Algorithm was also used to trace smaller white matter fiber tracts, such as Cingulum, Fornix, Uncinate Fasciculus, Arcuate Fasciculus on 3T "Santa Fe" dataset (http://www.na-mic.org/Wiki/index.php/SanteFe.Tractography.Conference)

; C - References

:* [http://www.na-mic.org/Wiki/index.php/Image:IC_posternew.png Shenton, M.E., Ngo, T., Rosenberger, G., Westin, C.F., Levitt, J.J., McCarley, R.W., Kubicki, M. Study of Thalamo-Cortical White Matter Fiber Tract Projections in Schizophrenia Using Diffusion Stochastic Tractography. Poster presented at the 46th Meeting of the American College of Neuropsychopharmacology, Boca Raton, FL, December 2007.]
:* [http://www.na-mic.org/Wiki/index.php/Media:dougt_poster.pdf Terry DP, Rausch AC, Alvarado JL, Melonakos ED, Markant D, Westin CF, Kikinis R, de Siebenthal J, Shenton ME, Kubicki M. White Matter Properties of Emotion Related Connections in Schizophrenia. Poster presented at the 2009 Mysell Poster Day, Dept. of Psychiatry, Harvard Medical School, April 2009]
:* [http://www.na-mic.org/Wiki/index.php/Media:Jorge_poster.pdf Alvarado JL, Terry DP, Markant D, Ngo T, Kikinis R, Westin CF, McCarley RW, Shenton ME, Kubicki M. Study of Language-Related White Matter Tract Connections in Schizophrenia using Diffusion Stochastic Tractography. Poster presented at the 2009 Mysell Poster Day, Dept. of Psychiatry, Harvard Medical School, April 2009]
:* Melonakos ED, Shenton ME, Markant D, Alvarado J, Westin CF, Kubicki M. White Matter Properties of Orbitofrontal Connections in Schizophrenia. Poster being presented at the 64th Meeting of the Society of Biological Psychiatry. Vancouver, BC. May 2009.
:* Kubicki, M. Khan, U., Bobrow, L., O'Donnell, L. Pieper, S. Westin, CF., Shenton, ME. New Methods for Assessing Whole Brain DTI Abnormalities in Schizophrenia. Presentation given at the International Congress of World Psychiatric Association. Florence, Italy. April 2009.
:* Kubicki, M., Markant, D., Ngo, T., Westin, CF., McCarley, RW., Shenton, ME. Study of Language Related White Matter Fiber Tract Projections in Schizophrenia Using Diffusion Stochastic Tractography. Presentation given at the International Congress of World Psychiatric Association. Florence, Italy. April 2009.

== Work in Progress ==

; A - Optimization and Testing of stochastic tractography module :
:* Julien is testing now separate components of work flow, including the masks, and impact of their precision on stochastic output, as well as impact of number of seeding points on tractography results.
:* At the same time, we are testing the module on a Max Plank dataset of Anna Rotarska-Jagiela by looking at the connections through the corpus callosum of the left auditory cortex to the right auditory cortex as defined by fMRI activation maps as well as running this data for the tractography comparison project.
:* We are discussing possibility of adding project specific functionality to module, such as third ROI to guide tractography, nonlinear registration button for merging fMRI and DTI data.

; B - Related Clinical Projects

;* Arcuate Fasciculus Extraction Project
[[Image:STArcuate.jpg|thumb|right|200px|<font size=1>Figure 7: The arcuate fasciculus including seed, midpoint and target ROI's.</font>]]
We have started the project of investigating Arcuate Fasciculus using Stochastic Tractography (Figure 7). This structure is especially important in both VCFS and schizophrenia, as it connects language related areas (Brocka and Wernicke's), and is involved in language processing quite disturbed in schizophrenia patients. It also can not be reliably traced using deterministic tractography.
:Project involves:
:* Whole brain segmentation, and automatic extraction of regions interconnected by Arcuate Fasciculus (Inferior frontal and Superior Temporal Gyri).
:* White matter segmentation, in order to prevent algorithm from traveling through the ventricles, where diffusivity is high.
:* Non-linear registration of labelmaps to the DTI space.
:* Seeding tracts. We have piloted it using 5000 seeds per voxel, however it is quite time consuming running it on even most powerful computers in the lab, so we have experimented with smaller number of seeds per voxel. We tested 1000 seeds, which gave virtually identical results.
:* Extracting path of interest, and calculating FA along the path for group comparison. Presentation of previous results for 7 schizophrenics and 12 control subjects, can be found here: [[Media:NAMIC_AHM_Arcuate.ppt|Progress Report Presentation]].
:* Results presentation and paper submission. Abstract was accepted and results presented at the World Biological Psychiatry Symposium in Venice, Italy. Paper is in preparation.

[[Image:connectivity1.jpg|thumb|right|200px|<font size=1>Figure 8: Maps of inferior frontal cortex (Broca) connectivity in controls and patients with schizophrenia.</font>]]

;* Semantic Network Connectivity Project
We use combination of fMRI and DTI data to define and characterize functional and anatomical connectivity within the semantic processing network in schizophrenia.
:Project involves:
:* fMRI data analysis and identification of functional nodes involved in semantic processing in healthy controls and seubjects with schizophrenia
:* Analysis of functional connectivity (using FSL) between nodes of semantic network
:* Whole brain Voxel Based analysis of DTI data in same population
:* Use of stochastic tractography to identify connections between functional nodes
:* Correlational analysis involving anatomical and functional connectivity data.
:* Results presentation and paper submission. Data was presented at HBM conference in 2007, paper has been submitted to HBM.
:* <font color="red">'''New: '''</font>June 10th 2009, paper accepted for publication in Human Brain Mapping: "Functional and Anatomical Connectivity Abnormalities in Left Inferior Frontal Gyrus in Schizophrenia" by Jeong, Wible, Hashimoto and Kubicki, HBM in Press

[[Image:ScreenshotFreeSurferDeepMatterSagitalView-vcase1-2009-06-12.jpg|thumb|right|200px|<font size=1>Figure 9: Brain automatic segmentation of subject with VCFS.</font>]]

;* Anatomical Connectivity Abnormalities in VCFS
We use combination of structural MRI and DTI to investigate anatomical abnormalities that would characterize patients with VCFS, and relationship of these abnormalities to those observed in schizophrenia.
:Project involves:
:* Whole brain, automatic segmentation of brain images obtained from patients with VCFS in order to identify structures involved in this disease.
:* Registration between anatomical and DTI scans (manual skull stripping followed by linear followed by nonlinear registration of SPGRs to DTI space).
:* Use of stochastic tractography to identify connections between gray matter regions identified in the disease.
:* Extracting paths of interest, and calculating FA along the paths for group comparison.
:* Correlational analysis involving anatomical and connectivity data, clinical information and genetic data.
:* Results presentation and paper submission. First step of data analysis has been already accomplished, we are in the process of registering structural and DTI data and preparing DTI data for stochastic tractography analysis. We plan to submit an abstract with study results for Biological Psychiatry symposium (dedline December 2009).

[[Image:Anna.png|thumb|right|200px|<font size=1>Figure 10: Max Plank data showing tracts through the corpus connecting 2 cortical ROIs defined by fMRI activations.</font>]]

;* Study of Default Network
We have started collaboration with Department of Neurophysiology Max Planck Institute in Frankfurt (Anna Rotarska contact person). They have dataset containing DTI and resting state fMRI in schizophrenia, and want to use stochastic tractography to measure integrity of anatomical connections within the default network in schizophrenia. Their fMRI data has been co-registered with anatomical scans, and has been put it into DTI space. Figure 8 shows pilot data of the white matter connections through the corpus callosum between the left and the right auditory cortex ROIs as defined by fMRI data. This data will be also used to test robustness of our module, since data was collected on a different scanner (3T Philips).

;* Tractography Comparison Project
We are also working on a [http://www.na-mic.org/Wiki/index.php/SanteFe.Tractography.Conference tractography comparison project]dataset, where we apply stochastic tractography to phantom, as well as test dataset.

===Staffing Plan===

* Sylvain and Yogesh are the DBP resources charged with adapting the tools in the NA-MIC Kit to the DBP needs
* Doug Markant, our NAMIC RA has left the lab, and now Doug Terry, is a new NAMIC RA.
* Julien is our new NAMIC software engineer. He is responsible for improving the STM (Stochastic Tractography Module), and making sure software works with STM compliant datasets.
[[Link Progress| Development Progress]]
* Polina is the algorithm core contact
* Brad is the engineering core contact

===Schedule===

* '''10/2007''' - Optimization of Stochastic Tractography algorythm for 1.5T data.
* '''10/2007''' - Algorythm testing on Santa Fe data set and diffusion phantom.
* '''06/2008''' - Optimization of Stochastic Tractography algorythm for 3T data.
* '''11/2008''' - Slicer 3 module prototype using python.
* '''12/2008''' - Slicer 3 module official release
* '''12/2008''' - Documentation and packaging for dissemination.
* '''12/2008''' - First clinical application of stochastic tractography module.
* '''01/2009''' - First draft of the clinical paper.
* '''05/2009''' - Distortion correction and nonlinear registration added to the module
* '''05/2009''' - Symposium on tractography, including stochastic methods at World Biological Psychiatry Symposium in Florence, Italy.
* '''05/2009''' - Presentation of Arcuate Fasciculus findings at World Biological Psychiatry Symposium in Florence, Italy.
* '''07/2009''' - Summer Programming week- work on optimizing and speeding up data processing, releasing second generation of software that includes preprocessing pipeline.
* '''07/2009''' - Continue working on clinical collaborative studies using stochastic tractography module.
* '''12/2009''' - Submission of abstracts reporting findings of several clinical studies involving stochastic tractography, including anatomical connectivity abnormalities in patients with VCFS and anatomical and functional connectivity abnormalities in schizophrenia.

===Team and Institute===
*PI: Marek Kubicki (kubicki at bwh.harvard.edu)
*DBP2 Investigators: Sylvain Bouix, Yogesh Rathi, Julien de Siebenthal
*NA-MIC Engineering Contact: Brad Davis, Kitware
*NA-MIC Algorithms Contact: Polina Gollard, MIT

===Publications===

''In print''

*[http://www.na-mic.org/pages/Special:Publications?text=Kubicki+AND+Westin+AND+DTI&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database - Clinical Applications]

*[http://www.na-mic.org/pages/Special:Publications?text=Ngo+AND+Golland&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database - Algorithms Development]

[[Category: Schizophrenia]] [[Category: Diffusion MRI]] [[Category: Segmentation]]

File:StochasticGUI1.PNG

2010-02-05T16:03:17Z

Rauscha: uploaded a new version of "File:StochasticGUI1.PNG"

Python Stochastic Tractography Tutorial

2010-02-03T17:21:28Z

Rauscha: /* Key Investigators */

{|
|[[Image:NAMIC-SLC.jpg|thumb|320px|Return to [[2009_Winter_Project_Week|Project Week Main Page]] ]]
|[[Image:Cc_01.png|thumb|340px|Model of the Corpus Callosum from Stochastic Tractography (coronal) ]]
|[[Image:Cc_02.png|thumb|340px|Model of the Corpus Callosum from Stochastic Tractography (sagittal) ]]
|[[Image:helix_withsmoothing.png|thumb|220px|Stochastic Tractography on Phantom]]
|}

__NOTOC__

===Key Investigators===
*PI: Marek Kubicki (kubicki at bwh.harvard.edu)
*DBP2 Investigators: Sylvain Bouix, Yogesh Rathi, Julien de Siebenthal, RA Doug Terry, Andrew Rausch
*NA-MIC Engineering Contact: Brad Davis, Kitware
*NA-MIC Algorithms Contact: Polina Gollard, MIT

===Tutorial===
*[[Media:IJData.tar.gz|Training Dataset]]
*[[Media:Stochastic_June09_1.ppt|Training Presentation]]
*[[Media:Helix.zip|Sample Helix Dataset]]

__NOTOC__
<div style="margin: 20px;">
<div style="width: 40%; float: left; padding-right: 3%;">
<h1>Objective</h1>
To address the uncertainty of deterministic tractography methods, stochastic tractography methods have been developed to quantify the uncertainty associated with estimated fibers (Bjornemo et al., 2002). Method uses 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. 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.
</div>

<div style="width: 27%; float: left; padding-right: 3%;">
<h1>Approach, Plan</h1>
This tutorial will walk users through the multiple stages of the python stochastic tractography module, where the inputs are a DWI and a region of interest labelmap. This will include:
# Loading the Volumes
# Extracting DWI and getting Baseline DWI threshold levels
# Using features of the module including smoothing, brain mask generation, and other settings.
# Generating a Connectivity Map, which is able to be thresholded and customized.
</div>

<div style="width: 27%; float: left;">
<h1>Progress</h1>
*A tutorial has been developed and data is available for tutorial.
*We have started the project of investigating Arcuate Fasciculus using Stochastic Tractography. This structure is especially important in both VCFS and schizophrenia, as it connects language related areas (Brocka and Wernicke's), and is involved in language processing quite disturbed in schizophrenia patients. It also can not be reliably traced using deterministic tractography.
</div>

<br style="clear: both;" />
</div>

Python Stochastic Tractography Tutorial

2010-02-03T17:20:51Z

Rauscha: /* Key Investigators */

{|
|[[Image:NAMIC-SLC.jpg|thumb|320px|Return to [[2009_Winter_Project_Week|Project Week Main Page]] ]]
|[[Image:Cc_01.png|thumb|340px|Model of the Corpus Callosum from Stochastic Tractography (coronal) ]]
|[[Image:Cc_02.png|thumb|340px|Model of the Corpus Callosum from Stochastic Tractography (sagittal) ]]
|[[Image:helix_withsmoothing.png|thumb|220px|Stochastic Tractography on Phantom]]
|}

__NOTOC__

===Key Investigators===
*PI: Marek Kubicki (kubicki at bwh.harvard.edu)
*DBP2 Investigators: Sylvain Bouix, Yogesh Rathi, Julien de Siebenthal, RA Doug Terry
*NA-MIC Engineering Contact: Brad Davis, Kitware
*NA-MIC Algorithms Contact: Polina Gollard, MIT

===Tutorial===
*[[Media:IJData.tar.gz|Training Dataset]]
*[[Media:Stochastic_June09_1.ppt|Training Presentation]]
*[[Media:Helix.zip|Sample Helix Dataset]]

__NOTOC__
<div style="margin: 20px;">
<div style="width: 40%; float: left; padding-right: 3%;">
<h1>Objective</h1>
To address the uncertainty of deterministic tractography methods, stochastic tractography methods have been developed to quantify the uncertainty associated with estimated fibers (Bjornemo et al., 2002). Method uses 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. 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.
</div>

<div style="width: 27%; float: left; padding-right: 3%;">
<h1>Approach, Plan</h1>
This tutorial will walk users through the multiple stages of the python stochastic tractography module, where the inputs are a DWI and a region of interest labelmap. This will include:
# Loading the Volumes
# Extracting DWI and getting Baseline DWI threshold levels
# Using features of the module including smoothing, brain mask generation, and other settings.
# Generating a Connectivity Map, which is able to be thresholded and customized.
</div>

