SandwichNM Denoising with Deep Learning-Based Approach

Abstract

Motivation: SandwichNM is an advanced neuromelanin sensitive MRI method, but it use the same sequence twice and averages them to increase signal-to-noise ratio (SNR) requiring long scan time. Goal(s): The objective is to preserve the SNR while reducing two scans into a single scan. Approach: We proposed deep learning-based denoising method for sandwichNM image to reduce the number of scans. Results: The proposed model achieved an increased PSNR and SSIM with utilizing single scan, which has reduced the scan time to half of the previous one. Impact: SandwichNM is an advanced neuromelanin sensitive MRI technique but requires averaging two scans due to SNR issue. The proposed method enabled higher SNR from single scan which can be useful for scanning patients with Parkinson's disease with involuntary movements.