EXTEND SCOPE & LEVEL OF DETAIL OF ANATOMICAL MODELS FROM IN VIVO DATA

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This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. One important advantage of the multi-echo flash (MEF) imaging sequence is that it provides better contrast information for various brain structures, and thus allows more detailed analysis of these structures. As the first approach to make use of the rich contrast information, we worked on a subspace projection method to generate optimally weighted images from the initial MEF data for differentiating different structures. The resulting images can be used to get better delineation of subtle boundaries of anatomical structures, and thus help build more detailed models of brain anatomy. To this end, we have adopted a linear discriminant analysis (LDA) method, which finds a linear projection of the original high-dimensional data into a lower-dimensional subspace while optimizing the contrast-to-noise ratio in the reduced data-space. Preliminary results, in which we used manually labeled data to compute the optimal projections for differentiating different brain structures, demonstrate that the technique helps to differentiate structures including the external (GPe) and internal pallidum (GPi), thalamus, and the brain from dura and fat.