High-Quality Brain MRI Reconstruction against Unknown Degradation: A Unified Framework with Prompt Learning

Motivation: Spatial resolution, signal-to-noise ratio, and motion artifacts critically matter in any MRI practices. Current methods focus on a single source of known degradation of imaging. A unified framework is desired, which allows for high-quality reconstruction in the face of multiple unknown sources of degradation. Goal(s): We reconstruct high-quality brain MRI against degradations by motion, noise, and low resolution, with an image-to-image translation-based deep neural framework. Approach: We developed a prompt-based learning approach and assessed it on a public brain MRI dataset. Results: Our method offered remarkably improved reconstructions (PSNR=30.96dB, SSIM=0.9133), as compared to two other state-of-the-art methods. Impact: We developed a new methodology that enables high-quality MRI reconstruction from scans corrupted by a mixture of multiple unknown sources of degradations, which commonly happen in clinical and research MRI studies, with a unified reconstruction framework.