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Abstract #2296

Enhancing SNR of CEST MRI Through Noise-to-Noise Deep Learning with K-space Data Consistency

Huabing LIU1,2,3, Ziyi XIA1, Lok Hin LAW1, Zilin CHEN1, Jianpan HUANG4, Dinggang SHEN3,5, and Kannie W.Y. CHAN1,2,6
1Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China, 2Hong Kong Centre for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China, 3School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China, 4Department of Diagnostic Radiology, University of Hong Kong, Hong Kong, China, 5Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China, 6Russell H Morgan Department of Radiology and Radiological Science, Johns Hopkins Medicine, Baltimore, MD, United States

Synopsis

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

Motivation: Endogenous CEST contrast is relatively small and vulnerable to imaging noise.

Goal(s): To develop a retrospective denoising method to enhance SNR of acquired CEST images.

Approach: A denoising neural network was trained using pairs of noisy CEST images through a noise-to-noise deep learning approach, distinct from conventional approaches that use clean or simulated CEST images. A data consistency layer was introduced to preserve center k-space of original CEST images to improve fidelity. A transformer module was used to exploit spatiotemporal correlations among different frequency offsets.

Results: Multipool Lorentzian fitting was performed. Compared to clean images, our method achieved mean correlation coefficient of 0.90.

Impact: Our method can considerably increase SNR of CEST images without sacrificing image fidelity. After denoising, the derived CEST maps could more reliably represent molecular changes in brain regions.

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Keywords