An Unsupervised Deep Learning-Based Approach to Denoise Hyperpolarized 129Xe MR Images

Motivation: Hyperpolarized 129Xe (HXe) MRI is a powerful, FDA-approved modality to assess lung function. While improvements in 129Xe technology enable polarizations of ~50%, low SNR images still hinder image interpretation and quantification. With only modest improvements in polarization levels still possible, other means must be developed to improve HXe SNR. Goal(s): Developed a denoising method to improve HXe SNR. Approach: This study adapts Noise2Void (N2V) denoising for HXe imaging and evaluates its performance on ventilation, diffusion, and gas exchange images. Results: Comparison with Block Matching 3D indicates the effectiveness of N2V in reducing noise and enhancing image quality. Impact: Elevated noise levels in hyperpolarized 129Xe MR images lower image quality and quantitative accuracy and are a confounding factor for clinical interpretation. The objective of this work is to develop a 129Xe-MR image denoising technique based on unsupervised deep learning.