Compressed sensing algorithms for accelerating DSI acquisitions (DSI-CS) have helped bring DSI into the realm of clinical feasibility. Here, we assess the efficacy of dictionary-based CS methods in reconstructing high resolution ex vivo DSI of human brain blocks, and provide validation of ex vivo DSI-CS with ground truth optical imaging. We find that reconstruction accuracy, computation time and inter-subject dictionary generalizability are comparable to in vivo results, and that SNR appears influential in determining the limit of attainable reconstruction quality. We also show that fiber orientation estimates of reconstructed data are as accurate as fully-sampled estimates at a microscopic level.
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