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Abstract #1353

A deep learning-driven CEST signal discovery model for transmit inhomogeneity correction with single-B1 acquisition

Jianping Xu1, Xingwang Yong1, Zhechuan Dai1, Yi-Cheng Hsu2, Kannie W. Y. Chan3, and Yi Zhang1
1Department of Biomedical Engineering, Zhejiang University, Hangzhou, China, 2MR Collaboration, Siemens Healthcare Ltd., Shanghai, China, Shanghai, China, 3Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China

Synopsis

Keywords: CEST / APT / NOE, CEST / APT / NOE

Motivation: Most B1 inhomogeneity correction approaches require time-consuming multiple z-spectra acquisition under several B1 levels.

Goal(s): To develop an interpretable deep learning pipeline for reliable B1-inhomogeneity corrected CEST imaging without the requirement for multi-B1 acquisitions.

Approach: A novel transformer-based model is designed to predict multiple CEST spectra across a wide range of B1 levels, which originally needed to be acquired during imaging. Equipped with this smart model, our pipeline can yield reliable B1 corrected CEST signals.

Results: The proposed approach enables quality and robust B1 correction on clinical CEST data, providing an effective way to improve quantitative CEST imaging.

Impact: Our proposed deep learning-driven signal discovery model facilitates precise B1-inhomogeneity corrected CEST imaging with single-B1 acquisition, while also offering extending applications, such as improved CEST quantification via additional predicted signals.

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Keywords