Spatially Regularized Compressed Sensing for High Angular Resolution Diffusion Imaging
نویسندگان
چکیده
منابع مشابه
Two Canonical Representations for Regularized High Angular Resolution Diffusion Imaging
Two canonical representations for regularization of unit sphere functions encountered in the context of high angular resolution diffusion imaging (HARDI) are discussed. One of these is based on spherical harmonic decomposition, and its one-parameter extension via Tikhonov regularization. This case is well-established, and is mainly reviewed for the sake of completeness. The second one is new, a...
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Despite the relative recency of its inception, the theory of compressive sampling (aka compressed sensing) (CS) has already revolutionized multiple areas of applied sciences, a particularly important instance of which is medical imaging. Specifically, the theory appears to provide an answer to the important problem of optimal sampling in MRI, with an ever-increasing body of works reporting stab...
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PURPOSE To accelerate the acquisition of simultaneously high spatial and angular resolution diffusion imaging. METHODS Accelerated imaging is achieved by recovering the diffusion signal at all voxels simultaneously from under-sampled k-q space data using a compressed sensing algorithm. The diffusion signal at each voxel is modeled as a sparse complex Gaussian mixture model. The joint recovery...
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ژورنال
عنوان ژورنال: IEEE Transactions on Medical Imaging
سال: 2011
ISSN: 0278-0062,1558-254X
DOI: 10.1109/tmi.2011.2142189