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This is a Experimental Study study.

Self-supervised motion-compensated reconstruction for cardiac Cine MRI

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SSL-MoCo achieved high-quality reconstruction performance and improved artifact removal in Cine MRI.
Compared to other self-supervised techniques, SSL-MoCo outperformed supervised learning while utilizing undersampled data.
The proposed framework enables single breath-hold acquisition for cardiac Cine MRI without needing fully-sampled data.
This adaptability may open new research opportunities for dynamic MRI applications beyond cardiac imaging.

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This method improves reconstruction quality in dynamic MRI using self-supervised learning, suggesting advancements in high acceleration rates.

Xu et al. (2025) studied this question.

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This method improves reconstruction quality in dynamic MRI using self-supervised learning, suggesting advancements in high acceleration rates.