<div style="width: 27%; float: left;">
<h1>Progress</h1>
*A tutorial has been developed and data is available for tutorial.
*We have started the project of investigating Arcuate Fasciculus using Stochastic Tractography. This structure is especially important in both VCFS and schizophrenia, as it connects language related areas (Brocka and Wernicke's), and is involved in language processing quite disturbed in schizophrenia patients. It also can not be reliably traced using deterministic tractography.
</div>

<br style="clear: both;" />
</div>

Python Stochastic Tractography Tutorial

2010-02-03T16:08:47Z

Rauscha: /* Tutorial */

{|
|[[Image:NAMIC-SLC.jpg|thumb|320px|Return to [[2009_Winter_Project_Week|Project Week Main Page]] ]]
|[[Image:Cc_01.png|thumb|340px|Model of the Corpus Callosum from Stochastic Tractography (coronal) ]]
|[[Image:Cc_02.png|thumb|340px|Model of the Corpus Callosum from Stochastic Tractography (sagittal) ]]
|[[Image:helix_withsmoothing.png|thumb|220px|Stochastic Tractography on Phantom]]
|}

__NOTOC__

===Key Investigators===
*PI: Marek Kubicki (kubicki at bwh.harvard.edu)
*DBP2 Investigators: Sylvain Bouix, Yogesh Rathi, Julien de Siebenthal, RA Doug Terry
*NA-MIC Engineering Contact: Brad Davis, Kitware
*NA-MIC Algorithms Contact: Polina Gollard, MIT

Go to [http://www.slicer.org/slicerWiki/index.php/Modules:StochasticTractography-Documentation-3.4 Slicer 3.4 module documentation] for more information

===Tutorial===
*[[Media:IJData.tar.gz|Training Dataset]]
*[[Media:Stochastic_June09_1.ppt|Training Presentation]]
*[[Media:Helix.zip|Sample Helix Dataset]]

__NOTOC__
<div style="margin: 20px;">
<div style="width: 40%; float: left; padding-right: 3%;">
<h1>Objective</h1>
To address the uncertainty of deterministic tractography methods, stochastic tractography methods have been developed to quantify the uncertainty associated with estimated fibers (Bjornemo et al., 2002). Method uses 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. 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.
</div>

<div style="width: 27%; float: left; padding-right: 3%;">
<h1>Approach, Plan</h1>
This tutorial will walk users through the multiple stages of the python stochastic tractography module, where the inputs are a DWI and a region of interest labelmap. This will include:
# Loading the Volumes
# Extracting DWI and getting Baseline DWI threshold levels
# Using features of the module including smoothing, brain mask generation, and other settings.
# Generating a Connectivity Map, which is able to be thresholded and customized.
</div>

<div style="width: 27%; float: left;">
<h1>Progress</h1>
*A tutorial has been developed and data is available for tutorial.
*We have started the project of investigating Arcuate Fasciculus using Stochastic Tractography. This structure is especially important in both VCFS and schizophrenia, as it connects language related areas (Brocka and Wernicke's), and is involved in language processing quite disturbed in schizophrenia patients. It also can not be reliably traced using deterministic tractography.
</div>

<br style="clear: both;" />
</div>

File:Stochastic June09 1.ppt

2010-01-06T21:00:10Z

Rauscha: uploaded a new version of "File:Stochastic June09 1.ppt"

File:Stochastic Jan09.ppt

2010-01-06T20:53:04Z

Rauscha: uploaded a new version of "File:Stochastic Jan09.ppt": This is a roughly complete version. Contact rauscha@bwh.harvard.edu with any errors found

Stochastic Tractography module tutorial for Slicer 3.5

2010 Winter Project Week Stochastic Tractography

2010-01-06T18:19:53Z

Rauscha:

__NOTOC__
[[File:Tract cloud.jpg|600px|thumb|left|tract cloud]]

==Key Investigators==
* Previously: Julien von Siebenthal
* BWH: Andrew Rausch, Marek Kubicki

<h3>Objective</h3>
Julien has kindly updated his stochastic tractography algorithm in the Python Stochastic Tractography module in Slicer 3.5. It needs to be tested and verified as working.

Andrew is currently updating the tutorial for this new method. Current progress can be found here: [[File:Stochastic Jan09.ppt| stochastic tutorial powerpoint]]

2010 Winter Project Week Stochastic Tractography

2010-01-06T18:19:36Z

Rauscha:

__NOTOC__
[[File:Tract cloud.jpg|600px|thumb|left|alt text]]

==Key Investigators==
* Previously: Julien von Siebenthal
* BWH: Andrew Rausch, Marek Kubicki

<h3>Objective</h3>
Julien has kindly updated his stochastic tractography algorithm in the Python Stochastic Tractography module in Slicer 3.5. It needs to be tested and verified as working.

Andrew is currently updating the tutorial for this new method. Current progress can be found here: [[File:Stochastic Jan09.ppt| stochastic tutorial powerpoint]]

2010 Winter Project Week Stochastic Tractography

2010-01-06T18:02:53Z

Rauscha:

__NOTOC__
[[File:Tract cloud.jpg|200px|thumb|left|alt text]]

==Key Investigators==
* Previously: Julien von Siebenthal
* BWH: Andrew Rausch, Marek Kubicki

<h3>Objective</h3>
Julien has kindly updated his stochastic tractography algorithm in the Python Stochastic Tractography module in Slicer 3.5. It needs to be tested and verified as working.

Andrew is currently updating the tutorial for this new method. Current progress can be found here: [[File:Stochastic Jan09.ppt| stochastic tutorial powerpoint]]

File:Tract cloud.jpg

2010-01-06T18:01:53Z

Rauscha:

File:Stochastic Jan09.ppt

2010-01-06T17:48:29Z

Rauscha: uploaded a new version of "File:Stochastic Jan09.ppt"

Stochastic Tractography module tutorial for Slicer 3.5

2010 Winter Project Week Stochastic Tractography

2010-01-06T17:18:10Z

Rauscha:

__NOTOC__

==Key Investigators==
* Previously: Julien von Siebenthal
* BWH: Andrew Rausch, Marek Kubicki

<h3>Objective</h3>
Julien has kindly updated his stochastic tractography algorithm in the Python Stochastic Tractography module in Slicer 3.5. It needs to be tested and verified as working.

Andrew is currently updating the tutorial for this new method. Current progress can be found here: [[File:Stochastic Jan09.ppt| stochastic tutorial powerpoint]]

2010 Winter Project Week Stochastic Tractography

2010-01-06T00:06:16Z

Rauscha:

__NOTOC__

==Key Investigators==
* Previously: Julien von Siebenthal
* BWH: Andrew Rausch, Marek Kubicki

<h3>Objective</h3>
Test new Stochastic Tractography algorithm created by Julien

Andrew is currently updating the tutorial for this new method. Current progress can be found here: [[File:Stochastic Jan09.ppt| stochastic tutorial powerpoint]]

2010 Winter Project Week Stochastic Tractography

2010-01-05T23:53:46Z

Rauscha: /* References */

2010 Winter Project Week Stochastic Tractography

2010-01-05T23:53:33Z

Rauscha: /* Key Investigators */

File:Stochastic Jan09.ppt

2010-01-05T23:27:00Z

Rauscha: Stochastic Tractography module tutorial for Slicer 3.5

Stochastic Tractography module tutorial for Slicer 3.5

2010 Winter Project Week Stochastic Tractography

2010-01-05T23:19:59Z

Rauscha: /* Instructions for Use of this Template */

__NOTOC__
<gallery>
Image:PW-SLC2010.png|[[2010_Winter_Project_Week#Projects|Projects List]]
Image:genuFAp.jpg|Scatter plot of the original FA data through the genu of the corpus callosum of a normal brain.
Image:genuFA.jpg|Regression of FA data; solid line represents the mean and dotted lines the standard deviation.
</gallery>

==Key Investigators==
* UNC: Isabelle Corouge, Casey Goodlett, Guido Gerig
* Utah: Tom Fletcher, Ross Whitaker

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>
We are developing methods for analyzing diffusion tensor data along fiber tracts. The goal is to be able to make statistical group comparisons with fiber tracts as a common reference frame for comparison.

</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>

Our approach for analyzing diffusion tensors is summarized in the IPMI 2007 reference below. The main challenge to this approach is <foo>.

Our plan for the project week is to first try out <bar>,...

</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>
Software for the fiber tracking and statistical analysis along the tracts has been implemented. The statistical methods for diffusion tensors are implemented as ITK code as part of the [[NA-MIC/Projects/Diffusion_Image_Analysis/DTI_Software_and_Algorithm_Infrastructure|DTI Software Infrastructure]] project. The methods have been validated on a repeated scan of a healthy individual. This work has been published as a conference paper (MICCAI 2005) and a journal version (MEDIA 2006). Our recent IPMI 2007 paper includes a nonparametric regression method for analyzing data along a fiber tract.

</div>
</div>

<div style="width: 97%; float: left;">

==References==
*Fletcher P, Tao R, Jeong W, Whitaker R. [http://www.na-mic.org/publications/item/view/634 A volumetric approach to quantifying region-to-region white matter connectivity in diffusion tensor MRI.] Inf Process Med Imaging. 2007;20:346-358. PMID: 17633712.
* Corouge I, Fletcher P, Joshi S, Gouttard S, Gerig G. [http://www.na-mic.org/publications/item/view/292 Fiber tract-oriented statistics for quantitative diffusion tensor MRI analysis.] Med Image Anal. 2006 Oct;10(5):786-98. PMID: 16926104.
* Corouge I, Fletcher P, Joshi S, Gilmore J, Gerig G. [http://www.na-mic.org/publications/item/view/1122 Fiber tract-oriented statistics for quantitative diffusion tensor MRI analysis.] Int Conf Med Image Comput Comput Assist Interv. 2005;8(Pt 1):131-9. PMID: 16685838.
* Goodlett C, Corouge I, Jomier M, Gerig G, A Quantitative DTI Fiber Tract Analysis Suite, The Insight Journal, vol. ISC/NAMIC/ MICCAI Workshop on Open-Source Software, 2005, Online publication: http://hdl.handle.net/1926/39 .

</div>

2010 Winter Project Week Stochastic Tractography

2010-01-05T23:19:39Z

Rauscha: Created page with '__NOTOC__ <gallery> Image:PW-SLC2010.png|Projects List Image:genuFAp.jpg|Scatter plot of the original FA data through the genu of the corpus…'

__NOTOC__
<gallery>
Image:PW-SLC2010.png|[[2010_Winter_Project_Week#Projects|Projects List]]
Image:genuFAp.jpg|Scatter plot of the original FA data through the genu of the corpus callosum of a normal brain.
Image:genuFA.jpg|Regression of FA data; solid line represents the mean and dotted lines the standard deviation.
</gallery>

==Instructions for Use of this Template==
#Please create a new wiki page with an appropriate title for your project using the convention Project/<Project Name>
#Copy the entire text of this page into the page created above
#Link the created page into the list of projects for the project event
#Delete this section from the created page
#Send an email to tkapur at bwh.harvard.edu if you are stuck

==Key Investigators==
* UNC: Isabelle Corouge, Casey Goodlett, Guido Gerig
* Utah: Tom Fletcher, Ross Whitaker

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>
We are developing methods for analyzing diffusion tensor data along fiber tracts. The goal is to be able to make statistical group comparisons with fiber tracts as a common reference frame for comparison.

</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>

Our approach for analyzing diffusion tensors is summarized in the IPMI 2007 reference below. The main challenge to this approach is <foo>.

Our plan for the project week is to first try out <bar>,...

</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>
Software for the fiber tracking and statistical analysis along the tracts has been implemented. The statistical methods for diffusion tensors are implemented as ITK code as part of the [[NA-MIC/Projects/Diffusion_Image_Analysis/DTI_Software_and_Algorithm_Infrastructure|DTI Software Infrastructure]] project. The methods have been validated on a repeated scan of a healthy individual. This work has been published as a conference paper (MICCAI 2005) and a journal version (MEDIA 2006). Our recent IPMI 2007 paper includes a nonparametric regression method for analyzing data along a fiber tract.

</div>
</div>

<div style="width: 97%; float: left;">

==References==
*Fletcher P, Tao R, Jeong W, Whitaker R. [http://www.na-mic.org/publications/item/view/634 A volumetric approach to quantifying region-to-region white matter connectivity in diffusion tensor MRI.] Inf Process Med Imaging. 2007;20:346-358. PMID: 17633712.
* Corouge I, Fletcher P, Joshi S, Gouttard S, Gerig G. [http://www.na-mic.org/publications/item/view/292 Fiber tract-oriented statistics for quantitative diffusion tensor MRI analysis.] Med Image Anal. 2006 Oct;10(5):786-98. PMID: 16926104.
* Corouge I, Fletcher P, Joshi S, Gilmore J, Gerig G. [http://www.na-mic.org/publications/item/view/1122 Fiber tract-oriented statistics for quantitative diffusion tensor MRI analysis.] Int Conf Med Image Comput Comput Assist Interv. 2005;8(Pt 1):131-9. PMID: 16685838.
* Goodlett C, Corouge I, Jomier M, Gerig G, A Quantitative DTI Fiber Tract Analysis Suite, The Insight Journal, vol. ISC/NAMIC/ MICCAI Workshop on Open-Source Software, 2005, Online publication: http://hdl.handle.net/1926/39 .

</div>

2010 Winter Project Week DTI Breakout Session

2010-01-05T23:17:46Z

Rauscha:

Agenda
* Overview of the DTI analysis tools and activities within NA-MIC, discussion of ongoing DTI project developments in relationship to 3D Slicer tools

Presenters
* Sonia Pujol, Harvard Medical School
* Lauren O'Donnell, Harvard Medical School
* Alex Yarmakovich, Isomics Inc.
* Guido Gerig, Utah

Audience:
* Project developers interested in learning about existing NA-MIC DTI resources, in order to discuss where the new tools would fit into a workflow

* Winter 2010 DTI projects:
#[[ 2010_Winter_Project_Week_HARDI_RSH|Integration of Real Spherical Harmonic basis for HARDI models]] (Luke Bloy, C-F Westin)
#[[ 2010_Winter_Project_Week_Tractography|Filtered tractography]] (James Malcolm, Peter Savadjiev, Yogesh Rathi, C-F Westin, Casey Goodlett)
#[[ 2010_Winter_Project_Week_HARDI_CONNECTIVITY|Connectivity Study of Neonatal Brain Data using HARDI Techniques]] ( Yundi(Wendy) Shi, Deepika Mahalingam, Martin Styner )
#[[2010_Winter_Project_Week_TractographyPickingEditing|Tractography Picking and Bundle Editing]] (Jim Miller, Mahnaz Maddah, Nicole Aucoin, Wendy Plesniak, James Malcolm, Alex Yarmarkovich)
#[[ 2010_Winter_Project_Week_DTI_Fiber_Tract_Statistics|DTI Fiber-Tract Statistics]] (Anuja Sharma, Guido Gerig)
#[[ 2010_Winter_Project_Week_Tractography_using_DTI_Atlasing|Tractography using DTI Atlasing]] (Gopalkrishna Veni, Ross Whitaker, Sarang Joshi)
#[[2010_Winter_Project_Week_DTI_QualityControl|DTI Quality Control tools integration with NITRC]] (Hans Johnson, UNC)
#[[ 2010_Winter_Project_Week_Stochastic_Tractography|Stochastic Tractography module in Slicer 3.5 ]] (Andrew Rausch)

Logistics:
* Wednesday January 6, 10:30 am - 11:30 am
* Location: Amethyst 1

[http://www.na-mic.org/Wiki/index.php/AHM_2010 Back to NA-MIC AHM 2010]

User:Rauscha

2009-12-31T20:55:37Z

Rauscha: /* Registration */

==Andrew Rausch==
Works at the Psychiatry Neuroimaging Laboratory at BWH. [http://pnl.bwh.harvard.edu/index.html| The PNL website]

===Registration===
Currently working on tract clustering (using Mahnaz Maddah's extension) and stochastic tractography (using Julien Von Sibenthal's module).

