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Abstract #4640

A K-means Clustering Algorithm for MRI Virtual Observation Points Compression in Local SAR Supervision

Xianglun Mao1, Joseph V. Rispoli1,2, and David J. Love1

1Electrical and Computer Engineering, Purdue University, West Lafayette, IN, United States, 2Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States

Virtual observation point (VOP) compression has become a standard technique to address SAR and temperature constraints in MRI parallel transmission (pTx) design. SAR matrices need to be pre-averaged for the regions of interest, and finally be conservatively compressed to a much smaller set of SAR matrices (i.e. VOPs) that is still capable of reliably calculating a peak spatial SAR. We demonstrated a new approach that used a k-means algorithm for VOP compression. The new VOP compression method does not yield any under-estimation but allows for a lower over-estimation in the peak local SAR prediction.

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