Patch2self denoising of magnitude- and complex-averaged peripheral nerve DTI data: Challenges and opportunities

Motivation: DTI is effective in characterizing nerve pathology but requires a high-resolution scans, resulting in low SNR data. Goal(s): To analyze the effectiveness of a self-supervised Patch2Self denoising technique on magnitude-averaged and complex-averaged DTI data of human median and ulnar nerve. Approach: The magnitude-averaged and complex-averaged DTI data was denoised with the Patch2Self algorithm; and DTI fitting performed was performed to obtained estimates of FA, AD, and RD in nerves. Results: Patch2Self denoising reduced the variability in the data by 20% at the cost of systematic bias, while complex averaging improved the contrast between muscle/nerve and suppressed fat. Impact: FA estimates obtained from DTI helps monitor nerve regeneration following catastrophic nerve injuries. Improving the quality of the DTI data may improves the reliability of FA estimates so that more subtle treatment effects can be detected.