Angiogram-aware deep learning methods for artifact correction of contrast enhanced MR angiography

Abstract

Motivation: CE MRA data is susceptible to motion and noise artifact due to its longer acquisition time. Conventional intensity-based registration are often unreliable, necessitating better artifact correction methods. Goal(s): To provide better artifact correction methods for CE MRA using generative deep learning and angiogram-aware loss functionApproach: two deep learning architectures were trained with/without angiogram-aware loss function. Network accuracy was evaluated based on CE MRA dynamic scans and angiogram. Results: motion correction was successfully performed, resulting in angiograms with PSNR=37.9±4.3 and SSIM=0.97±0.04. angiogram-aware loss function improved the correction accuracy by up to 13 points in PSNR and 17 points in SSIM. Impact: We developed accurate deep learning solutions for CE MRA artifact correction, potentially reducing the need for repeated MRA scans. We also showed that angiogram-aware loss function, which considers the last processing steps of CE MRA data, can improve correction accuracy.