Patch2Self denoising of diffusion MRI in the cervical spinal cord improves intra-cord contrast, signal modelling, repeatability, and feature conspicuity

Abstract Quantitative diffusion MRI (dMRI) is a promising technique for evaluating the spinal cord in health and disease. However, low signal-to-noise ratio (SNR) can impede interpretation and quantification of these images. The purpose of this study is to evaluate a denoising approach, Patch2Self, to improve the quality, reliability, and accuracy of quantitative diffusion MRI of the spinal cord. Patch2Self is a self-supervised learning-based denoising method that leverages statistical independence of noise to suppress signal components strictly originating from random fluctuations. We conduct three experiments to validate the denoising performance of Patch2Self on clinical-quality, single-shell dMRI acquisitions with a small number of gradient directions: 1) inter-session scanrescan in healthy volunteers to evaluate enhancements in image contrast and model fitting; 2) repeated intra-session scans in a healthy volunteer to compare signal averaging to Patch2Self; and 3) assessment of spinal cord lesion conspicuity in a multiple sclerosis group. We find that Patch2Self improves intra-cord contrast, signal modeling, SNR, and lesion conspicuity within the spinal cord. This denoising approach holds promise for facilitating reliable diffusion measurements in the spinal cord to investigate biological and pathological processes.