verbatim): Magnetic resonance imaging (MRI) is a noninvasive technique offering high resolution analysis of fundamental brain anatomy. Specific disease conditions are increasingly associated with volume changes in specific brain structures, and/or the presence of focal bright spots on MRI images (hyperintensities) This application introduces an objective, automatic and rapid computer algorithm for postacquisition analysis of brain MRI studies; the Knowledge-Guided MRI Analysis Program. This computer algorithm automatically recognizes brain regions, computes volumes and identifies hyperintensities for each region. In a pilot series of MRI studies from AD and Non-AD demented patients, the output from the algorithm discriminated the two diagnostic categories with sensitivity and specificity averaging 80 percent.
Aim 1 of this proposal will expand the program to include all of the brain from apex to foramen magnum, and fine tune the detection of presently identified and additional brain regions (e.g. cerebellum, brain stem).
Aim 2 will continue analysis of MRI studies, with an emphasis on dementias, and identify associations between regional volumes and/or hyperintensity burdens with specific diagnostic categories. From these associations, discriminant function equations will be developed to aid in differential diagnosis. The program will also provide a rapid initial screen of all brain MRI studies to highlight regions that are outside of the age- and/or gender-adjusted normal range of values, thereby, suggesting areas to be scrutinized first by the neuroradiologist.
The long term goal is to develop KGMAP into a general analysis program for conventional MRI studies. The output will routinely provide volumetric data for a number of brain regions and indicate the locations of hyperintensities. This output will be provided with normative data much like routine blood chemistry results are presently display.
