This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. Models of brain structures generated from magnetic resonance imaging (MRI) data have grown in complexity in recent years, evolving from simple models with few classes such as gray matter, white matter and cerebrospinal fluid (CSF), into more complex ones representing multiple neural structures separately. This evolution has been possible due to developments in MR data acquisition technology that has yielded finer resolution, higher signal-to-noise ratio (SNR) images and an increasing number of contrast mechanisms, all of which have been used by increasingly sophisticated analysis tools to improve and extend classification. Nevertheless, despite these important advances, a critical unmet goal of this type of modeling is the generation of representations of myelo- and cytoarchitectonic boundaries from in vivo imaging data. In this competing renewal we seek to significantly augment and extend the tools developed in the last cycle. Using models and probabilistic information assembled in the previous cycle, we now propose to develop cortical registration and segmentation tools that are explicitly optimal for the alignment and localization of architectonic boundaries across subjects, focusing on cortical area V5/MT. The surface- based registration utility will then be combined with volumetric intensity information to generate a nonlinear volume warp using a biomechanical model of the brain. This combination will yield a single highly accurate coordinate system applicable across the entire brain. Finally, this coordinate system will be used as initialization for extending the scope and level of detail of our existing segmentation models to explicitly segment bone, air, fat and water for used in MR-based PET attenuation correction. The segmentation will be facilitated by the use of specifically designed sequences incorporating ultra-short TE (UTE) contrast that can directly image bone.
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