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Abstract #3188

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

Abdullah S. Bdaiwi1,2, Matthew M. Willmering1,2, Riaz Hussain1,2, Laura L. Walkup1,2,3,4,5,6, Jason C. Woods1,2,4,5,6, and Zackary I. Cleveland1,2,3,4,5,6
1Division of Pulmonary Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States, 2Center for Pulmonary Imaging Research, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States, 3Department of Biomedical Engineering, University of Cincinnati, Cincinnati, OH, United States, 4Department of Pediatrics, University of Cincinnati, Cincinnati, OH, United States, 5Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States, 6Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States

Synopsis

Keywords: Hyperpolarized MR (Gas), Lung, Denoise, 129Xe MRI

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.

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Abstract

Keywords