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Abstract #0342

Compressed-Sensing-Accelerated Spherical Deconvolution

Jonathan I. Sperl 1 , Tim Sprenger 1,2 , Ek T. Tan 3 , Marion I. Menzel 1 , Christopher J. Hardy 3 , and Luca Marinelli 3

1 GE Global Research, Munich, BY, Germany, 2 IMETUM, Technical University Munich, Munich, BY, Germany, 3 GE Global Research, Niskayuna, NY, United States

Spherical Deconvolution (SD) is a model-based approach to retrieve angular fiber information from HARDI data. This work proposes to apply concepts and algorithms known in the context of Compressed Sensing, namely L1-sparsity and minimum Total Variation, to regularize the numerically ill-posed inverse problem addressed by SD. Moreover, the proposed method allows undersampling the data to substantially speed up the data acquisition by a factor of three. Improved fiber peak detection and tractography results are shown for simulated as well as for in vivo human subject data.

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