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

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 n + m $$ n+m $$ full stages of the zero-shot learning were divided into two parts to reduce backpropagation depth during training: (1) n $$ n $$ frozen stages initialized with n $$ n $$ -stage pretrained network and (2) m $$ m $$ trainable stages initialized either randomly or m $$ m $$ -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.