Abstract #0495

Generalized Model Compression Method For Peak Local SAR Estimation

Joonsung Lee¹, Matthias Gebhardt², Lawrence L. Wald^3,4, Elfar Adalsteinsson^1,4

¹Electrical Engineering & Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA; ²Siemens Healthcare, Erlangen, Germany; ³A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; ⁴Harvard-MIT Division of Health Sciences & Massachusetts Institute of Technology, Cambridge, MA, USA

Parallel transmit (pTx) applications in MRI are limited by local SAR constraints. The peak local SAR can be estimated by monitoring so-called virtual observation points (VOPs) instead of searching exhaustively over all voxels in a 3D model. The VOPs can be pre-computed once for a given model and array configuration and applied in subsequent computation to efficiently estimate peak local SAR due to a given pTx RF pulse. We present a generalization of the original model compression method by VOPs, whereby we maintain the same accuracy in peak local SAR estimates, but with a reduced number of VOPs.