Previously working on documenting registration issues in Slicer 3. Find the registration page [[Projects:DBP2:Harvard:Registration Documentation| here]].

2009 Summer Project Week Slicer3 registration

2009-06-22T16:50:12Z

Rauscha:

__NOTOC__
<gallery>
Image:PW2009-v3.png|[[2009_Summer_Project_Week|Project Week Main Page]]
Image:Aparc-overlay-affine.png|Overlay of affine registered freesurfer label map on DTI data.
Image:T2-base masked rigid-linear-affine.png|Rigid-Affine-Bspline registration of T2 to DTI data.
</gallery>

==Key Investigators==
* BWH: Andrew Rausch, Doug Terry, Sylvain Bouix, Marek Kubicki
* Kitware: Luis Ibanez

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>
We are determining which methods of registration in slicer (and other tools) work best for registration of various types of volumes. Our focus is currently on registering data from structural to DTI space.

</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>
We're going through each type of registration and registration pipeline in Slicer 3 with a specific image to test which methods give the best results. We are also performing comparisons with FSL's FLIRT and FNIRT tools.

</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>
Some of Slicer 3's registration works ok, but it currently has no good way to register from structural to DTI, and no way to apply a similar transform to a label map from structural space. Currently we must use FSL tools to accomplish this. Luis Ibanez has been helping us with developing tools outside of Slicer that seem to be performing well.

</div>
</div>

<div style="width: 970%; float: left;">

==References==
http://www.na-mic.org/Wiki/index.php/Projects:DBP2:Harvard:Registration_Documentation

</div>

2009 Summer Project Week Slicer3 registration

2009-06-16T16:13:57Z

Rauscha:

__NOTOC__
<gallery>
Image:PW2009-v3.png|[[2009_Summer_Project_Week|Project Week Main Page]]
Image:Aparc-overlay-affine.png|Overlay of affine registered freesurfer label map on DTI data.
Image:T2-base masked rigid-linear-affine.png|Rigid-Affine-Bspline registration of T2 to DTI data.
</gallery>

==Key Investigators==
* BWH: Andrew Rausch, Doug Terry, Sylvain Bouix, Marek Kubicki

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>
We are determining which methods of registration in slicer (and other tools) work best for registration of various types of volumes. Our focus is currently on registering data from structural to DTI space.

</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>
We're going through each type of registration and registration pipeline in Slicer 3 with a specific image to test which methods give the best results. We are also performing comparisons with FSL's FLIRT and FNIRT tools.

</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>
Some of Slicer 3's registration works ok, but it currently has no good way to register from structural to DTI, and no way to apply a similar transform to a label map from structural space. Currently we must use FSL tools to accomplish this.

</div>
</div>

<div style="width: 970%; float: left;">

==References==
http://www.na-mic.org/Wiki/index.php/Projects:DBP2:Harvard:Registration_Documentation

</div>

2009 Summer Project Week

2009-06-10T17:40:45Z

Rauscha: /* Projects */

Back to [[Project Events]], [[Events]]

[[Image:PW2009-v3.png|300px]]

*'''Dates:''' June 22-26, 2009
*'''Location:''' MIT. [[Meeting_Locations:MIT_Grier_A_%26B|Grier Rooms A & B: 34-401A & 34-401B]].

==Introduction to the FIRST JOINT PROJECT WEEK==

We are pleased to announce the FIRST JOINT PROJECT WEEK of hands-on research and development activity for Image-Guided Therapy and Neuroscience applications. Participants will engage in open source programming using the [[NA-MIC-Kit|NA-MIC Kit]], algorithm design, medical imaging sequence development, tracking experiments, and clinical application. The main goal of this event is to move forward the translational research deliverables of the sponsoring centers and their collaborators. Active and potential collaborators are encouraged and welcome to attend this event. This event will be set up to maximize informal interaction between participants.

Active preparation will begin on''' Thursday, April 16th at 3pm ET''', with a kick-off teleconference. Invitations to this call will be sent to members of the sponsoring communities, their collaborators, past attendees of the event, as well as any parties who have expressed an interest in working with these centers. The main goal of the kick-off call is to get an idea of which groups/projects will be active at the upcoming event, and to ensure that there is sufficient coverage for all. Subsequent teleconferences will allow for more focused discussions on individual projects and allow the hosts to finalize the project teams, consolidate any common components, and identify topics that should be discussed in breakout sessions. In the final days leading upto the meeting, all project teams will be asked to fill in a template page on this wiki that describes the objectives and plan of their projects.

The event itself will start off with a short presentation by each project team, driven using their previously created description, and will help all participants get acquainted with others who are doing similar work. In the rest of the week, about half the time will be spent in breakout discussions on topics of common interest of subsets of the attendees, and the other half will be spent in project teams, doing hands-on project work. The hands-on activities will be done in 30-50 small teams of size 2-4, each with a mix of multi-disciplinary expertise. To facilitate this work, a large room at MIT will be setup with several tables, with internet and power access, and each computer software development based team will gather on a table with their individual laptops, connect to the internet to download their software and data, and be able to work on their projects. Teams working on projects that require the use of medical devices will proceed to Brigham and Women's Hospital and carry out their experiments there. On the last day of the event, a closing presentation session will be held in which each project team will present a summary of what they accomplished during the week.

This event is part of the translational research efforts of [http://www.na-mic.org NA-MIC], [http://www.ncigt.org NCIGT], [http://nac.spl.harvard.edu/ NAC], [http://catalyst.harvard.edu/home.html Harvard Catalyst], and [http://www.cimit.org CIMIT]. It is an expansion of the NA-MIC Summer Project Week that has been held annually since 2005. It will be held every summer at MIT and Brigham and Womens Hospital in Boston, typically during the last full week of June, and in Salt Lake City in the winter, typically during the second week of January.

A summary of all past NA-MIC Project Events that this FIRST JOINT EVENT is based on is available [[Project_Events#Past|here]].

== Agenda==
* Monday
** noon-1pm lunch
**1pm: Welcome (Ron Kikinis)
** 1:05-3:30pm Introduce [[#Projects|Projects]] using templated wiki pages (all Project Leads) ([http://wiki.na-mic.org/Wiki/index.php/Project_Week/Template Wiki Template])
** 3:30-5:30pm Start project work
* Tuesday
** 8:30am breakfast
**9:30-10am: NA-MIC Kit Overview (Jim Miller)
** 10-10:30am Slicer 3.4 Update (Steve Pieper)
** 10:30-11am Slicer IGT and Imaging Kit Update Update (Noby Hata, Scott Hoge)
** 11am-12:00pm Breakout Session: [[2009 Project Week Breakout Session: Slicer-Python]] (Demian W)
** noon lunch
** 2:30pm-5pm: [[2009 Project Week Data Clinic|Data Clinic]] (Ron Kikinis)
** 5:30pm adjourn for day
* Wednesday
** 8:30am breakfast
** 9am-12pm Breakout Session: [[2009 Project Week Breakout Session: ITK]] (Luis Ibanez)
** noon lunch
** 2:30pm: Breakout Session: [[2009 Project Week Breakout Session: 3D+T Microscopy Cell Dataset Segmentation]] (Alex G.)
** 5:30pm adjourn for day
* Thursday
** 8:30am breakfast
** 9-11am [[Events:TutorialContestJune2009|Tutorial Contest Presentations]]
** noon lunch
** 2:30pm: Breakout Session: [[2009 Project Week Breakout Session: XNAT for Programmers]] (Dan M.)
** 5:30pm adjourn for day
* Friday
** 8:30am breakfast
** 10am-noon: [[Events:TutorialContestJune2009|Tutorial Contest Winner Announcement]] and [[#Projects|Project Progress Updates]]
*** Noon: Lunch boxes and adjourn by 1:30pm.
***We need to empty room by 1:30. You are welcome to use wireless in Stata.
***Please sign up for the developer [http://www.slicer.org/pages/Mailinglist mailing lists]
***Next Project Week [[AHM_2010|in Utah, January 4-8, 2010]]

== Projects ==

#[[2009_Summer_Project_Week_Slicer3_Cortical_Thickness_Pipeline|Cortical Thickness Pipeline]] (Clement Vachet UNC)
#[[2009_Summer_Project_Week_Prostate_Robotics |Prostate Robotics]] (Junichi Tokuda BWH)
# Need Lupus Project here
# Need VCSF Project here
#[[2009_Summer_Project_Week_Project_Segmentation_of_Muscoskeletal_Images]] (Saikat Pal Stanford)
#[[2009_Summer_Project_Week_Liver_Ablation_Slicer|Liver Ablation in Slicer]] (Ziv Yaniv Georgetown)
#[[Measuring Alcohol Stress Interaction]] (Vidya Rajgopalan Virginia Tech)
#[[2009_Summer_Project_Week_Skull_Stripping | Skull Stripping]] (Snehasish Roy JHU)
# [[MeshingSummer2009 | IAFE Mesh Modules - improvements and testing]] (Curt Lisle Knowledge Vis)
#[[2009_Summer_Project_Week_Slicer3_Adaptive_Radiotherapy|Adaptive Radiotherapy - Deformable registration and DICOMRT]] (Greg Sharp MGH)
#[[2009_Summer_Project_Week_Slicer3_Brainlab_Introduction|SLicer3, BioImage Suite and Brainlab - Introduction to UCLA]] (Haiying Liu BWH)
#[[2009_Summer_Project_Week_Slicer3_Brainlab_Demo|Demo Brainlab-BioImage Suite-Slicer in BWH OR]] (Haiying Liu BWH)
#[[2009_Summer_Project_Week_Multimodal_SPL_Brain_Atlas|Segmentation of thalamic nuclei from DTI]] (Ion-Florin Talos BWH)
#[[2009_Summer_Project_Week_Slicer3_Fibre_Dispersion|Slicer module for the computation of fibre dispersion and curving measures]] (Peter Savadjiev BWH)
#[[2009_Summer_Project_Week_Hageman_FMTractography | Fluid mechanics tractography and visualization]] (Nathan Hageman UCLA)
#[[2009_Summer_Project_Week_DWI_/_DTI_QC_and_Prepare_Tool:_DTIPrep | DWI/DTI QC and Preparation Tool: DTIPrep]] (Zhexing Liu BWH)
#[[2009_Summer_Project_Week_Hageman_DTIDigitalPhantom | DTI digital phantom generator to create validation data sets - webservice/cmdlin module/binaries are downloadable from UCLA ]] (Nathan Hageman UCLA)
# [[EPI Correction in Slicer3 | EPI Correction in Slicer3]] (Ran Tao Utah)
#[[2009_Summer_Project_Week_WML_SEgmentation |White Matter Lesion segmentation]] (Minjeong Kim GE Research)
#[[2009_Summer_Project_Week-FastMarching_for_brain_tumor_segmentation |FastMarching for brain tumor segmentation]] (Andrey Fedorov BWH)
# [[EMSegment|EM Segment]] (Sylvain Jaume BWH)
#[[2009_Summer_Project_Week_Meningioma_growth_simulation|Meningioma growth simulation]] (Andrey Fedorov BWH)
#[[2009_Summer_Project_Week_Automatic_Brain_MRI_Pipeline|Automatic brain MRI processing pipeline]] (Marcel Prastawa Utah)
#[[2009_Summer_Project_Week_HAMMER_Registration | HAMMER Registration]] (Guorong Wu GE Research)
#[[2009_Summer_Project_Week_Spherical_Mesh_Diffeomorphic_Demons_Registration |Spherical Mesh Diffeomorphic Demons Registration]] (Luis Ibanez Kitware)
# [[BSpline Registration in Slicer3 | BSpline Registration in Slicer3]] (Samuel Gerber Utah)
#[[2009_Summer_Project_Week_4D_Imaging| 4D Imaging (Perfusion, Cardiac, etc.) ]] (Junichi Tokuda BWH)
#[[2009_Summer_Project_Week_MRSI-Module|MRSI Module]] (Bjoern Menze MIT)
#[[2009_Summer_Project_Week_4D_Gated_US_In_Slicer |Gated 4D ultrasound reconstruction for Slicer3]] (Danielle Pace Robarts Institute)
# [[Integration of stereo video into Slicer3]] (Mehdi Esteghamatian Robarts Institute)
#[[2009_Summer_Project_Week_Statistical_Toolbox |multi-modality statistical toolbox for MR T1, T2, fMRI, DTI data]] (Diego Cantor Robarts Institute)
# [[Summer2009:Using_ITK_in_python| Using ITK in python]] (Steve Pieper BWH)
# [[Summer2009:Implementing_parallelism_in_python| Taking advantage of multicore machines & clusters with python]] (Julien de Siebenthal BWH)
# [[Summer2009:Using_client_server_paradigm_with_python_and_slicer| Deferring heavy computational tasks with Slicer python]] (Julien de Siebenthal BWH)
# [[Summer2009:Using_cython| Accelerating python with cython: application to stochastic tractography]] (Julien de Siebenthal BWH)
# [[2009_Summer_Project_Week_VTK_3D_Widgets_In_Slicer3|VTK 3d Widgets in Slicer3]] (Nicole Aucoin BWH)
# [[2009_Summer_Project_Week_Colors_Module |Updates to Slicer3 Colors module]] (Nicole Aucoin BWH)
# [[Plug-In 3D Viewer based on XIP|Plug-in 3D Viewer based on XIP]] (Lining Yang Siemens Research)
# [[Slicer3 Informatics Workflow Design & XNAT updates | Slicer3 Informatics Workflow Design & XNAT updates for Slicer]] (Wendy Plesniak BWH)
# [[Summer2009:Registration reproducibility in Slicer|Registration reproducibility in Slicer3]] (Andrey Fedorov BWH)
# [[Summer2009:The Vascular Modeling Toolkit in 3D Slicer | The Vascular Modeling Toolkit in 3D Slicer]] (Daniel Haehn BWH)
# [[Summer2009:Extension of the Command Line XML Syntax/Interface | Extension of the Command Line XML Syntax/Interface]] (Bennett Landman)
#[[2009_Summer_Project_Week_Slicer3_XNAT_UI | XNAT user interface improvements for NA-MIC]] (Dan Marcus WUSTL)
#[[2009_Summer_Project_Week_XNATFS | XNAT File System with FUSE]] (Dan Marcus WUSTL)
#[[2009_Summer_Project_Week_XNAT_i2b2|XNAT integration into Harvard Catalyst i2b2 framework]] (Yong Harvard)
#[[2009_Summer_Project_Week_Slicer3_registration| Slicer 3 registration ]] (Andrew Rausch)

===CUDA Projects===

This is a list of candidate cuda projects that will be discussed with Joe Stam shortly:

