Zero‐Shot Self‐Supervised Learning of Single Breath‐Hold Magnetic Resonance Cholangiopancreatography ( MRCP ) Reconstruction

ABSTRACT Purpose To investigate the feasibility of zero‐shot self‐supervised learning reconstruction for reducing breath‐hold times in magnetic resonance cholangiopancreatography (MRCP). Methods Breath‐hold MRCP was acquired from 11 healthy volunteers on 3T scanners using an incoherent k ‐space sampling pattern, leading to a 14‐s acquisition time and an acceleration factor of R = 25. Zero‐shot reconstruction was compared with parallel imaging of respiratory‐triggered MRCP (338 s, R = 3) and compressed sensing reconstruction. For two volunteers, breath‐hold scans (40 s, R = 6) were additionally acquired and retrospectively undersampled to R = 25 to compute peak signal‐to‐noise ratio (PSNR). To address long zero‐shot training time, the full stages of the zero‐shot learning were divided into two parts to reduce backpropagation depth during training: (1) frozen stages initialized with ‐stage pretrained network and (2) trainable stages initialized either randomly or ‐stage pretrained network. Efficiency of our approach was assessed by varying initialization strategies and the number of trainable stages using the retrospectively undersampled data. Results Zero‐shot reconstruction significantly improved visual image quality over compressed sensing, particularly in SNR and ductal delineation, and achieved image quality comparable to that of successful respiratory‐triggered acquisitions with regular breathing patterns. Improved initializations enhanced PSNR and reduced reconstruction time. Adjusting frozen/trainable configurations demonstrated that PSNR decreased only slightly from 38.25 dB (0/13) to 37.67 dB (12/1), while training time decreased up to 6.7‐fold. Conclusion Zero‐shot learning delivers high‐fidelity MRCP reconstructions with reduced breath‐hold times, and the proposed partially trainable approach offers a practical solution for translation into time‐constrained clinical workflows.