Abstract #1875

Highly accelerated Bloch-Siegert B1+ mapping using variational modeling

Andreas Lesch¹, Matthias Schlögl¹, Martin Holler², and Rudolf Stollberger^1,3

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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