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Motion-Guided Deep Image Prior for Dynamic Cardiac MRI

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M-DIP significantly improved image quality in dynamic cardiac MRI while requiring no training data.
In phantom and in-vivo studies, M-DIP outperformed existing DIP methods and matched supervised deep learning quality.
The method integrates a low-rank approach with motion fields to accurately model image variations during motion.
This technique enables new possibilities for real-time free-breathing cine and LGE imaging with excellent resolution.

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Unsupervised reconstruction method improves image quality in dynamic cardiac MRI, highlighting high fidelity and real-time capabilities.

Vornehm et al. (2025) studied this question.

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