#[[2009_Summer_Project_Week_Registration_for_RT|2d/3d Registration (and GPGPU acceleration) for Radiation Therapy]] (Tina Kapur BWH)
#[[2009_Summer_Project_Week_Statistical_Toolbox |multi-modality statistical toolbox for MR T1, T2, fMRI, DTI data]] (Diego Cantor Robarts Institute)
#[[2009_Summer_Project_Week_Dose_Calculation |accelerate calculation for LDR seeds]] (Jack Blevins Acousticmed)
#[[2009_Summer_Project_Week_Cone_Beam_backprojection]](Zhou Shen U Michigan)
#[[2009_Summer_project_week_3d_Deformable_alignment]](Dan McShan U Michigan)
#[[Summer2009:Using_CUDA_for_stochastic_tractography|Developing interactive stochastic tractography using CUDA]] (Julien de Siebenthal BWH)
#acceleration of parallel real time processing of strain and elasticity images for monitoring of ablative therapy (Clif Burdette Acousticmed)

== Preparation ==

# Please make sure that you are on the http://public.kitware.com/cgi-bin/mailman/listinfo/na-mic-project-week mailing list
# Join the kickoff TCON on April 16, 3pm ET.
# [[Engineering:TCON_2009|June 18 TCON]] at 3pm ET to tie loose ends. Anyone with un-addressed questions should call.
# By 3pm ET on June 11, 2009: [[Project_Week/Template|Complete a templated wiki page for your project]]. Please do not edit the template page itself, but create a new page for your project and cut-and-paste the text from this template page. If you have questions, please send an email to tkapur at bwh.harvard.edu.
# By 3pm on June 18, 2009: Create a directory for each project on the [[Engineering:SandBox|NAMIC Sandbox]] (Zack)
## Commit on each sandbox directory the code examples/snippets that represent our first guesses of appropriate methods. (Luis and Steve will help with this, as needed)
## Gather test images in any of the Data sharing resources we have (e.g. the BIRN). These ones don't have to be many. At least three different cases, so we can get an idea of the modality-specific characteristics of these images. Put the IDs of these data sets on the wiki page. (the participants must do this.)
## Setup nightly tests on a separate Dashboard, where we will run the methods that we are experimenting with. The test should post result images and computation time. (Zack)
# Please note that by the time we get to the project event, we should be trying to close off a project milestone rather than starting to work on one...
# People doing Slicer related projects should come to project week with slicer built on your laptop.
## Projects to develop extension modules should work with the [http://viewvc.slicer.org/viewcvs.cgi/branches/Slicer-3-4/#dirlist Slicer-3-4 branch] (new code should not be checked into the branch).
## Projects to modify core behavior of slicer should be done on the [http://viewvc.slicer.org/viewcvs.cgi/trunk/ trunk].

==Attendee List==
If you plan to attend, please add your name here.

#Ron Kikinis, BWH (NA-MIC, NAC, NCIGT)
#Clare Tempany, BWH (NCIGT)
#Tina Kapur, BWH (NA-MIC, NCIGT)
#Steve Pieper, Isomics Inc
#Jim Miller, GE Research
#Xiaodong Tao, GE Research
#Randy Gollub, MGH
#Nicole Aucoin, BWH (NA-MIC) (Attending Tuesday-Friday)
#Dan Marcus, WUSTL
#Junichi Tokuda, BWH (NCIGT)
#Alex Gouaillard, Harvard Systems Biology
#Arnaud Gelas, Harvard Systems Biology
#Kishore Mosanliganti, Harvard Systems Biology
#Lydie Souhait, Harvard Systems Biology
#Luis Ibanez, Kitware Inc (Attending: Monday/Tuesday/Wednesday)
#Vincent Magnotta, UIowa
#Hans Johnson, UIowa
#Xenios Papademetris, Yale
#Gregory S. Fischer, WPI (Mon, Tue, Wed)
#Daniel Blezek, Mayo (Tue-Fri)
#Danielle Pace, Robarts Research Institute / UWO
#Clement Vachet, UNC-Chapel Hill
#Dave Welch, UIowa
#Demian Wassermann, Odyssée lab, INRIA, France
#Manasi Ramachandran, UIowa
#Greg Sharp, MGH
#Rui Li, MGH
#Mehdi Esteghamatian, Robarts Research Institute / UWO
#Misha Milchenko, WUSTL
#Kevin Archie, WUSTL
#Tim Olsen, WUSTL
#Wendy Plesniak BWH (NAC)
#Haiying Liu BWH (NCIGT)
#Curtis Lisle, KnowledgeVis / Isomics
#Diego Cantor, Robarts Research Institute / UWO
#Daniel Haehn, BWH
#Nicolas Rannou, BWH
#Sylvain Jaume, MIT
#Alex Yarmarkovich, Isomics
#Marco Ruiz, UCSD
#Andriy Fedorov, BWH (NA-MIC)
#Harish Doddi, Stanford University
#Saikat Pal, Stanford University
#Scott Hoge, BWH (NCIGT)
#Vandana Mohan, Georgia Tech
#Ivan Kolosev, Georgia Tech
#Behnood Gholami, Georgia Tech
#James Balter, U Michigan
#Dan McShan, U Michigan
#Zhou Shen, U Michigan
#Maria Francesca Spadea, Italy
#Lining Yang, Siemens Corporate Research
#Beatriz Paniagua, UNC-Chapel Hill
#Bennett Landman, Johns Hopkins University
#Snehashis Roy, Johns Hopkins University
#Marta Peroni, Politecnico di Milano
#Sebastien Barre, Kitware, Inc.
#Samuel Gerber, SCI University of Utah
#Ran Tao, SCI University of Utah
#Marcel Prastawa, SCI University of Utah
#Katie Hayes, BWH (NA-MIC)
#Sonia Pujol, BWH (NA-MIC)
#Andras Lasso, Queen's University
#Yong Gao, MGH
#Minjeong Kim, UNC-Chapel Hill
#Guorong Wu, UNC-Chapel Hill
#Jeffrey Yager, UIowa
#Yanling Liu, SAIC/NCI-Frederick
#Ziv Yaniv, Georgetown
#Bjoern Menze, MIT
#Vidya Rajagopalan, Virginia Tech
#Sandy Wells, BWH (NAC, NCIGT)
#Lilla Zollei, MGH (NAC)
#Lauren O'Donnell, BWH
#Florin Talos, BWH (NAC)
#Nobuhiko Hata, BWH (NCIGT)
#Alark Joshi, Yale
#Yogesh Rathi, BWH
#Jimi Malcolm, BWH
#Dustin Scheinost, Yale
#Dominique Belhachemi, Yale
#Sam Song, JHU
#Nathan Cho, JHU
#Julien de Siebenthal, BWH
#Peter Savadjiev, BWH
#Carl-Fredrik Westin, BWH
#John Melonakos, AccelerEyes (Wed & Thu morning)
#Yi Gao, Georgia Tech
#Sylvain Bouix, BWH
#Zhexing Liu, UNC-CH
#Eric Melonakos, BWH
#Lei Qin, BWH
#Giovanna Danagoulian, BWH
#Andrew Rausch, BWH (1st day only)
#Haytham Elhawary, BWH
#Jayender Jagadeesan, BWH
#Marek Kubicki, BWH
#Doug Terry, BWH
#Nathan Hageman, LONI (UCLA)
#Dana Peters, Beth Israel Deaconess
#Sun Woo Lee, BWH
# Melanie Grebe, Siemens Corporate Research
# Megumi Nakao, BWH/NAIST
# Moti Freiman, The Hebrew Univ. of Jerusalem
#Jack Blevins, Acoustic Med Systems
#Michael Halle, BWH
#Amanda Peters, Harvard SEAS
#Joe Stam, NVIDIA (Wednesday, Thursday)
#Petter Risholm, BWH (NCIGT)
#Kimberly Powell, NVIDIA (Wednesday)
#Padma Akella, BWH (NCIGT)
#Clif Burdette, Acousticmed (Mon, Tue, Wed)

== Logistics ==
*'''Dates:''' June 22-26, 2009
*'''Location:''' MIT. [[Meeting_Locations:MIT_Grier_A_%26B|Grier Rooms A & B: 34-401A & 34-401B]].
*'''Registration Fee:''' $260 (covers the cost of breakfast, lunch and coffee breaks for the week). Due by Friday, June 12th, 2009. Please make checks out to "Massachusetts Institute of Technology" and mail to: Donna Kaufman, MIT, 77 Massachusetts Ave., 38-409a, Cambridge, MA 02139. Receipts will be provided by email as checks are received. Please send questions to dkauf at mit.edu. '''If this is your first event and you are attending for only one day, the registration fee is waived.''' Please let us know, so that we can cover the costs with one of our grants.
*'''Registration Method''' Add your name to the Attendee List section of this page
*'''Hotel:''' We have a group rate of $189/night (plus tax) at the Le Meridien (which used to be the Hotel at MIT). [http://www.starwoodmeeting.com/Book/MITDECSE Please click here to reserve.] This rate is good only through June 1.
*Here is some information about several other Boston area hotels that are convenient to NA-MIC events: [[Boston_Hotels|Boston_Hotels]]. Summer is tourist season in Boston, so please book your rooms early.
*2009 Summer Project Week [[NA-MIC/Projects/Theme/Template|'''Template''']]
*[[2008_Summer_Project_Week#Projects|Last Year's Projects as a reference]]
*For hosting projects, we are planning to make use of the NITRC resources. See [[NA-MIC_and_NITRC | Information about NITRC Collaboration]]

2009 Summer Project Week Slicer3 registration

2009-06-10T17:38:53Z

Rauscha: /* Key Investigators */

__NOTOC__
<gallery>
Image:PW2009-v3.png|[[2009_Summer_Project_Week|Project Week Main Page]]
Image:Aparc-overlay-affine.png|Overlay of affine registered freesurfer label map on DTI data.
Image:T2-base masked rigid-linear-affine.png|Rigid-Affine-Bspline registration of T2 to DTI data.
</gallery>

==Key Investigators==
* BWH: Andrew Rausch, Sylvain Bouix, Marek Kubicki

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>
We are determining which methods of registration in slicer (and other tools) work best for registration of various types of volumes. Our focus is currently on registering data from structural to DTI space.

</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>
We're going through each type of registration and registration pipeline in Slicer 3 with a specific image to test which methods give the best results. We are also performing comparisons with FSL's FLIRT and FNIRT tools.

</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>
Some of Slicer 3's registration works ok, but it currently has no good way to register from structural to DTI, and no way to apply a similar transform to a label map from structural space. Currently we must use FSL tools to accomplish this.

</div>
</div>

<div style="width: 970%; float: left;">

==References==
http://www.na-mic.org/Wiki/index.php/Projects:DBP2:Harvard:Registration_Documentation

</div>

2009 Summer Project Week Slicer3 registration

2009-06-10T17:37:56Z

Rauscha:

__NOTOC__
<gallery>
Image:PW2009-v3.png|[[2009_Summer_Project_Week|Project Week Main Page]]
Image:Aparc-overlay-affine.png|Overlay of affine registered freesurfer label map on DTI data.
Image:T2-base masked rigid-linear-affine.png|Rigid-Affine-Bspline registration of T2 to DTI data.
</gallery>

==Key Investigators==
* BWH: Andrew Rausch, Sylvain Bouix, Marek Kubicki

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>
We are determining which methods of registration in slicer (and other tools) work best for registration of various types of volumes. Our focus is currently on registering data from structural to DTI space.

</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>
We're going through each type of registration and registration pipeline in Slicer 3 with a specific image to test which methods give the best results. We are also performing comparisons with FSL's FLIRT and FNIRT tools.

</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>
Some of Slicer 3's registration works ok, but it currently has no good way to register from structural to DTI, and no way to apply a similar transform to a label map from structural space. Currently we must use FSL tools to accomplish this.

</div>
</div>

<div style="width: 970%; float: left;">

==References==</div>
http://www.na-mic.org/Wiki/index.php/Projects:DBP2:Harvard:Registration_Documentation

</div>

2009 Summer Project Week Slicer3 registration

2009-06-10T17:37:34Z

Rauscha:

__NOTOC__
<gallery>
Image:PW2009-v3.png|[[2009_Summer_Project_Week|Project Week Main Page]]
Image:Aparc-overlay-affine.png|Overlay of affine registered freesurfer label map on DTI data.
Image:T2-base masked rigid-linear-affine.png|Rigid-Affine-Bspline registration of T2 to DTI data.
</gallery>

==Key Investigators==
* BWH: Andrew Rausch, Sylvain Bouix, Marek Kubicki

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>
We are determining which methods of registration in slicer (and other tools) work best for registration of various types of volumes. Our focus is currently on registering data from structural to DTI space.

</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>
We're going through each type of registration and registration pipeline in Slicer 3 with a specific image to test which methods give the best results. We are also performing comparisons with FSL's FLIRT and FNIRT tools.

</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>
Some of Slicer 3's registration works ok, but it currently has no good way to register from structural to DTI, and no way to apply a similar transform to a label map from structural space. Currently we must use FSL tools to accomplish this.

</div>
</div>

<div style="width: 970%; float: left;">

==References==</div>
http://www.na-mic.org/Wiki/index.php/Projects:DBP2:Harvard:Registration_Documentation
</div>

2009 Summer Project Week Slicer3 registration

2009-06-10T15:26:46Z

Rauscha: Created page with '__NOTOC__ <gallery> Image:PW2009-v3.png|Project Week Main Page Image:genuFAp.jpg|Scatter plot of the original FA data through the genu of the corpus ...'

__NOTOC__
<gallery>
Image:PW2009-v3.png|[[2009_Summer_Project_Week|Project Week Main Page]]
Image:genuFAp.jpg|Scatter plot of the original FA data through the genu of the corpus callosum of a normal brain.
Image:genuFA.jpg|Regression of FA data; solid line represents the mean and dotted lines the standard deviation.
</gallery>

==Instructions for Use of this Template==
#Please create a new wiki page with an appropriate title for your project using the convention Project/<Project Name>
#Copy the entire text of this page into the page created above
#Link the created page into the list of projects for the project event
#Delete this section from the created page
#Send an email to tkapur at bwh.harvard.edu if you are stuck

==Key Investigators==
* UNC: Isabelle Corouge, Casey Goodlett, Guido Gerig
* Utah: Tom Fletcher, Ross Whitaker

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>
We are developing methods for analyzing diffusion tensor data along fiber tracts. The goal is to be able to make statistical group comparisons with fiber tracts as a common reference frame for comparison.

</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>

Our approach for analyzing diffusion tensors is summarized in the IPMI 2007 reference below. The main challenge to this approach is <foo>.

Our plan for the project week is to first try out <bar>,...

</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>
Software for the fiber tracking and statistical analysis along the tracts has been implemented. The statistical methods for diffusion tensors are implemented as ITK code as part of the [[NA-MIC/Projects/Diffusion_Image_Analysis/DTI_Software_and_Algorithm_Infrastructure|DTI Software Infrastructure]] project. The methods have been validated on a repeated scan of a healthy individual. This work has been published as a conference paper (MICCAI 2005) and a journal version (MEDIA 2006). Our recent IPMI 2007 paper includes a nonparametric regression method for analyzing data along a fiber tract.

