Mitigating transmit-B artifacts by predicting parallel transmission images with deep learning: A feasibility study using high-resolution whole-brain diffusion at 7 Tesla.

Journal: Magnetic resonance in medicine

Published Date: Apr 10, 2022

Abstract

PURPOSE: To propose a novel deep learning (DL) approach to transmit-B (B )-artifact mitigation without direct use of parallel transmission (pTx), by predicting pTx images from single-channel transmission (sTx) images.

Authors

Xiaodong Ma

Center for Magnetic Resonance Research, Radiology, Medical School, University of Minnesota, Minneapolis, Minnesota, USA.
Kâmil Uğurbil

Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota.
Xiaoping Wu

Keywords

Artifacts Brain Deep Learning Diffusion Tensor Imaging Feasibility Studies Humans Image Processing, Computer-Assisted

External Resources

View on PubMed Access via DOI PubMed (35403237)

Mitigating transmit-B artifacts by predicting parallel transmission images with deep learning: A feasibility study using high-resolution whole-brain diffusion at 7 Tesla.

Abstract

Authors

Keywords

External Resources

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