Highly accelerated Bloch-Siegert B1+ mapping using variational modeling
Andreas Lesch1, Matthias Schlögl1, Martin Holler2, and Rudolf Stollberger1,3
1Institute of Medical Engineering, Graz University of Technology, Graz, Austria, 2Institute of Mathematics and Scientific Computing, University of Graz, Graz, Austria, 3BioTechMed Graz, Graz, Austria
In this
work we describe a novel method, which is able to reconstruct B1+-maps from
highly under-sampled Bloch-Siegert data. This method is based on variational
methods and a problem specific regularization approach.
We show its capability to achieve successful reconstructions from more
than 100times under-sampled 3D-data in the human brain with a
mean error below 1%. The results are compared to a fully-sampled
reference and a conventional low resolution reconstruction for
different under-sampling factors.
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