</div>
</div>

<div style="width: 970%; float: left;">

==References==
*Fletcher P, Tao R, Jeong W, Whitaker R. [http://www.na-mic.org/publications/item/view/634 A volumetric approach to quantifying region-to-region white matter connectivity in diffusion tensor MRI.] Inf Process Med Imaging. 2007;20:346-358. PMID: 17633712.
* Corouge I, Fletcher P, Joshi S, Gouttard S, Gerig G. [http://www.na-mic.org/publications/item/view/292 Fiber tract-oriented statistics for quantitative diffusion tensor MRI analysis.] Med Image Anal. 2006 Oct;10(5):786-98. PMID: 16926104.
* Corouge I, Fletcher P, Joshi S, Gilmore J, Gerig G. [http://www.na-mic.org/publications/item/view/1122 Fiber tract-oriented statistics for quantitative diffusion tensor MRI analysis.] Int Conf Med Image Comput Comput Assist Interv. 2005;8(Pt 1):131-9. PMID: 16685838.
* Goodlett C, Corouge I, Jomier M, Gerig G, A Quantitative DTI Fiber Tract Analysis Suite, The Insight Journal, vol. ISC/NAMIC/ MICCAI Workshop on Open-Source Software, 2005, Online publication: http://hdl.handle.net/1926/39 .

</div>

2009 Summer Project Week

2009-06-05T20:04:05Z

Rauscha: /* Attendee List */

Back to [[Project Events]], [[Events]]

[[Image:PW2009-v3.png|300px]]

*'''Dates:''' June 22-26, 2009
*'''Location:''' MIT. [[Meeting_Locations:MIT_Grier_A_%26B|Grier Rooms A & B: 34-401A & 34-401B]].

==Introduction to the FIRST JOINT PROJECT WEEK==

We are pleased to announce the FIRST JOINT PROJECT WEEK of hands-on research and development activity for Image-Guided Therapy and Neuroscience applications. Participants will engage in open source programming using the [[NA-MIC-Kit|NA-MIC Kit]], algorithm design, medical imaging sequence development, tracking experiments, and clinical application. The main goal of this event is to move forward the translational research deliverables of the sponsoring centers and their collaborators. Active and potential collaborators are encouraged and welcome to attend this event. This event will be set up to maximize informal interaction between participants.

Active preparation will begin on''' Thursday, April 16th at 3pm ET''', with a kick-off teleconference. Invitations to this call will be sent to members of the sponsoring communities, their collaborators, past attendees of the event, as well as any parties who have expressed an interest in working with these centers. The main goal of the kick-off call is to get an idea of which groups/projects will be active at the upcoming event, and to ensure that there is sufficient coverage for all. Subsequent teleconferences will allow for more focused discussions on individual projects and allow the hosts to finalize the project teams, consolidate any common components, and identify topics that should be discussed in breakout sessions. In the final days leading upto the meeting, all project teams will be asked to fill in a template page on this wiki that describes the objectives and plan of their projects.

The event itself will start off with a short presentation by each project team, driven using their previously created description, and will help all participants get acquainted with others who are doing similar work. In the rest of the week, about half the time will be spent in breakout discussions on topics of common interest of subsets of the attendees, and the other half will be spent in project teams, doing hands-on project work. The hands-on activities will be done in 30-50 small teams of size 2-4, each with a mix of multi-disciplinary expertise. To facilitate this work, a large room at MIT will be setup with several tables, with internet and power access, and each computer software development based team will gather on a table with their individual laptops, connect to the internet to download their software and data, and be able to work on their projects. Teams working on projects that require the use of medical devices will proceed to Brigham and Women's Hospital and carry out their experiments there. On the last day of the event, a closing presentation session will be held in which each project team will present a summary of what they accomplished during the week.

This event is part of the translational research efforts of [http://www.na-mic.org NA-MIC], [http://www.ncigt.org NCIGT], [http://nac.spl.harvard.edu/ NAC], [http://catalyst.harvard.edu/home.html Harvard Catalyst], and [http://www.cimit.org CIMIT]. It is an expansion of the NA-MIC Summer Project Week that has been held annually since 2005. It will be held every summer at MIT and Brigham and Womens Hospital in Boston, typically during the last full week of June, and in Salt Lake City in the winter, typically during the second week of January.

A summary of all past NA-MIC Project Events that this FIRST JOINT EVENT is based on is available [[Project_Events#Past|here]].

== Agenda==
* Monday
** noon-1pm lunch
**1pm: Welcome (Ron Kikinis)
** 1:05-3:30pm Introduce [[#Projects|Projects]] using templated wiki pages (all Project Leads) ([http://wiki.na-mic.org/Wiki/index.php/Project_Week/Template Wiki Template])
** 3:30-5:30pm Start project work
* Tuesday
** 8:30am breakfast
**9:30-10am: NA-MIC Kit Overview (Jim Miller)
** 10-10:30am Slicer 3.4 Update (Steve Pieper)
** 10:30-11am Slicer IGT and Imaging Kit Update Update (Noby Hata, Scott Hoge)
** 11am-12:00pm Breakout Session: [[2009 Project Week Breakout Session: Slicer-Python]] (Demian W)
** noon lunch
** 2:30pm-5pm: [[2009 Project Week Data Clinic|Data Clinic]] (Ron Kikinis)
** 5:30pm adjourn for day
* Wednesday
** 8:30am breakfast
** 9am-12pm Breakout Session: [[2009 Project Week Breakout Session: ITK]] (Luis Ibanez)
** noon lunch
** 2:30pm: Breakout Session: [[2009 Project Week Breakout Session: 3D+T Microscopy Cell Dataset Segmentation]] (Alex G.)
** 5:30pm adjourn for day
* Thursday
** 8:30am breakfast
** 9-11pm Tutorial Contest Presentations
** noon lunch
** 2:30pm: Breakout Session: [[2009 Project Week Breakout Session: XNAT]] (Dan M.)
** 5:30pm adjourn for day
* Friday
** 8:30am breakfast
** 10am-noon: [[Events:TutorialContestJune2009|Tutorial Contest Winner Announcement]] and [[#Projects|Project Progress Updates]]
*** Noon: Lunch boxes and adjourn by 1:30pm.
***We need to empty room by 1:30. You are welcome to use wireless in Stata.
***Please sign up for the developer [http://www.slicer.org/pages/Mailinglist mailing lists]
***Next Project Week [[AHM_2010|in Utah, January 4-8, 2010]]

== Projects ==

The list of projects for this week will go here.
=== Collaboration Projects ===
#[[2009_Summer_Project_Week_Project_Segmentation_of_Muscoskeletal_Images]]
#[[2009_Summer_Project_Week_4D_Imaging| 4D Imaging (Perfusion, Cardiac, etc.) ]] (Junichi, Dan Blezek?, Steve, Alex G?)
#[[2009_Summer_Project_Week_Liver_Ablation_Slicer|Liver Ablation in Slicer (Haiying, Ziv, Noby)]]
#[[2009_Summer_Project_Week_Slicer3_Brainlab_Introduction|SLicer3, BioImage Suite and Brainlab - Introduction to UCLA (Haiying, Xenios, Pratik, Nathan Hageman)]]
#[[2009_Summer_Project_Week_Slicer3_Adaptive_Radiotherapy|Adaptive Radiotherapy - Deformable registration and DICOMRT (Greg Sharp, Steve, Wendy)]]
#Brain DTI Atlas? (Florin, Utah, UNC, GeorgiaTech)
#Slicer module for the computation of fibre dispersion and curving measures (Peter Savadjiev, C-F Westin)
#Xnat user interface improvements for NA-MIC (Dan M, Florin, Ron, Wendy)
#xnat and DICOMRT (Greg Sharp, Dan M) - might be done?
#[[2009_Summer_Project_Week_Hageman_FMTractography | Fluid mechanics tractography and visualization]] (Nathan Hageman UCLA)
#[[2009_Summer_Project_Week_Hageman_DTIDigitalPhantom | DTI digital phantom generator to create validation data sets - webservice/cmdlin module/binaries are downloadable from UCLA ]] (Nathan Hageman UCLA)
#[[2009_Summer_Project_Week_Slicer3_Cortical_Thickness_Pipeline|Cortical Thickness Pipeline (Clement Vachet)]]
#[[2009_Summer_Project_Week_Slicer3_Brainlab_Demo|Demo Brainlab-BioImage Suite-Slicer in BWH OR (Haiying, Isaiah, Nathan Hageman, Haytham)]]
#[[2009_Summer_Project_Week_Skull_Stripping | Skull Stripping]] (Xiaodong, Snehashis Roy, Nicole Aucoin)
#[[2009_Summer_Project_Week_HAMMER_Registration | HAMMER Registration]] (Guorong Wu, Xiaodong Tao, Jim Miller)
#[[2009_Summer_Project_Week_WML_SEgmentation |White Matter Lesion segmentation]] (Minjeong Kim, Xiaodong Tao, Jim Miller)
#[[2009_Summer_Project_Week-FastMarching_for_brain_tumor_segmentation |FastMarching for brain tumor segmentation]] (Fedorov, GeorgiaTech)
#[[2009_Summer_Project_Week_Meningioma_growth_simulation|Meningioma growth simulation]] (Fedorov, Marcel, Ron)
#[[2009_Summer_Project_Week_Automatic_Brain_MRI_Pipeline|Automatic brain MRI processing pipeline]] (Marcel, Hans)
#XNAT integration into Harvard Catalyst i2b2 framework(Gao, Yong)
#[[2009_Summer_Project_Week_Spherical_Mesh_Diffeomorphic_Demons_Registration |Spherical Mesh Diffeomorphic Demons Registration]] (Luis Ibanez,Thomas Yeo, Polina Goland), - (Mon, Tue, Wed)
#[[2009_Summer_Project_Week_MRSI-Module|MRSI Module]] (Bjoern Menze, Jeff Yager, Vince Magnotta)
#[[Measuring Alcohol Stress Interaction]] (Vidya Rajgopalan, Andrey Fedorov)
#DWI/DTI QC and Preparation Tool: DTIPrep (Zhexing Liu)

===IGT Projects:===
#[[2009_Summer_Project_Week_Prostate_Robotics |Prostate Robotics]] (Junichi, Sam, Nathan Cho, Jack), - Mon, Tue, Thursday 7pm-midnight)
#[[2009_Summer_Project_Week_4D_Gated_US_In_Slicer |Gated 4D ultrasound reconstruction for Slicer3]] (Danielle Pace)
#integration of stereo video into Slicer (Mehdi)
#[[2009_Summer_Project_Week_Statistical_Toolbox |multi-modality statistical toolbox for MR T1, T2, fMRI, DTI data]] (Diego Cantor, Sylvain Jaume, Nicholas, Noby)
#neuroendoscope workflow presentation (sebastien barre)
#breakout session on Dynamic Patient Models (James Balter)

===NA-MIC Engineering Projects===
# DICOM Validation and Cleanup Tool (Luis, Sid, Steve, Greg)
# [[Summer2009:Using_ITK_in_python| Using ITK in python]] (Steve, Demian, Jim)
# [[Summer2009:Implementing_parallelism_in_python| Taking advantage of multicore machines & clusters with python]] (Julien de Siebenthal, Sylvain Bouix)
# [[Summer2009:Using_client_server_paradigm_with_python_and_slicer| Deferring heavy computational tasks with python]] (Julien de Siebenthal, Sylvain Bouix)
# [[Summer2009:Using_CUDA_for_stochastic_tractography| Developing realtime feedback using CUDA]] (Julien de Siebenthal, Sylvain Bouix)
# [[2009_Summer_Project_Week_VTK_3D_Widgets_In_Slicer3|VTK 3d Widgets in Slicer3]] (Nicole, Karthik, Sebastien, Wendy)
# [[2009_Summer_Project_Week_Colors_Module |Updates to Slicer3 Colors module]] (Nicole)
# [[EM_Segmenter|EM Segmenter]] (Sylvain Jaume, Nicolas Rannou)
# Plug-in 3D Viewer based on XIP (Lining)
# [[MeshingSummer2009 | IAFE Mesh Modules - improvements and testing]] (Curt, Steve, Vince)
# [[Slicer3 Informatics Workflow Design & XNAT updates | Slicer3 Informatics Workflow Design & XNAT updates for Slicer]] (Wen, Steve, Dan M, Dan B)
# [[BSpline Registration in Slicer3 | BSpline Registration in Slicer3]] (Samuel Gerber,Jim Miller, Ross Whitaker)
# [[EPI Correction in Slicer3 | EPI Correction in Slicer3]] (Ran Tao, Jim Miller, Sylvain Bouix, Tom Fletcher, Ross Whitaker, Julien de Siebenthal)
# [[Summer2009:Registration reproducibility in Slicer|Registration reproducibility in Slicer3]] (Andriy, Luis, Bill, Jim, Steve)
# [[Summer2009:The Vascular Modeling Toolkit in 3D Slicer | The Vascular Modeling Toolkit in 3D Slicer]] (Daniel Haehn)
===CUDA Projects===
#[[2009_Summer_Project_Week_Registration_for_RT|2d/3d Registration (and GPGPU acceleration) for Radiation Therapy]] (Sandy Wells, Jim Balter, and others)
#[[2009_Summer_Project_Week_Statistical_Toolbox |multi-modality statistical toolbox for MR T1, T2, fMRI, DTI data]] (Diego Cantor, Sylvain Jaume, Nicholas, Noby)

== Preparation ==

# Please make sure that you are on the http://public.kitware.com/cgi-bin/mailman/listinfo/na-mic-project-week mailing list
# Join the kickoff TCON on April 16, 3pm ET.
# [[Engineering:TCON_2009|June 18 TCON]] at 3pm ET to tie loose ends. Anyone with un-addressed questions should call.
# By 3pm ET on June 11, 2009: [[Project_Week/Template|Complete a templated wiki page for your project]]. Please do not edit the template page itself, but create a new page for your project and cut-and-paste the text from this template page. If you have questions, please send an email to tkapur at bwh.harvard.edu.
# By 3pm on June 18, 2009: Create a directory for each project on the [[Engineering:SandBox|NAMIC Sandbox]] (Zack)
## Commit on each sandbox directory the code examples/snippets that represent our first guesses of appropriate methods. (Luis and Steve will help with this, as needed)
## Gather test images in any of the Data sharing resources we have (e.g. the BIRN). These ones don't have to be many. At least three different cases, so we can get an idea of the modality-specific characteristics of these images. Put the IDs of these data sets on the wiki page. (the participants must do this.)
## Setup nightly tests on a separate Dashboard, where we will run the methods that we are experimenting with. The test should post result images and computation time. (Zack)
# Please note that by the time we get to the project event, we should be trying to close off a project milestone rather than starting to work on one...
# People doing Slicer related projects should come to project week with slicer built on your laptop.
## Projects to develop extension modules should work with the [http://viewvc.slicer.org/viewcvs.cgi/branches/Slicer-3-4/#dirlist Slicer-3-4 branch] (new code should not be checked into the branch).
## Projects to modify core behavior of slicer should be done on the [http://viewvc.slicer.org/viewcvs.cgi/trunk/ trunk].

==Attendee List==
If you plan to attend, please add your name here.

