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
GE Global Research, Munich, BY, Germany,
Technical University Munich, Munich, BY, Germany,
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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