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Abstract #3084

High-Resolution Deuterium Metabolic Spectroscopy and Imaging with Self-Supervised Deep Denoising

Gang Chen1,2,3, Xinjie Liu1,2,3, Jinrui Zhao1,2, ShaSha Wang1,2, Peng Sun4, Lucio Frydman5, Michal Neeman5, Xin Zhou1,2,3, Maili Liu1,2,3, Chaoyang Liu1,2,3, and Qingjia Bao1,2,3
1Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, China, 2State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan, China, 3University of Chinese Academy of Sciences, Beijing, China, 4Clinical & Technical Support, Philips Healthcare, Beijing, China, 5Weizmann Institute of Science, Rehovot, Israel

Synopsis

Keywords: Deuterium, Deuterium

Motivation: Denoising is a crucial step in deuterium metabolic resonance spectroscopy/imaging (DMRS/DMI ). However, existing supervised learning-based methods require large paired data, which are challenging to obtain in low-sensitivity DMRS/DMI.

Goal(s): Develop a self-supervised deep denoising method that leverages the data acquisition characteristics of DMRS/DMI, eliminating the need for paired data.

Approach: Our method leverages the observation that when target data for training is corrupted samples from the same scene, the neural network can converge to the mean estimator, enabling DMRS/DMI denoising via self-supervised learning.

Results: This method significantly enhances the sensitivity of DMRS/DMI, enabling high spatiotemporal resolution DMRS/DMI.

Impact: The constructed self-supervised deep denoising method significantly enhances SNR, enabling high spatiotemporal resolution DMRS/DMI.

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