#Ron Kikinis, BWH (NA-MIC, NAC, NCIGT)
#Ferenc Jolesz, BWH (NCIGT, NAC)
#Clare Tempany, BWH (NCIGT)
#Tina Kapur, BWH (NA-MIC, NCIGT)
#Steve Pieper, Isomics Inc
#Jim Miller, GE Research
#Xiaodong Tao, GE Research
#Randy Gollub, MGH
#Nicole Aucoin, BWH (NA-MIC)
#Dan Marcus, WUSTL
#Junichi Tokuda, BWH (NCIGT)
#Alex Gouaillard, Harvard Systems Biology
#Arnaud Gelas, Harvard Systems Biology
#Kishore Mosanliganti, Harvard Systems Biology
#Lydie Souhait, Harvard Systems Biology
#Luis Ibanez, Kitware Inc
#Vincent Magnotta, UIowa
#Hans Johnson, UIowa
#Xenios Papademetris, Yale
#Gregory S. Fischer, WPI (Mon, Tue, Wed)
#Daniel Blezek, Mayo (Tue-Fri)
#Danielle Pace, Robarts Research Institute / UWO
#Clement Vachet, UNC-Chapel Hill
#Dave Welch, UIowa
#Demian Wassermann, Odyssée lab, INRIA, France
#Manasi Ramachandran, UIowa
#Greg Sharp, MGH
#Rui Li, MGH
#Mehdi Esteghamatian, Robarts Research Institute / UWO
#Misha Milchenko, WUSTL
#Kevin Archie, WUSTL
#Tim Olsen, WUSTL
#Wendy Plesniak BWH (NAC)
#Haiying Liu BWH (NCIGT)
#Curtis Lisle, KnowledgeVis / Isomics
#Diego Cantor, Robarts Research Institute / UWO
#Daniel Haehn, BWH
#Nicolas Rannou, BWH
#Sylvain Jaume, MIT
#Alex Yarmarkovich, Isomics
#Marco Ruiz, UCSD
#Andriy Fedorov, BWH (NA-MIC)
#Harish Doddi, Stanford University
#Saikat Pal, Stanford University
#Scott Hoge, BWH (NCIGT)
#Vandana Mohan, Georgia Tech
#Ivan Kolosev, Georgia Tech
#Behnood Gholami, Georgia Tech
#James Balter, U Michigan
#Dan McShan, U Michigan
#Zhou Shen, U Michigan
#Maria Francesca Spadea, Italy
#Lining Yang, Siemens Corporate Research
#Beatriz Paniagua, UNC-Chapel Hill
#Bennett Landman, Johns Hopkins University
#Snehashis Roy, Johns Hopkins University
#Marta Peroni, Politecnico di Milano
#Sebastien Barre, Kitware, Inc.
#Samuel Gerber, SCI University of Utah
#Ran Tao, SCI University of Utah
#Marcel Prastawa, SCI University of Utah
#Katie Hayes, BWH (NA-MIC)
#Sonia Pujol, BWH (NA-MIC)
#Andras Lasso, Queen's University
#Yong Gao, MGH
#Minjeong Kim, UNC-Chapel Hill
#Guorong Wu, UNC-Chapel Hill
#Jeffrey Yager, UIowa
#Yanling Liu, SAIC/NCI-Frederick
#Ziv Yaniv, Georgetown
#Bjoern Menze, MIT
#Vidya Rajagopalan, Virginia Tech
#Sandy Wells, BWH (NAC, NCIGT)
#Lilla Zollei, MGH (NAC)
#Lauren O'Donnell, BWH
#Florin Talos, BWH (NAC)
#Nobuhiko Hata, BWH (NCIGT)
#Alark Joshi, Yale
#Yogesh Rathi, BWH
#Jimi Malcolm, BWH
#Dustin Scheinost, Yale
#Dominique Belhachemi, Yale
#Sam Song, JHU
#Nathan Cho, JHU
#Julien de Siebenthal, BWH
#Peter Savadjiev, BWH
#Carl-Fredrik Westin, BWH
#John Melonakos, AccelerEyes (Wed & Thu morning)
#Yi Gao, Georgia Tech
#Sylvain Bouix, BWH
#Zhexing Liu, UNC-CH
#Eric Melonakos, BWH
#Lei Qin, BWH
#Giovanna Danagoulian, BWH
#Andrew Rausch, BWH (1st day only)
#Haytham Elhawary, BWH
#Jayender Jagadeesan, BWH
#Marek Kubicki, BWH
#Doug Terry, BWH
#Nathan Hageman, LONI (UCLA)
#Dana Peters, Beth Israel Deaconess
#Sun Woo Lee, BWH

== Logistics ==
*'''Dates:''' June 22-26, 2009
*'''Location:''' MIT. [[Meeting_Locations:MIT_Grier_A_%26B|Grier Rooms A & B: 34-401A & 34-401B]].
*'''Registration Fee:''' $260 (covers the cost of breakfast, lunch and coffee breaks for the week). Due by Friday, June 12th, 2009. Please make checks out to "Massachusetts Institute of Technology" and mail to: Donna Kaufman, MIT, 77 Massachusetts Ave., 38-409a, Cambridge, MA 02139. Receipts will be provided by email as checks are received. Please send questions to dkauf at mit.edu. '''If this is your first event and you are attending for only one day, the registration fee is waived.''' Please let us know, so that we can cover the costs with one of our grants.
*'''Registration Method''' Add your name to the Attendee List section of this page
*'''Hotel:''' We have a group rate of $189/night (plus tax) at the Le Meridien (which used to be the Hotel at MIT). [http://www.starwoodmeeting.com/Book/MITDECSE Please click here to reserve.] This rate is good only through June 1.
*Here is some information about several other Boston area hotels that are convenient to NA-MIC events: [[Boston_Hotels|Boston_Hotels]]. Summer is tourist season in Boston, so please book your rooms early.
*2009 Summer Project Week [[NA-MIC/Projects/Theme/Template|'''Template''']]
*[[2008_Summer_Project_Week#Projects|Last Year's Projects as a reference]]
*For hosting projects, we are planning to make use of the NITRC resources. See [[NA-MIC_and_NITRC | Information about NITRC Collaboration]]

2009 Summer Project Week

2009-06-04T01:27:57Z

Rauscha: /* Attendee List */

Back to [[Project Events]], [[Events]]

*'''Dates:''' June 22-26, 2009
*'''Location:''' MIT. [[Meeting_Locations:MIT_Grier_A_%26B|Grier Rooms A & B: 34-401A & 34-401B]].

==Introduction to the FIRST JOINT PROJECT WEEK==

We are pleased to announce the FIRST JOINT PROJECT WEEK of hands-on research and development activity for Image-Guided Therapy and Neuroscience applications. Participants will engage in open source programming using the [[NA-MIC-Kit|NA-MIC Kit]], algorithm design, medical imaging sequence development, tracking experiments, and clinical application. The main goal of this event is to move forward the translational research deliverables of the sponsoring centers and their collaborators. Active and potential collaborators are encouraged and welcome to attend this event. This event will be set up to maximize informal interaction between participants.

Active preparation will begin on''' Thursday, April 16th at 3pm ET''', with a kick-off teleconference. Invitations to this call will be sent to members of the sponsoring communities, their collaborators, past attendees of the event, as well as any parties who have expressed an interest in working with these centers. The main goal of the kick-off call is to get an idea of which groups/projects will be active at the upcoming event, and to ensure that there is sufficient coverage for all. Subsequent teleconferences will allow for more focused discussions on individual projects and allow the hosts to finalize the project teams, consolidate any common components, and identify topics that should be discussed in breakout sessions. In the final days leading upto the meeting, all project teams will be asked to fill in a template page on this wiki that describes the objectives and plan of their projects.

The event itself will start off with a short presentation by each project team, driven using their previously created description, and will help all participants get acquainted with others who are doing similar work. In the rest of the week, about half the time will be spent in breakout discussions on topics of common interest of subsets of the attendees, and the other half will be spent in project teams, doing hands-on project work. The hands-on activities will be done in 30-50 small teams of size 2-4, each with a mix of multi-disciplinary expertise. To facilitate this work, a large room at MIT will be setup with several tables, with internet and power access, and each computer software development based team will gather on a table with their individual laptops, connect to the internet to download their software and data, and be able to work on their projects. Teams working on projects that require the use of medical devices will proceed to Brigham and Women's Hospital and carry out their experiments there. On the last day of the event, a closing presentation session will be held in which each project team will present a summary of what they accomplished during the week.

This event is part of the translational research efforts of [http://www.na-mic.org NA-MIC], [http://www.ncigt.org NCIGT], [http://nac.spl.harvard.edu/ NAC], [http://catalyst.harvard.edu/home.html Harvard Catalyst], and [http://www.cimit.org CIMIT]. It is an expansion of the NA-MIC Summer Project Week that has been held annually since 2005. It will be held every summer at MIT and Brigham and Womens Hospital in Boston, typically during the last full week of June, and in Salt Lake City in the winter, typically during the second week of January.

A summary of all past NA-MIC Project Events that this FIRST JOINT EVENT is based on is available [[Project_Events#Past|here]].

== Agenda==
* Monday
** noon-1pm lunch
**1pm: Welcome (Ron Kikinis)
** 1:05-3:30pm Introduce [[#Projects|Projects]] using templated wiki pages (all Project Leads) ([http://wiki.na-mic.org/Wiki/index.php/Project_Week/Template Wiki Template])
** 3:30-5:30pm Start project work
* Tuesday
** 8:30am breakfast
**9:30-10am: NA-MIC Kit Overview (Jim Miller)
** 10-10:30am Slicer 3.4 Update (Steve Pieper)
** 10:30-11am Slicer IGT and Imaging Kit Update Update (Noby Hata, Scott Hoge)
** 11am-12:00pm Breakout Session: [[2009 Project Week Breakout Session: Slicer-Python]] (Demian W)
** noon lunch
** 2:30pm-5pm: [[2009 Project Week Data Clinic|Data Clinic]] (Ron Kikinis)
** 5:30pm adjourn for day
* Wednesday
** 8:30am breakfast
** 9am-12pm Breakout Session: [[2009 Project Week Breakout Session: ITK]] (Luis Ibanez)
** noon lunch
** 2:30pm: Breakout Session: [[2009 Project Week Breakout Session: 3D+T Microscopy Cell Dataset Segmentation]] (Alex G.)
** 5:30pm adjourn for day
* Thursday
** 8:30am breakfast
** 9-11pm Tutorial Contest Presentations
** noon lunch
** 2:30pm: Breakout Session: [[2009 Project Week Breakout Session: XNAT]] (Dan M.)
** 5:30pm adjourn for day
* Friday
** 8:30am breakfast
** 10am-noon: [[Events:TutorialContestJune2009|Tutorial Contest Winner Announcement]] and [[#Projects|Project Progress Updates]]
*** Noon: Lunch boxes and adjourn by 1:30pm.
***We need to empty room by 1:30. You are welcome to use wireless in Stata.
***Please sign up for the developer [http://www.slicer.org/pages/Mailinglist mailing lists]
***Next Project Week [[AHM_2010|in Utah, January 4-8, 2010]]

== Projects ==

The list of projects for this week will go here.
=== Collaboration Projects ===
#[[2009_Summer_Project_Week_Project_Segmentation_of_Muscoskeletal_Images]]
#[[2009_Summer_Project_Week_4D_Imaging| 4D Imaging (Perfusion, Cardiac, etc.) ]] (Junichi, Dan Blezek?, Steve, Alex G?)
#[[2009_Summer_Project_Week_Liver_Ablation_Slicer|Liver Ablation in Slicer (Haiying, Ziv, Noby)]]
#[[2009_Summer_Project_Week_Slicer3_Brainlab_Introduction|SLicer3, BioImage Suite and Brainlab - Introduction to UCLA (Haiying, Xenios, Pratik, Nathan Hageman)]]
#Adaptive Radiotherapy - Deformable registration and DICOMRT (Greg Sharp, Steve, Wendy)
#Brain DTI Atlas? (Florin, Utah, UNC, GeorgiaTech)
#Slicer module for the computation of fibre dispersion and curving measures (Peter Savadjiev, C-F Westin)
#Xnat user interface improvements for NA-MIC (Dan M, Florin, Ron, Wendy)
#xnat and DICOMRT (Greg Sharp, Dan M) - might be done?
#Grid Wizard+xnat clinic (Clement Vachet)
#?Fluid Mechanincs Module (Nathan Hageman)
#?DTI digital phantom generator to create validation data sets - webservice/cmdlin module/binaries are downloadable from UCLA (Nathan Hageman)
#Cortical Thickness Pipeline (Clement Vachet, Ipek Oguz)
#[[2009_Summer_Project_Week_Slicer3_Brainlab_Demo|Demo Brainlab-BioImage Suite-Slicer in BWH OR (Haiying, Isaiah, Nathan Hageman)]]
#Skull Stripping (Xiaodong, Snehashis Roy)
#[[2009_Summer_Project_Week-FastMarching_for_brain_tumor_segmentation |FastMarching for brain tumor segmentation]] (Fedorov, GeorgiaTech)
#[[2009_Summer_Project_Week_Meningioma_growth_simulation|Meningioma growth simulation]] (Fedorov, Marcel, Ron)
#Automatic brain MRI processing pipeline (Marcel, Hans)
#XNAT integration into Harvard Catalyst i2b2 framework(Gao, Yong)
#[[2009_Summer_Project_Week_Spherical_Mesh_Diffeomorphic_Demons_Registration |Spherical Mesh Diffeomorphic Demons Registration]] (Luis Ibanez,Thomas Yeo, Polina Goland), - (Mon, Tue, Wed)
#[[2009_Summer_Project_Week_MRSI-Module|MRSI Module]] (Bjoern Menze, Jeff Yager, Vince Magnotta)
#[[Measuring Alcohol Stress Interaction]] (Vidya Rajgopalan, Andrey Fedorov)
#DWI/DTI QC and Preparation Tool: DTIPrep (Zhexing Liu)

===IGT Projects:===
#[[2009_Summer_Project_Week_Prostate_Robotics |Prostate Robotics]] (Junichi, Sam, Nathan Cho, Jack), - Mon, Tue, Thursday 7pm-midnight)
#port 4d gated ultrasound code to Slicer - (Danielle)
#integration of stereo video into Slicer (Mehdi)
#multi-modality statistical toolbox for MR T1, T2, fMRI, DTI data (Diego, sylvain jaume, nicholas, noby)
#neuroendoscope workflow presentation (sebastien barre)
#breakout session on Dynamic Patient Models (James Balter)
#[[2009_Summer_Project_Week_Registration_for_RT|2d/3d Registration (and GPGPU acceleration) for Radiation Therapy]] (Sandy Wells, Jim Balter, and others)

===NA-MIC Engineering Projects===
# DICOM Validation and Cleanup Tool (Luis, Sid, Steve, Greg)
# [[Summer2009:Using_ITK_in_python| Using ITK in python]] (Steve, Demian, Jim)
# [[Summer2009:Implementing_parallelism_in_python| Taking advantage of multicore machines & clusters with python]] (Julien de Siebenthal, Sylvain Bouix)
# [[Summer2009:Using_client_server_paradigm_with_python_and_slicer| Deferring heavy computational tasks with python]] (Julien de Siebenthal, Sylvain Bouix)
# [[Summer2009:Using_CUDA_for_stochastic_tractography| Developing realtime feedback using CUDA]] (Julien de Siebenthal, Sylvain Bouix)
# [[2009_Summer_Project_Week_VTK_3D_Widgets_In_Slicer3|VTK 3d Widgets in Slicer3]] (Nicole, Karthik, Sebastien, Wendy)
# [[2009_Summer_Project_Week_Colors_Module |Updates to Slicer3 Colors module]] (Nicole)
# [[EM_Segmenter|EM Segmenter]] (Sylvain Jaume, Nicolas Rannou)
# Plug-in 3D Viewer based on XIP (Lining)
# [[MeshingSummer2009 | IAFE Mesh Modules - improvements and testing]] (Curt, Steve, Vince)
# [[Slicer3 Informatics Workflow Design & XNAT updates | Slicer3 Informatics Workflow Design & XNAT updates for Slicer]] (Wen, Steve, Dan M, Dan B)
# [[BSpline Registration in Slicer3 | BSpline Registration in Slicer3]] (Samuel Gerber,Jim Miller, Ross Whitaker)
# [[EPI Correction in Slicer3 | EPI Correction in Slicer3]] (Ran Tao, Jim Miller, Sylvain Bouix, Tom Fletcher, Ross Whitaker, Julien de Siebenthal)
# Fix [http://www.na-mic.org/Bug/view.php?id=416 bug 416] in registration (Andriy, Luis, Bill, Jim, Steve)
# [[Summer2009:The Vascular Modeling Toolkit in 3D Slicer | The Vascular Modeling Toolkit in 3D Slicer]] (Daniel Haehn)

