Deep Learning-based Accelerated MR Cholangiopancreatography without Fully-sampled Data

TL;DR

This study demonstrates that deep learning-based reconstruction can significantly accelerate MR cholangiopancreatography at different field strengths, reducing scan time by over twofold while preserving image quality, compared to traditional methods.

Contribution

The paper introduces deep learning reconstruction methods for accelerated MRCP that outperform conventional techniques, enabling faster scans without quality loss at 3T and 0.55T.

Findings

DL reconstruction reduces scan time by over 2.4 times.

DL methods outperform parallel imaging and compressed sensing in PSNR and SSIM.

High-quality images are maintained at lower field strength (0.55T).

Abstract

The purpose of this study was to accelerate MR cholangiopancreatography (MRCP) acquisitions using deep learning-based (DL) reconstruction at 3T and 0.55T. A total of 35 healthy volunteers underwent conventional two-fold accelerated MRCP scans at field strengths of 3T and 0.55T. We trained DL reconstructions using two different training strategies, supervised (SV) and self-supervised (SSV), with retrospectively six-fold undersampled data obtained at 3T. We then evaluated the DL reconstructions against standard techniques, parallel imaging (PI) and compressed sensing (CS), focusing on peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) as metrics. We also tested DL reconstructions with prospectively accelerated acquisitions and evaluated their robustness when changing fields strengths from 3T to 0.55T. DL reconstructions demonstrated a reduction in average acquisition time from 599/542 to 255/180 seconds for MRCP at 3T/0.55T. In both retrospective and prospective undersampling, PSNR and SSIM of DL reconstructions were higher than those of PI and CS. At the same time, DL reconstructions preserved the image quality of undersampled data, including sharpness and the visibility of hepatobiliary ducts. In addition, both DL approaches produced high-quality reconstructions at 0.55T. In summary, DL reconstructions trained for highly accelerated MRCP enabled a reduction in acquisition time by a factor of 2.4/3.0 at 3T/0.55T while maintaining the image quality of conventional acquisitions.