Rigid motion-resolved B1+ prediction using deep learning for real-time parallel-transmission pulse design.

ABSTRACT:

Purpose

Tailored parallel-transmit (pTx) pulses produce uniform excitation profiles at 7 T, but are sensitive to head motion. A potential solution is real-time pulse redesign. A deep learning framework is proposed to estimate pTx B1+ distributions following within-slice motion, which can then be used for tailored pTx pulse redesign.

Methods

Using simulated data, conditional generative adversarial networks were trained to predict B1+ distributions in the head following a displacement. Predictions were made for two virtual body models that were not included in training. Predicted maps were compared with ground-truth (simulated, following motion) B₁ maps. Tailored pTx pulses were designed using B₁ maps at the original position (simulated, no motion) and ev

SUBMITTER: Plumley A 

PROVIDER: S-EPMC7613077 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature