Time‐Conditioned Zero‐Shot Self‐Supervised Reconstruction for Accelerated 3D Ultra‐Low‐Field MRI
Magnetic Resonance in Medicine, vol. 96, pp. 1303–1312
Abstract
ABSTRACT 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 T 1 ‐, T 2 ‐, and inversion‐recovery–T 1 ‐weighted contrasts. Additional T 1 ‐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.
Authors 7
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Mart W J van Straten Aachen C.J. Gorter MRI Center Department of Radiology LUMC Leiden The Netherlands
Leiden University Medical Center · Eindhoven University of Technology
Affiliation as printed
C.J. Gorter MRI Center, Department of Radiology LUMC Leiden the Netherlands
Department of Biomedical Engineering Eindhoven University of Technology Eindhoven the Netherlands
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Leiden University Medical Center
Affiliation as printed
C.J. Gorter MRI Center, Department of Radiology LUMC Leiden the Netherlands
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Leiden University Medical Center
Affiliation as printed
C.J. Gorter MRI Center, Department of Radiology LUMC Leiden the Netherlands
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Ruben van den Broek Aachen C.J. Gorter MRI Center Department of Radiology LUMC Leiden The Netherlands
Leiden University Medical Center
Affiliation as printed
C.J. Gorter MRI Center, Department of Radiology LUMC Leiden the Netherlands
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Leiden University Medical Center · Philips (Germany)
Affiliation as printed
C.J. Gorter MRI Center, Department of Radiology LUMC Leiden the Netherlands
Philips Innovative Technologies Hamburg Hamburg Germany
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Leiden University Medical Center
Affiliation as printed
C.J. Gorter MRI Center, Department of Radiology LUMC Leiden the Netherlands
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Yiming Dong corresponding Aachen C.J. Gorter MRI Center Department of Radiology LUMC Leiden The Netherlands
Leiden University Medical Center
Affiliation as printed
C.J. Gorter MRI Center, Department of Radiology LUMC Leiden the Netherlands
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