View source for 2006 Scientific Report DTI Summary

==== Overview ====

Over the past year, we have been successful in developing tools relevant to diffusion tensor estimation, fiber tractography, and geometric and statistical diffusion tensor analysis. These tools have already been integrated into diffusion-dedicated software (i.e., fiber viewer-UNC, slicer -Harvard/MIT) and are currently being used in multiple clinical projects involving several psychiatric populations (schizophrenia, schizotypal personality disorder, and bipolar disorder), in addition to normal controls. Below we provide more detailed information relevant to progress in the area of diffusion image analysis.

==== Fiber Tract Extraction and Analysis ====

* Fiber tractography is now implemented in both ''fiber viewer'' and ''slicer'' packages. New developments have been made in terms of fiber tractography generation (using anisotropic energy function), post-processing including fiber clustering (i.e., using shape similarity-based fiber separation/grouping), and statistical fiber comparison. The latter technique is especially important for studying population differences and has already been used in clinical investigations of schizophrenia.
* Fiber tractography and geometric estimation of diffusion have been integrated, which makes estimation of diffusion properties along the fiber tracts feasible. These procedures have already been implemented in clinical studies. For example, fiber tractography is being applied to manually drawn cingulum bundle regions of interest to generate the entire cingulum bundle fiber tract. Next, fractional anisotropy and other diffusion anisotropy metrices, such as mode, linear, planar, and spherical diffusion index, as well as geodesic diffusion, will be calculated along the fiber tracts and compared between populations.
* We are developing a method for elastic registration of diffusion tensor images that would permit us to make a direct comparisom of diffusion tensor properties between groups, as well as to generate templates and white matter fiber atlases. This method requires image matching metrics, interpolation of tensor images, and tensor image transformations. Thanks to several programming weeks, a great deal of progress has been made in each of these representative areas.
* We have also developed a method for integrating registration and shape-based tract grouping into population clustering, where brain fibers are labeled into anatomically relevant fiber tracts based on their shape and anatomical location across multiple subjects. This method is currently being applied to the NAMIC schizophrenia data.

==== Fractional Anisotropy Analysis ====

We have developed tools that permit diffusion anisotropy estimation comparison along the entire fiber bundles generated by means of fiber tractography. In addition to popular diffusion indices, such as fractional anisotropy, new diffusion measures that more precisely describe diffusion properties, such as mode, geodesic diffusion, linear, planar, and spherical diffusion indices have been introduced and are being tested on the NAMIC dataset and applied to other clinical projects described below:

* Connections between frontal and temporal lobes including the uncinate fasciculus, fornix, and cingulum bundle are being investigated in schizophrenia, schizotypal personality disorder, and bipolar disorder by applying already existing (fractional anisotropy, relative anisotropy, trace) and new (mode, geodesic diffusion) diffusion metrices to the regions of interest available through the NAMIC Dataset.
* The same diffusion indices are also being estimated along the paths generated with fiber tractography.
* Interhemispheric connections that include the anterior commissure and corpus callosum are being investigated by estimating the anisotropy indices listed above along the fiber paths generated with fiber tractography. In addition, fiber clustering, which permits differentiation of fiber bundles that interconnect with different anatomic subregions of the brain, is being applied to interhemispheric connections.

==== Integration of fMRI and DTI- Path of Interest Analysis ====

We are actively working on ITK tool to register functional MRI (fMRI) and diffusion tensor imaging (DTI) data that would result in the further integration of information from different imaging modalities. Additionally, we are working on a newly developed optimal path analysis (Harvard), which is being tested at both Dartmouth and BWH. This path analysis is meant to integrate diffusion and functional information, as well as generate optimal anatomical connections between brain regions activated during fMRI experiments.

==== DTI Validation ====

Understanding the biological meaning of measures obtained from DTI has been one of our principal objectives. Thus, various projects have been undertaken within the last year to validate both the precision of algorithms generating fiber tracts, by comparing their results with postmortem investigations and anatomic atlases, and the specificity of diffusion measures by finding their correlates among neuropsychological and clinical measures.

==== Algorithm/Software Infrastructure ====

Over the past year, two complete, publicly available tools critical for diffusion tensor imaging and data analysis have been released: slicer DTI module, and fiber viewer.

* ''Slicer DTI module'' developed at Harvard and MIT includes a tensor estimation, as well as newly enhanced algorithms for fiber tractography. Among these are ROI-guided fiber extraction and shape-based clustering. Of further note, slicer can now generate maps of various diffusion indices, including mode, linear, planar and spherical diffusion.
* ''Fiber tracker'' and ''fiber viewer'', both developed at UNC, are stand-alone, diffusion tools capable of tensor estimation, fiber tractography, and geometric and statistical data analysis. The statistical comparison mode is enhanced by the plane-cutting utility, and diffusion indices can be generated and compared along the fiber tract. In addition, several modes of fiber clustering and diffusion tensor image filtering developed at the University of Utah are all part of these new tools.

==== Key Investigators ====

* BWH: Martha Shenton, Marek Kubicki, Marc Niethammer, Sylvain Bouix, Mark Dreusicke, Carl-Fredrik Westin, Raul San Jose, Steve Pieper, Gordon Kindlmann, Doug Markant
* Harvard: Dave Tuch, Josh Snyder
* MIT: Lauren O'Donnell
* UNC: Guido Gerig, Isabelle Corouge, Casey Goodlett, Martin Styner
* Utah: Tom Fletcher, Ross Whitaker, Saurav Basu
* Georgia Tech: Eric Pichon, John Melonakos, Xavier LeFaucheur, Allen Tannenbaum
* Dartmouth: John West, Andrew Saykin, Laura Flashman, Paul Wang, Heather Pixley, Robert Roth