Abstract
MRI is the most effective method for screening high-risk breast cancer patients. While current exams rely on the qualitative evaluation of morphological features before and after contrast administration and less on contrast kinetic information, recent developments in fast acquisition methods aim to combine both. However, balancing spatial resolution, temporal resolution and scan time poses a considerable challenge in dynamic MRI. Here, we introduce a radial MRI reconstruction framework for Dynamic Contrast Enhanced (DCE) imaging, termed Enhanced Locally low-rank Imaging for Tissue contrast Enhancement (ELITE), to address these limitations. ELITE combines locally low-rank subspace modeling to capture spatially localized tissue dynamics with deep learning. We evaluate its effectiveness using the publicly available fastMRI breast initiative, demonstrating substantial improvements in CNR and noise reduction while enabling flexible temporal resolution down to 1 second. ELITE also shows benefits in neck and brain imaging, making it a viable alternative for other DCE-MRI applications.
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Acknowledgements
We acknowledge support from RSNA Research Seed Grant RSD1830 received by L.H., NIH R01CA160620 received by S.G.K., R01CA219964 received by L.M. and S.G.K., UH3CA228699 received by S.G.K, and R01EB030549 received by L.F. We thank Prof. Florian Knoll and Dr. Jinwei Zhang for fruitful discussions.
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Kim, S.G., Bae, J., Moy, L. et al. Dynamic breast MRI with Flexible Temporal Resolution Aided by Deep Learning. Nat Commun (2026). https://doi.org/10.1038/s41467-026-72776-z
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DOI: https://doi.org/10.1038/s41467-026-72776-z
