Personalized local SAR prediction for parallel transmit neuroimaging at 7T from a single T1-weighted dataset.

ABSTRACT:

Purpose

Parallel RF transmission (PTx) is one of the key technologies enabling high quality imaging at ultra-high fields (≥7T). Compliance with regulatory limits on the local specific absorption rate (SAR) typically involves over-conservative safety margins to account for intersubject variability, which negatively affect the utilization of ultra-high field MR. In this work, we present a method to generate a subject-specific body model from a single T1-weighted dataset for personalized local SAR prediction in PTx neuroimaging at 7T.

Methods

Multi-contrast data were acquired at 7T (N = 10) to establish ground truth segmentations in eight tissue types. A 2.5D convolutional neural network was trained using the T1-weighted data as input in a leave-one-out cross-validation study.

SUBMITTER: Brink WM 

PROVIDER: S-EPMC9314883 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature