Deep Learning-based Reconstruction of Accelerated MR Cholangiopancreatography

Motivation: We address the issue of long scan times in MR Cholangiopancreatography (MRCP), which often leads to poor image quality. Goal(s): We aim to leverage a Deep Learning-based model to accelerate MRCP acquisition. Approach: We acquired two-times parallel imaging accelerated MRCP data at 3T, trained a variational network with retrospective undersampling to a total acceleration factor of 6, and then tested the trained model with both retrospective and prospective 6-times accelerated data, acquired at both 3T and 0.55T. Results: The trained model shows potential to improve MRCP by reducing artifacts and enhancing distal ducts compared to parallel imaging and compressed sensing. Impact: The proposed method effectively removes artifacts in highly accelerated MRCP, shortening scan times from 303 seconds to 138 seconds. Moreover, the corresponding SNR enhancement enables MRCP acquisitions at 0.55T, where traditional image reconstruction methods face challenges.