  This study accelerates MR cholangiopancreatography (MRCP) acquisitions using
deep learning-based (DL) reconstruction at 3T and 0.55T. Thirty healthy
volunteers underwent conventional two-fold MRCP scans at field strengths of 3T
or 0.55T. We trained a variational network (VN) using retrospectively six-fold
undersampled data obtained at 3T. We then evaluated our method against standard
techniques such as parallel imaging (PI) and compressed sensing (CS), focusing
on peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) as
metrics. Furthermore, considering acquiring fully-sampled MRCP is impractical,
we added a self-supervised DL reconstruction (SSDU) to the evaluating group. We
also tested our method in a prospective accelerated scenario to reflect
real-world clinical applications and evaluated its adaptability to MRCP at
0.55T. Our method demonstrated a remarkable reduction of average acquisition
time from 599/542 to 255/180 seconds for MRCP at 3T/0.55T. In both
retrospective and prospective undersampling scenarios, the PSNR and SSIM of VN
were higher than those of PI, CS, and SSDU. At the same time, VN preserved the
image quality of undersampled data, i.e., sharpness and the visibility of
hepatobiliary ducts. In addition, VN also produced high quality reconstructions
at 0.55T resulting in the highest PSNR and SSIM. In summary, VN trained for
highly accelerated MRCP allows to reduce the acquisition time by a factor of
2.4/3.0 at 3T/0.55T while maintaining the image quality of the conventional
acquisition.
