CAMP-Net: The Application of Consistency-Aware Multi-Prior in Deep Learning for Rapid MRI

Motivation: Accelerated MRI acquisitions offer reduced imaging scan times but pose challenges in image reconstructions. Tremendous progress has been made to reconstruct accelerated MRI, but it remains challenging to restore high-frequency image details in highly undersampled data. Goal(s): Our goal is to develop a solution that can restore subtle structures even for highly accelerated MRI. Approach: We propose CAMP-Net, a consistency-aware multi-prior framework, that leverages scan-specific features with both image and $$$k$$$-space domain knowledge for MRI reconstruction. Results: Results on a publicly available brain dataset demonstrated that CAMP-Net can achieve high-quality reconstructions with fine brain anatomical structures even at an acceleration factor of 10X. Impact: The successful restoration of subtle structures for MRI with high acceleration factors can significantly reduce MRI scan time in clinical routines, benefiting patients, increasing the access to MRI, and significantly reducing healthcare cost of MRI.