Advancing Low-Field Abdominal MRI: A Comprehensive Dataset with Various Denoising Strategies

Motivation: Abdominal MRI plays a crucial role in non-invasively visualizing abdominal structures. Goal(s): But abdominal MRI data often faces artifacts such as the strong noise. Approach: In this work, a low-field abdominal MRI dataset is presented, comprising multi-contrast multi-repetition abdominal images of 58 healthy subjects. The dataset includes images with different contrasts, i.e., BH (Breath Hold)-T1, FB (Free breath)-T2, RT (Respiratory Triggered)-T2, RT-FST2 (Respiratory Triggered Fat-Suppressed T2). Additionally, various denoising methods, including traditional, supervised and self-supervised approaches, were explored with different structures and various loss functions. Results: These methods show promising results and provide some initial comparative conclusions for abdominal MRI denoising task. Impact: A low-field abdominal MRI dataset is presented, and various denoising methods were explored. During denoising, the main challenge is the trade-off between detail preservation and denoising. This dataset can inspire further exploration and research in this field.