Denoising Arterial Spin Labeling Perfusion MRI using Deep Learning Methods

Motivation: The low SNR of arterial spin labeling (ASL) perfusion weighted images (PWI) directly affects the accuracy of cerebral blood flow (CBF) quantification. Goal(s): Our goal was to improve SNR of ASL PWI and access accurate CBF quantification. Approach: We developed XtwASL, a deep learning model for denoising ASL PWI. Evaluation included SNR, peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) for PWI quality as well as ICC and Bland-Altman analysis for CBF assessment. Results: Compared with other methods, the proposed XtwASL significantly improved SNR of ASL in the case of short acquisition time and the CBF after XtwASL denoising was more accurate. Impact: The ASL denoising method XtwASL not only improves SNR but also has good clinical usability in CBF quantification, and this may reduce patient discomfort and artifacts caused by long scan time especially in multi-PLD ASL and high-resolution ASL field.