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Time-Conditioned Zero-Shot Self-Supervised Reconstruction for Accelerated 3D Ultra-Low-Field MRI
Mart W J van Straten1,2, Beatrice Lena1, Chloé Najac1
1C.J. Gorter MRI Center, Department of Radiology, LUMC, Leiden, the Netherlands.
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:
This image reconstruction method combines a physics-based data-consistency step with a 3D residual network prior and sinusoidal time-step embeddings to improve convergence speed. Data were acquired on a 47 mT Halbach-based scanner using 3D turbo spin-echo sequences with T1-, T2-, and inversion-recovery-T1-weighted contrasts. Additional T1-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.
