From VISTA LAB WIKI
Diffusion-weighted imaging methods and diffusion tensors are introduced in the Diffusion Primer. Here we describe the specific methods used to analyze diffusion data. There are software tools for visualization and analysis of fibers. Please consult the pages about MrDiffusion, QUENCH and ConTrack.
This page describes model acquisition parameters, includes screenshots of user interfaces at the scanner and how to fill them in, and describes common problems and troubleshooting tips.
The DTI Preprocessing page describes the steps involved in preprocessing DTI data. It includes instructions for dealing with GE data from Stanford as well as general information for those who have data from Siemens and Phillips scanners. It also describes the dtiRaw preprocessing pipeline.
QUENCH integrates CINCH and dtiQuery. These are two fiber tractography tools. You can download QUENCH from the Software page or you can visit the QUENCH page to learn how to build QUENCH for your specific platform. Sample data can be found in the Vistadata repository within the quench folder. To learn how to use QUENCH visit the QUENCH Tutorial page.
- Jump to the ConTrack Page
ConTrack is a probabilistic tracking algorithm developed by Anthony Sherbondy. This software package is included in mrVISTA and is run primarily through Matlab. The actual executable is run through a bash shell. For info regarding ConTrack and directions on how to generate fibers please visit the ConTrack Page.
A paper describing the probabilistic conTrack algorithm: Sherbondy et al 2008.
There is another paper coming out on tracking the optic radiation, stay tuned for that.
CINCH is an independent software component, developed by David Akers' in collaboration with our team, for visualization and segmentation of tractography results into meaningful fiber bundles. The interface is gesture based, not ROI based, which makes fiber segmentation easier and less dependent on crude ROI drawing (also much more fun). The program is a C++ application. It is designed to work either independently or in coordination with MrDiffusion. You will need around 2GB of memory to run it. CINCH runs under Windows and Linux. To download CINCH, people from Vistalab should go to the software page, and others are welcome too. Alternatively, we encourage you to visit Dave Akers' site where you will find the official version of CINCH.
- Jump to the AFQ Page
AFQ, Automated Fiber Quantification, is a package of functions written in MATLAB that pulls together MrDiffusion STT routines to automatically track and segment major fiber tracts and compare measurements along these tracts between two groups of subjects (e.g., clinical and control groups).
A paper describing the AFQ package is published in PLoS ONE (Yeatman et al., 2012).
An earlier paper that contains an implementation of along-the-tract measurement included in AFQ: Yeatman et al, 2011
- Jump to the LiFE Page
LiFE, Linear Fascicle Evaluation, is a package of functions written in MATLAB that pulls together MrDiffusion to evaluate the connectome in terms of prediction accuracy for diffusion signal.
A paper describing the LiFE is published (Pestilli et al., 2014).
The DTI Data Handling page includes several useful functions for handling DTI data. Including:
Within the Atlases and Templates page you can find instructions for creating a template and creating an atlas, as well as information regarding current templates and atlases that were developed here at the VISTA lab.
The terms atlas and template refer to different things. The template refers to a coordinate frame for the data. The atlas refers to the data from many subjects represented in the template coordinate frame. We use the template to specify how we normalize an individual subject's brain. We use the atlas for statistical testing of the data in the template coordinate frame.
After we estimate the fibers, using between two ROIs, we often want to summarize the fiber properties. There are some scripts that illustrate how to do this, including ctrCompStatsORBundles.m, orDiffusivity.m and orVolume.m. These were developed to study MM and they are in the analysis section of the mrDiffusion directory. In the Tensor Summary Measures page we list some of the issues we considered when writing these scripts.
An alternative to combining data along the path is computing tensor properties (FA/MD/AD/RD) along the pathway trajectory in a stepwise fashion, one node at a time. This can be done using GUI (walk-through), for an example of a batch script see dtiComputeDiffusionPropertiesAlongFG_group.m.
The Tensor Statistics page describes functions that were designed for performing advanced tensor statistics in the context of voxelwise group comparisons. These functions assume that the DTI images have been normalized to the same coordinate frame (e.g. MNI coordinates) so that each voxel coresponds to the same anatomical structure in all subjects.
To compare a single subject to a group template, see dtiAnalyzeSubjectAL.m in mrDiffusion/analysisScripts.
There are several other scripts within VISTASOFT/mrDiffusion/analysisScripts that might be of interest. For example, the most general would be dtiAnalyzeEigVecs, which can do a Watson test (principal eigenvector difference) or it can do a logNormal tensor eigenvector or eigenvalue test. This latter test takes into account all three eigenvectors/values and does a voxel-by-voxel analysis. The one-subject version of this test is dtiAnalyzeSubjectAL, which was written specifically for subject AL but should apply to any single subject.
Another useful script might be dtiAnalyzeFA, which produced a correlation map of FA and basic reading score in normalized brains. It is also voxel-based.
 Applications and Related Packages
Fiber tracking can be used to segment the corpus callosum into regions where fibers share a common projection zone.
There is a page that defines our methods for segmenting the corpus callosum. An example of a segmentation is shown in the figure at the right.
Sometimes we want to compare our estimated fascicles with those derived from other packages. To learn about applying
MrDiffusion is normally ROI or tract-based. In some cases, we wish to perform a whole brain analysis. One approach is to combine mrVista and TBSS from FSL. Another is to combine mrVista and VBM. These pages to describe how to integrate group-based, whole-brain methods from FSL and SPM with MrDiffusion.
The following ROI segmentation protocols for fascicle segmentation is derived from Wakana et al (2007). This paper builds on previous work from Mori (2002), but focuses on the reproducibility of tracking results. As compared to the ROIs defined by Mori, Wakana's ROIs are much larger to limit the effect of variations in individual brains as well as in different raters. Consequently, more fiber cleaning may be necessary after tracking is complete. However, while the reproducibility ratings of Mori's ROIs are unknown, Wakana's ROIs have been tested with three different raters within their institution as well as three outside raters from different institutions.
We used ConTrack to search for the most likely connection between the callosum and regions of functional activation on the superior temporal lobe. This project identified where the callosal zone with temporal fibers projects. We thought it might be MT. But it appears to be the STG instead. How we came to this conclusion is shown here.
MRtrix provides a set of tools to perform diffusion-weighted MR white-matter tractography in a manner robust to crossing fibres, using constrained spherical deconvolution (CSD) and probabilistic streamlines.
MRtrix is released under the GNU General Public License, and is available for download at NITRC
TRACULA (TRACULA wiki) is a tool for automatic reconstruction of a set of major white-matter pathways from diffusion-weighted MR images. It uses global probabilistic tractography with anatomical priors. Prior distributions on the neighboring anatomical structures of each pathway are derived from an atlas and combined with the FreeSurfer cortical parcellation and subcortical segmentation of the subject that is being analyzed to constrain the tractography solutions. This obviates the need for user interaction, e.g., to draw ROIs manually or to set thresholds on path angle and length, and thus automates the application of tractography to large datasets.