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Abstract #1317

Creating parallel-transmission-style MRI with deep learning (deepPTx): a feasibility study using high-resolution whole-brain diffusion at 7T

Xiaodong Ma1, Kamil Uğurbil1, and Xiaoping Wu1
1Center for Magnetic Resonance Research, Radiology, Medical School, University of Minnesota, Minneapolis, MN, United States

Parallel transmission (pTx) has proven capable of addressing two RF-related challenges at ultrahigh fields (≥7 Tesla): RF non-uniformity and power deposition in tissues. However, the conventional pTx workflow is tedious and requires special expertise. Here we propose a novel deep-learning framework, dubbed deepPTx, which aims to train a deep neural network to directly predict pTx-style images from images obtained with single transmission (sTx). The feasibility of deepPTx is demonstrated using 7 Tesla high-resolution, whole-brain diffusion MRI. Our preliminary results show that deepPTx can substantially enhance the image quality and improve the downstream diffusion analysis.

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