== Preparation ==

# Please make sure that you are on the http://public.kitware.com/cgi-bin/mailman/listinfo/na-mic-project-week mailing list
# Join the kickoff TCON on April 16, 3pm ET.
# [[Engineering:TCON_2009|June 18 TCON]] at 3pm ET to tie loose ends. Anyone with un-addressed questions should call.
# By 3pm ET on June 11, 2009: [[Project_Week/Template|Complete a templated wiki page for your project]]. Please do not edit the template page itself, but create a new page for your project and cut-and-paste the text from this template page. If you have questions, please send an email to tkapur at bwh.harvard.edu.
# By 3pm on June 18, 2009: Create a directory for each project on the [[Engineering:SandBox|NAMIC Sandbox]] (Zack)
## Commit on each sandbox directory the code examples/snippets that represent our first guesses of appropriate methods. (Luis and Steve will help with this, as needed)
## Gather test images in any of the Data sharing resources we have (e.g. the BIRN). These ones don't have to be many. At least three different cases, so we can get an idea of the modality-specific characteristics of these images. Put the IDs of these data sets on the wiki page. (the participants must do this.)
## Setup nightly tests on a separate Dashboard, where we will run the methods that we are experimenting with. The test should post result images and computation time. (Zack)
# Please note that by the time we get to the project event, we should be trying to close off a project milestone rather than starting to work on one...
# People doing Slicer related projects should come to project week with slicer built on your laptop.
## Projects to develop extension modules should work with the [http://viewvc.slicer.org/viewcvs.cgi/branches/Slicer-3-4/#dirlist Slicer-3-4 branch] (new code should not be checked into the branch).
## Projects to modify core behavior of slicer should be done on the [http://viewvc.slicer.org/viewcvs.cgi/trunk/ trunk].

==Attendee List==
If you plan to attend, please add your name here.

#Ron Kikinis, BWH (NA-MIC, NAC, NCIGT)
#Ferenc Jolesz, BWH (NCIGT, NAC)
#Clare Tempany, BWH (NCIGT)
#Tina Kapur, BWH (NA-MIC, NCIGT)
#Steve Pieper, Isomics Inc
#Jim Miller, GE Research
#Xiaodong Tao, GE Research
#Randy Gollub, MGH
#Nicole Aucoin, BWH (NA-MIC)
#Dan Marcus, WUSTL
#Junichi Tokuda, BWH (NCIGT)
#Alex Gouaillard, Harvard Systems Biology
#Arnaud Gelas, Harvard Systems Biology
#Kishore Mosanliganti, Harvard Systems Biology
#Lydie Souhait, Harvard Systems Biology
#Luis Ibanez, Kitware Inc
#Vincent Magnotta, UIowa
#Hans Johnson, UIowa
#Xenios Papademetris, Yale
#Gregory S. Fischer, WPI (Mon, Tue, Wed)
#Daniel Blezek, Mayo (Tue-Fri)
#Danielle Pace, Robarts Research Institute / UWO
#Clement Vachet, UNC-Chapel Hill
#Dave Welch, UIowa
#Demian Wassermann, Odyssée lab, INRIA, France
#Manasi Ramachandran, UIowa
#Greg Sharp, MGH
#Rui Li, MGH
#Mehdi Esteghamatian, Robarts Research Institute / UWO
#Misha Milchenko, WUSTL
#Kevin Archie, WUSTL
#Tim Olsen, WUSTL
#Wendy Plesniak BWH (NAC)
#Haiying Liu BWH (NCIGT)
#Curtis Lisle, KnowledgeVis / Isomics
#Diego Cantor, Robarts Research Institute / UWO
#Daniel Haehn, BWH
#Nicolas Rannou, BWH
#Sylvain Jaume, MIT
#Alex Yarmarkovich, Isomics
#Marco Ruiz, UCSD
#Andriy Fedorov, BWH (NA-MIC)
#Harish Doddi, Stanford University
#Saikat Pal, Stanford University
#Scott Hoge, BWH (NCIGT)
#Vandana Mohan, Georgia Tech
#Ivan Kolosev, Georgia Tech
#Behnood Gholami, Georgia Tech
#James Balter, U Michigan
#Dan McShan, U Michigan
#Zhou Shen, U Michigan
#Maria Francesca Spadea, Italy
#Lining Yang, Siemens Corporate Research
#Beatriz Paniagua, UNC-Chapel Hill
#Bennett Landman, Johns Hopkins University
#Snehashis Roy, Johns Hopkins University
#Marta Peroni, Politecnico di Milano
#Sebastien Barre, Kitware, Inc.
#Samuel Gerber, SCI University of Utah
#Ran Tao, SCI University of Utah
#Marcel Prastawa, SCI University of Utah
#Katie Hayes, BWH (NA-MIC)
#Sonia Pujol, BWH (NA-MIC)
#Andras Lasso, Queen's University
#Yong Gao, MGH
#Minjeong Kim, UNC-Chapel Hill
#Guorong Wu, UNC-Chapel Hill
#Jeffrey Yager, UIowa
#Yanling Liu, SAIC/NCI-Frederick
#Ziv Yaniv, Georgetown
#Bjoern Menze, MIT
#Vidya Rajagopalan, Virginia Tech
#Sandy Wells, BWH (NAC, NCIGT)
#Lilla Zollei, MGH (NAC)
#Lauren O'Donnell, BWH
#Florin Talos, BWH (NAC)
#Nobuhiko Hata, BWH (NCIGT)
#Alark Joshi, Yale
#Yogesh Rathi, BWH
#Jimi Malcolm, BWH
#Dustin Scheinost, Yale
#Dominique Belhachemi, Yale
#Sam Song, JHU
#Nathan Cho, JHU
#Julien de Siebenthal, BWH
#Peter Savadjiev, BWH
#Carl-Fredrik Westin, BWH
#John Melonakos, AccelerEyes (Wed & Thu morning)
#Yi Gao, Georgia Tech
#Sylvain Bouix, BWH
#Zhexing Liu, UNC-CH
#Eric Melonakos, BWH
#Lei Qin, BWH
#Giovanna Danagoulian, BWH
#Andrew Rausch, BWH (1st day only)

== Logistics ==
*'''Dates:''' June 22-26, 2009
*'''Location:''' MIT. [[Meeting_Locations:MIT_Grier_A_%26B|Grier Rooms A & B: 34-401A & 34-401B]].
*'''Registration Fee:''' $260 (covers the cost of breakfast, lunch and coffee breaks for the week). Due by Friday, June 12th, 2009. Please make checks out to "Massachusetts Institute of Technology" and mail to: Donna Kaufman, MIT, 77 Massachusetts Ave., 38-409a, Cambridge, MA 02139. Receipts will be provided by email as checks are received. Please send questions to dkauf at mit.edu. '''If this is your first event and you are attending for only one day, the registration fee is waived.''' Please let us know, so that we can cover the costs with one of our grants.
*'''Registration Method''' Add your name to the Attendee List section of this page
*'''Hotel:''' We have a group rate of $189/night (plus tax) at the Le Meridien (which used to be the Hotel at MIT). [http://www.starwoodmeeting.com/Book/MITDECSE Please click here to reserve.] This rate is good only through June 1.
*Here is some information about several other Boston area hotels that are convenient to NA-MIC events: [[Boston_Hotels|Boston_Hotels]]. Summer is tourist season in Boston, so please book your rooms early.
*2009 Summer Project Week [[NA-MIC/Projects/Theme/Template|'''Template''']]
*[[2008_Summer_Project_Week#Projects|Last Year's Projects as a reference]]
*For hosting projects, we are planning to make use of the NITRC resources. See [[NA-MIC_and_NITRC | Information about NITRC Collaboration]]

Projects:DBP2:Harvard:Registration Documentation:Compare

2009-05-28T20:44:00Z

Rauscha:

This page aims to create a concise comparison of registration methods to show the difference in quality of registration between FSL 4.1 and Slicer 3.3. All registrations were done with case 01031 T2w as the target and case 01053 T2w as the source image. We've used only masked images for T2w to DTI baseline because anything else is expected to fail.

Other types of registrations and a link to the files used can be found [[Projects:DBP2:Harvard:Registration_Documentation|here]].

Note: for FSL all files need to be in nifti format. For me, FSL was interpreting my masked baseline image strangely so I had to flip the nrrd before converting to nifti using unu flip. If you encounter problems while trying this yourself, view your nifti files in FSL before registration to see if they are oriented properly.

----

=T2 to Baseline registration=
In this registration we take a masked T2 image as our source, and register it to the corresponding masked baseline image target using FSL's FLIRT and FNIRT tools, and in Slicer 3 by the 'Registration -> Rigid Registration' and 'Registration -> Deformable BSpline Registration' modules.

==Rigid==
[[File:T2-baseline rigid target FSL slicer.png|900px|thumb|left| Rigid registration comparison: Very similar, only small differences viewable. Both are properly aligned. ]]

<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>
Rigid registration is very similar between the two tools. Differences are only visible when switching back and forth in slicer. The Slicer registration seems slightly more diffuse, but both seem to be properly aligned for further bspline registration.

==BSpline==
[[File:T2-baseline rig-bspline target FSL slicer.png |900px|thumb|left| BSpline warp comparison: FSL is quite good, Slicer is OK, but much worse. Both have slight troubles in lower frontal and temporal, and some issues at back of brain.]]

<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>
Bspline registration shows some differences between the two tools. FSL produces a good registration, only really having trouble in the bottom few slices of the brain when looking at the temporal lobe, and at the very front of the brain, though it does match much of the DTI distortion. Slicer does not fare as well, working only in the middle portion of the brain. It does not match the DTI distortion as well, leaving more of the frontal lobe and back of the brain mismatched. Also, FSL produces a deformation volume that can be used to transform a label map, while Slicer's output transform does not seem to accurately match the volume created when directly outputting a volume, meaning label maps cannot be accurately transformed.

----

=Case to case registration=
==Rigid==
[[ File:Case2case target RGD FSL Slicer.png |1000px|thumb|left| Rigid registration comparison: Mostly good, slight differences in tilt, with masks giving better results than non-masks. ]]
<br><br><br><br><br><br><br><br><br><br><br>
All a rigid registration will manage is aligning the two brains in space, it won't account for any anatomic differences.
Slicer's non-masked rigid registration is close to in line, though with the front slightly rightward and the top slightly leftward of the target.
FSL's non-masked rigid registration is close to in line, though with the front slightly downward and leftward of the target.
Slicer's masked rigid registration is very close. There's a slight tip rightward of the top.
FSL's masked rigid registration seems right on.

==Affine==
[[ File:Case2case target AFFpostRGD FSL Slicer.png |1000px|thumb|left| Rigid-Affine registration comparison: Non-masked are sized by skull and therefore a little off. Non-masked are done better, mostly well alighed with FSL performing better than slicer. ]]
<br><br><br><br><br><br><br><br><br><br><br>
Here we've taken the Rigid images and used them as the source, so that the images you see above have been run through both rigid and affine registration. Here there should be some better sizing, as an affine transform will allow more leeway than a rigid. Don't expect exact matches at this stage.
Slicer's masked affine registration is, oddly, more turned than the rigid, with the front tilting definitively left and the top slightly right. The ventricles match up reasonably well here, however.
FSL's masked affine registration is for the most part well matched on length, width and orientation.
Slicer's non-masked affine registration is well aligned though appears high and posterior to where I would place it by eye. This seems due to the matching of height and width by the skull instead of the brain.
FSL's non-masked affine registration is similarly well aligned but seemingly misplaces, with the front appearing better matched but the posterior worse.

==Bspline==
[[ File:Case2case target BSPpostAFF FSL Slicer.png |1000px|thumb|left| Rigid-affine-Bspline registration comparison: Pretty poor for the most part. Outer boundaries are matched, but little if anything inside is in the same place. Perhaps this is expecte dfor case to case registration. ]]
<br><br><br><br><br><br><br><br><br><br><br>
Here we've taken the results of the Rigid-Affine registration and done a bspline registration on them, meaning images have been through three steps. Here is where good registrations should be found, if ever.
Slicer's masked bspline registration seems ok, though there's maybe a leftward bow to the midsagittal plane, and the back of the brain seems much larger than the target.
FSL's masked bspline registration seems too large all around, though very well matched on ventricles.
Slicer's non-masked bspline registration looks perfectly matched on the skull, but horrible on the brain.
FSL's non-masked bspline registration looks well matched on size and skull, but very few gyri are aligned. Even the sylvian isn't really matched up.

----

Projects:DBP2:Harvard:Registration Documentation:Compare

2009-05-28T20:40:43Z

Rauscha: /* Bspline */

'''UNDER CONSTRUCTION'''

This page aims to create a concise comparison of registration methods to show the difference in quality of registration between FSL 4.1 and Slicer 3.3.

Other types of registrations and a link to the files used can be found [[Projects:DBP2:Harvard:Registration_Documentation|here]].

Note: for FSL all files need to be in nifti format. For me, FSL was interpreting my masked baseline image strangely so I had to flip the nrrd before converting to nifti using unu flip. If you encounter problems while trying this yourself, view your nifti files in FSL before registration to see if they are oriented properly.

----

=T2 to Baseline registration=
In this registration we take a masked T2 image as our source, and register it to the corresponding masked baseline image target using FSL's FLIRT and FNIRT tools, and in Slicer 3 by the 'Registration -> Rigid Registration' and 'Registration -> Deformable BSpline Registration' modules.

==Rigid==
[[File:T2-baseline rigid target FSL slicer.png|900px|thumb|left| Rigid registration comparison: Very similar, only small differences viewable. Both are properly aligned. ]]

<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>
Rigid registration is very similar between the two tools. Differences are only visible when switching back and forth in slicer. The Slicer registration seems slightly more diffuse, but both seem to be properly aligned for further bspline registration.

==BSpline==
[[File:T2-baseline rig-bspline target FSL slicer.png |900px|thumb|left| BSpline warp comparison: FSL is quite good, Slicer is OK, but much worse. Both have slight troubles in lower frontal and temporal, and some issues at back of brain.]]

<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>
Bspline registration shows some differences between the two tools. FSL produces a good registration, only really having trouble in the bottom few slices of the brain when looking at the temporal lobe, and at the very front of the brain, though it does match much of the DTI distortion. Slicer does not fare as well, working only in the middle portion of the brain. It does not match the DTI distortion as well, leaving more of the frontal lobe and back of the brain mismatched. Also, FSL produces a deformation volume that can be used to transform a label map, while Slicer's output transform does not seem to accurately match the volume it created.

