This SBIR project will develop a medical device software system, """"""""FiberQuant"""""""", for the automatic identification and analysis of white matter fiber tracts based on data provided by diffusion tensor imaging (DTI) magnetic resonance images. The Phase I effort will first demonstrate feasibility by implementing methods and validating their application in a control population, but the resulting technology will be useful with regard to several neurological and psychiatric disorders. Major technical challenges must be overcome if such methods are to be effectively automated and transitioned into a medical device suitable for routine clinical use. To address these challenges, in Phase I we will implement methods for preprocessing DTI data that will include correction for eddy current distortion, B0 distortion, and motion. Building on CorTechs Labs'existing patented and commercially marketed atlas-based segmentation methods we will also create and apply a DTI fiber atlas to drive an automated track identification algorithm. Automatically derived fibers will be validated by comparison to manually derived fibers. Finally we will develop and apply unbiased methods for quantifying white matter properties in the automatically identified fiber tracts. The resulting software tools promise to have widespread scientific and clinical utility. CorTechs Labs has previously demonstrated the capability of developing intelligent image analysis methods for deriving quantitative measures of brain anatomy from MRI measurements, and in transitioning such methods into commercial, FDA-cleared medical devices. Assuming successful transition from Phase I, in Phase II we will extend these techniques by developing a normative database of diffusion-derived measures, to allow physicians to visualize the parts of fiber tracts that show significant deviation from the mean of a control population. Validation studies with a variety of patient groups will be performed to determine whether such tools would provide a means to identify abnormal white matter tissue. Properly validated, these tools will have diagnostic use as well as utility for presurgical planning. We ultimately plan to evaluate these tools in clinical beta site installations, and obtain FDA clearance for them as medical device software suitable for routine clinical use. 1
There is a growing need for effective and efficient computational methods for automatically identifying and quantifying individual white matter fiber tracts in the human brain -- and potential abnormalities within given fiber tracts -- based on diffusion tensor imaging magnetic resonance data. Such tools could be useful for medical diagnosis and presurgical planning, as well as biomedical and pharmaceutical research. In both applications, the identification of individual white matter fiber tracts is of central importance, in order to locate the pathological changes in functionally relevant structures, as well as to provide regions-of-interest that improve statistical power relative to voxel- wise comparisons. There is a large potential market for the tools this project will provide. 1
