Deep Learning-Based Accelerated MR Cholangiopancreatography Without Fully-Sampled Data

Kim J, Nickel MD, Knoll F (2025)


Publication Language: English

Publication Type: Journal article, Online publication

Publication year: 2025

Journal

Book Volume: 38

Pages Range: e70002

Article Number: e70002

Journal Issue: 3

DOI: 10.1002/nbm.70002

Open Access Link: https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/10.1002/nbm.70002

Abstract

The purpose of this study was to accelerate MR cholangiopancreatography (MRCP) acquisitions using deep learning-based (DL) reconstruction at 3 and 0.55 T. A total of 35 healthy volunteers underwent conventional twofold accelerated MRCP scans at field strengths of 3 and 0.55 T. We trained DL reconstructions using two different training strategies, supervised (SV) and self-supervised (SSV), with retrospectively sixfold undersampled data obtained at 3 T. 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 3 to 0.55 T. DL reconstructions demonstrated a reduction in average acquisition time from 599/542 to 255/180 s for MRCP at 3 T/0.55 T. 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 0.55 T. In summary, DL reconstructions trained for highly accelerated MRCP enabled a reduction in acquisition time by a factor of 2.4/3.0 at 3 T/0.55 T while maintaining the image quality of conventional acquisitions.

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How to cite

APA:

Kim, J., Nickel, M.D., & Knoll, F. (2025). Deep Learning-Based Accelerated MR Cholangiopancreatography Without Fully-Sampled Data. NMR in Biomedicine, 38(3), e70002. https://doi.org/10.1002/nbm.70002

MLA:

Kim, Jinho, Marcel Dominik Nickel, and Florian Knoll. "Deep Learning-Based Accelerated MR Cholangiopancreatography Without Fully-Sampled Data." NMR in Biomedicine 38.3 (2025): e70002.

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