----

=Case to case registration=
==Rigid==
[[ File:Case2case target RGD FSL Slicer.png |1000px|thumb|left| Rigid registration comparison: Mostly good, slight differences in tilt, with masks giving better results than non-masks. ]]
<br><br><br><br><br><br><br><br><br><br><br>
All a rigid registration will manage is aligning the two brains in space, it won't account for any anatomic differences.
Slicer's non-masked rigid registration is close to in line, though with the front slightly rightward and the top slightly leftward of the target.
FSL's non-masked rigid registration is close to in line, though with the front slightly downward and leftward of the target.
Slicer's masked rigid registration is very close. There's a slight tip rightward of the top.
FSL's masked rigid registration seems right on.

==Affine==
[[ File:Case2case target AFFpostRGD FSL Slicer.png |1000px|thumb|left| Rigid-Affine registration comparison: Non-masked are sized by skull and therefore a little off. Non-masked are done better, mostly well alighed with FSL performing better than slicer. ]]
<br><br><br><br><br><br><br><br><br><br><br>
Here we've taken the Rigid images and used them as the source, so that the images you see above have been run through both rigid and affine registration. Here there should be some better sizing, as an affine transform will allow more leeway than a rigid. Don't expect exact matches at this stage.
Slicer's masked affine registration is, oddly, more turned than the rigid, with the front tilting definitively left and the top slightly right. The ventricles match up reasonably well here, however.
FSL's masked affine registration is for the most part well matched on length, width and orientation.
Slicer's non-masked affine registration is well aligned though appears high and posterior to where I would place it by eye. This seems due to the matching of height and width by the skull instead of the brain.
FSL's non-masked affine registration is similarly well aligned but seemingly misplaces, with the front appearing better matched but the posterior worse.

==Bspline==
[[ File:Case2case target BSPpostAFF FSL Slicer.png |1000px|thumb|left| Rigid-affine-Bspline registration comparison: Pretty poor for the most part. Outer boundaries are matched, but little if anything inside is in the same place. Perhaps this is expecte dfor case to case registration. ]]
<br><br><br><br><br><br><br><br><br><br><br>
Here we've taken the results of the Rigid-Affine registration and done a bspline registration on them, meaning images have been through three steps. Here is where good registrations should be found, if ever.
Slicer's masked bspline registration seems ok, though there's maybe a leftward bow to the midsagittal plane, and the back of the brain seems much larger than the target.
FSL's masked bspline registration seems too large all around, though very well matched on ventricles.
Slicer's non-masked bspline registration looks perfectly matched on the skull, but horrible on the brain.
FSL's non-masked bspline registration looks well matched on size and skull, but very few gyri are aligned. Even the sylvian isn't really matched up.

----

Projects:DBP2:Harvard:Registration Documentation:Compare

2009-05-28T20:38:58Z

Rauscha: /* Affine */

'''UNDER CONSTRUCTION'''

This page aims to create a concise comparison of registration methods to show the difference in quality of registration between FSL 4.1 and Slicer 3.3.

Other types of registrations and a link to the files used can be found [[Projects:DBP2:Harvard:Registration_Documentation|here]].

Note: for FSL all files need to be in nifti format. For me, FSL was interpreting my masked baseline image strangely so I had to flip the nrrd before converting to nifti using unu flip. If you encounter problems while trying this yourself, view your nifti files in FSL before registration to see if they are oriented properly.

----

=T2 to Baseline registration=
In this registration we take a masked T2 image as our source, and register it to the corresponding masked baseline image target using FSL's FLIRT and FNIRT tools, and in Slicer 3 by the 'Registration -> Rigid Registration' and 'Registration -> Deformable BSpline Registration' modules.

==Rigid==
[[File:T2-baseline rigid target FSL slicer.png|900px|thumb|left| Rigid registration comparison: Very similar, only small differences viewable. Both are properly aligned. ]]

<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>
Rigid registration is very similar between the two tools. Differences are only visible when switching back and forth in slicer. The Slicer registration seems slightly more diffuse, but both seem to be properly aligned for further bspline registration.

==BSpline==
[[File:T2-baseline rig-bspline target FSL slicer.png |900px|thumb|left| BSpline warp comparison: FSL is quite good, Slicer is OK, but much worse. Both have slight troubles in lower frontal and temporal, and some issues at back of brain.]]

<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>
Bspline registration shows some differences between the two tools. FSL produces a good registration, only really having trouble in the bottom few slices of the brain when looking at the temporal lobe, and at the very front of the brain, though it does match much of the DTI distortion. Slicer does not fare as well, working only in the middle portion of the brain. It does not match the DTI distortion as well, leaving more of the frontal lobe and back of the brain mismatched. Also, FSL produces a deformation volume that can be used to transform a label map, while Slicer's output transform does not seem to accurately match the volume it created.

----

=Case to case registration=
==Rigid==
[[ File:Case2case target RGD FSL Slicer.png |1000px|thumb|left| Rigid registration comparison: Mostly good, slight differences in tilt, with masks giving better results than non-masks. ]]
<br><br><br><br><br><br><br><br><br><br><br>
All a rigid registration will manage is aligning the two brains in space, it won't account for any anatomic differences.
Slicer's non-masked rigid registration is close to in line, though with the front slightly rightward and the top slightly leftward of the target.
FSL's non-masked rigid registration is close to in line, though with the front slightly downward and leftward of the target.
Slicer's masked rigid registration is very close. There's a slight tip rightward of the top.
FSL's masked rigid registration seems right on.

==Affine==
[[ File:Case2case target AFFpostRGD FSL Slicer.png |1000px|thumb|left| Rigid-Affine registration comparison: Non-masked are sized by skull and therefore a little off. Non-masked are done better, mostly well alighed with FSL performing better than slicer. ]]
<br><br><br><br><br><br><br><br><br><br><br>
Here we've taken the Rigid images and used them as the source, so that the images you see above have been run through both rigid and affine registration. Here there should be some better sizing, as an affine transform will allow more leeway than a rigid. Don't expect exact matches at this stage.
Slicer's masked affine registration is, oddly, more turned than the rigid, with the front tilting definitively left and the top slightly right. The ventricles match up reasonably well here, however.
FSL's masked affine registration is for the most part well matched on length, width and orientation.
Slicer's non-masked affine registration is well aligned though appears high and posterior to where I would place it by eye. This seems due to the matching of height and width by the skull instead of the brain.
FSL's non-masked affine registration is similarly well aligned but seemingly misplaces, with the front appearing better matched but the posterior worse.

==Bspline==
[[ File:Case2case target BSPpostAFF FSL Slicer.png |1000px|thumb|left| Rigid-affine-Bspline registration comparison: ##### ]]
<br><br><br><br><br><br><br><br><br><br><br>
Slicer's masked bspline registration seems ok, though there's maybe a leftward bow to the midsagittal plane, and the back of the brain seems much larger than the target.
FSL's masked bspline registration seems too large all around, though very well matched on ventricles.
Slicer's non-masked bspline registration looks perfectly matched on the skull, but horrible on the brain.
FSL's non-masked bspline registration looks well matched on size and skull, but very few gyri are aligned. Even the sylvian isn't really matched up.

----

Projects:DBP2:Harvard:Registration Documentation:Compare

2009-05-28T20:36:41Z

Rauscha: /* Bspline */

'''UNDER CONSTRUCTION'''

This page aims to create a concise comparison of registration methods to show the difference in quality of registration between FSL 4.1 and Slicer 3.3.

Other types of registrations and a link to the files used can be found [[Projects:DBP2:Harvard:Registration_Documentation|here]].

Note: for FSL all files need to be in nifti format. For me, FSL was interpreting my masked baseline image strangely so I had to flip the nrrd before converting to nifti using unu flip. If you encounter problems while trying this yourself, view your nifti files in FSL before registration to see if they are oriented properly.

----

=T2 to Baseline registration=
In this registration we take a masked T2 image as our source, and register it to the corresponding masked baseline image target using FSL's FLIRT and FNIRT tools, and in Slicer 3 by the 'Registration -> Rigid Registration' and 'Registration -> Deformable BSpline Registration' modules.

==Rigid==
[[File:T2-baseline rigid target FSL slicer.png|900px|thumb|left| Rigid registration comparison: Very similar, only small differences viewable. Both are properly aligned. ]]

<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>
Rigid registration is very similar between the two tools. Differences are only visible when switching back and forth in slicer. The Slicer registration seems slightly more diffuse, but both seem to be properly aligned for further bspline registration.

==BSpline==
[[File:T2-baseline rig-bspline target FSL slicer.png |900px|thumb|left| BSpline warp comparison: FSL is quite good, Slicer is OK, but much worse. Both have slight troubles in lower frontal and temporal, and some issues at back of brain.]]

<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>
Bspline registration shows some differences between the two tools. FSL produces a good registration, only really having trouble in the bottom few slices of the brain when looking at the temporal lobe, and at the very front of the brain, though it does match much of the DTI distortion. Slicer does not fare as well, working only in the middle portion of the brain. It does not match the DTI distortion as well, leaving more of the frontal lobe and back of the brain mismatched. Also, FSL produces a deformation volume that can be used to transform a label map, while Slicer's output transform does not seem to accurately match the volume it created.

----

=Case to case registration=
==Rigid==
[[ File:Case2case target RGD FSL Slicer.png |1000px|thumb|left| Rigid registration comparison: Mostly good, slight differences in tilt, with masks giving better results than non-masks. ]]
<br><br><br><br><br><br><br><br><br><br><br>
All a rigid registration will manage is aligning the two brains in space, it won't account for any anatomic differences.
Slicer's non-masked rigid registration is close to in line, though with the front slightly rightward and the top slightly leftward of the target.
FSL's non-masked rigid registration is close to in line, though with the front slightly downward and leftward of the target.
Slicer's masked rigid registration is very close. There's a slight tip rightward of the top.
FSL's masked rigid registration seems right on.

==Affine==
[[ File:Case2case target AFFpostRGD FSL Slicer.png |1000px|thumb|left| Rigid-Affine registration comparison: ##### ]]
<br><br><br><br><br><br><br><br><br><br><br>
Here there should be some better sizing, as an affine transform will allow more leeway than a rigid. We shouldn't expect exact matches at this stage however.
Slicer's masked affine registration is, oddly, more turned than the rigid, with the front tilting definitively left and the top slightly right. The ventricles match up reasonably well here, however.
FSL's masked affine registration is for the most part well matched on length, width and orientation.
Slicer's non-masked affine registration is well aligned though appears high and posterior to where I would place it by eye. This seems due to the matching of height and width by the skull instead of the brain.
FSL's non-masked affine registration is similarly well aligned but seemingly misplaces, with the front appearing better matched but the posterior worse.

==Bspline==
[[ File:Case2case target BSPpostAFF FSL Slicer.png |1000px|thumb|left| Rigid-affine-Bspline registration comparison: ##### ]]
<br><br><br><br><br><br><br><br><br><br><br>
Slicer's masked bspline registration seems ok, though there's maybe a leftward bow to the midsagittal plane, and the back of the brain seems much larger than the target.
FSL's masked bspline registration seems too large all around, though very well matched on ventricles.
Slicer's non-masked bspline registration looks perfectly matched on the skull, but horrible on the brain.
FSL's non-masked bspline registration looks well matched on size and skull, but very few gyri are aligned. Even the sylvian isn't really matched up.

----

Projects:DBP2:Harvard:Registration Documentation:Compare

2009-05-28T20:15:25Z

Rauscha: /* Affine */

'''UNDER CONSTRUCTION'''

This page aims to create a concise comparison of registration methods to show the difference in quality of registration between FSL 4.1 and Slicer 3.3.

Other types of registrations and a link to the files used can be found [[Projects:DBP2:Harvard:Registration_Documentation|here]].

Note: for FSL all files need to be in nifti format. For me, FSL was interpreting my masked baseline image strangely so I had to flip the nrrd before converting to nifti using unu flip. If you encounter problems while trying this yourself, view your nifti files in FSL before registration to see if they are oriented properly.

----

=T2 to Baseline registration=
In this registration we take a masked T2 image as our source, and register it to the corresponding masked baseline image target using FSL's FLIRT and FNIRT tools, and in Slicer 3 by the 'Registration -> Rigid Registration' and 'Registration -> Deformable BSpline Registration' modules.

==Rigid==
[[File:T2-baseline rigid target FSL slicer.png|900px|thumb|left| Rigid registration comparison: Very similar, only small differences viewable. Both are properly aligned. ]]

<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>
Rigid registration is very similar between the two tools. Differences are only visible when switching back and forth in slicer. The Slicer registration seems slightly more diffuse, but both seem to be properly aligned for further bspline registration.

==BSpline==
[[File:T2-baseline rig-bspline target FSL slicer.png |900px|thumb|left| BSpline warp comparison: FSL is quite good, Slicer is OK, but much worse. Both have slight troubles in lower frontal and temporal, and some issues at back of brain.]]

<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>
Bspline registration shows some differences between the two tools. FSL produces a good registration, only really having trouble in the bottom few slices of the brain when looking at the temporal lobe, and at the very front of the brain, though it does match much of the DTI distortion. Slicer does not fare as well, working only in the middle portion of the brain. It does not match the DTI distortion as well, leaving more of the frontal lobe and back of the brain mismatched. Also, FSL produces a deformation volume that can be used to transform a label map, while Slicer's output transform does not seem to accurately match the volume it created.

----

=Case to case registration=
==Rigid==
[[ File:Case2case target RGD FSL Slicer.png |1000px|thumb|left| Rigid registration comparison: Mostly good, slight differences in tilt, with masks giving better results than non-masks. ]]
<br><br><br><br><br><br><br><br><br><br><br>
All a rigid registration will manage is aligning the two brains in space, it won't account for any anatomic differences.
Slicer's non-masked rigid registration is close to in line, though with the front slightly rightward and the top slightly leftward of the target.
FSL's non-masked rigid registration is close to in line, though with the front slightly downward and leftward of the target.
Slicer's masked rigid registration is very close. There's a slight tip rightward of the top.
FSL's masked rigid registration seems right on.

==Affine==
[[ File:Case2case target AFFpostRGD FSL Slicer.png |1000px|thumb|left| Rigid-Affine registration comparison: ##### ]]
<br><br><br><br><br><br><br><br><br><br><br>
Here there should be some better sizing, as an affine transform will allow more leeway than a rigid. We shouldn't expect exact matches at this stage however.
Slicer's masked affine registration is, oddly, more turned than the rigid, with the front tilting definitively left and the top slightly right. The ventricles match up reasonably well here, however.
FSL's masked affine registration is for the most part well matched on length, width and orientation.
Slicer's non-masked affine registration is well aligned though appears high and posterior to where I would place it by eye. This seems due to the matching of height and width by the skull instead of the brain.
FSL's non-masked affine registration is similarly well aligned but seemingly misplaces, with the front appearing better matched but the posterior worse.

==Bspline==
[[ File:Case2case target BSPpostAFF FSL Slicer.png |1000px|thumb|left| Rigid-affine-Bspline registration comparison: ##### ]]
<br><br><br><br><br><br><br><br><br><br><br>
TEXT

----