PURPOSE: Ultra-low-field (ULF) MRI provides a cost-effective, portable imaging option but has relatively low SNR and long acquisition times compared to standard clinical scans. This study presents a time-conditioned zero-shot self-supervised learning image reconstruction framework (ULF-ZS-SSL) to accelerate 3D-acquired single-coil ULF MRI without relying on external training data. In addition, for faster computation, a transfer-learning (TL) variant (ULF-ZS-SSL-TL) was implemented by pretraining on a small fully-sampled ULF brain dataset and fine-tuning on the target subject in a zero-shot manner. METHODS: -weighted wrist scans were acquired to evaluate cross-anatomy generalization. Both true and retrospectively undersampled data were compared with total variation (TV) and model-based deep learning (MoDL). RESULTS: The ULF-ZS-SSL method produced high-quality reconstructions across all tested contrasts, outperforming zero-filled and TV reconstructions, particularly at higher acceleration factors. Time-step conditioning improved convergence speed, while ULF-ZS-SSL-TL further accelerated the image reconstruction three-fold, enabling full 3D reconstructions in about 3 min. Pretraining on brain data also worked well for wrist reconstructions, indicating cross-anatomy generalization. CONCLUSION: The ULF-ZS-SSL framework enables accurate, training-free reconstruction of undersampled single-coil ULF MRI data, as does the ULF-ZS-SSL-TL approach using minimal training data. The combination of physics-based unrolling, time-step conditioning, and transfer-learning supports rapid and robust application in portable or resource-limited ULF MRI systems.
Straten et al. (Tue,) studied this question.