Machine learning RF shimming: Prediction by iteratively projected ridge regression.

ABSTRACT:

Purpose

To obviate online slice-by-slice RF shim optimization and reduce B1+ mapping requirements for patient-specific RF shimming in high-field magnetic resonance imaging.

Theory and methods

RF Shim Prediction by Iteratively Projected Ridge Regression (PIPRR) predicts patient-specific, SAR-efficient RF shims with a machine learning approach that merges learning with training shim design. To evaluate it, a set of B1+ maps was simulated for 100 human heads for a 24-element coil at 7T. Features were derived from tissue masks and the DC Fourier coefficients of the coils' B1+ maps in each slice, which were used for kernelized ridge regression prediction of SAR-efficient RF shim weights. Predicted shims were compared to directly designed shims, circularly polarized mode, and near

SUBMITTER: Ianni JD 

PROVIDER: S-EPMC6107437 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature