Abstract
Identification of microvascular obstruction (MVO) in acute myocardial infarction patients is critical for prognosis and has a direct link to mortality risk. Current approaches using late gadolinium enhancement (LGE) for contrast-enhanced cardiovascular magnetic resonance (CMR) pose risks to the kidney and may not be applicable to many patients. This highlights the need to explore alternative non-contrast imaging methods, such as cine CMR, for MVO identification. However, the scarcity of datasets and the challenges in annotation make the MVO identification in cine CMR challenging and remain largely under-explored. For this purpose, we propose a non-contrast MVO identification framework in cine CMR with a novel coarse-grained mask regularization strategy to effectively utilize information from LGE annotations in training. We train and validate our model on a dataset comprising 680 cases. Our model demonstrates superior performance over competing methods in cine CMR-based MVO identification, proving its feasibility and presenting a novel and patient-friendly approach to the field. The code is available at https://github.com/code-koukai/MVO-identification.
Y. Yan—The work was done during an internship at \(\text {I}^2\text {R}\), A*STAR.
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Acknowledgments
This work is supported by the Agency for Science, Technology and Research under its AI\(^3\) Horizontal Technology Coordinating Office Grant C231118001.
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Yan, Y. et al. (2024). Coarse-Grained Mask Regularization for Microvascular Obstruction Identification from Non-contrast Cardiac Magnetic Resonance. In: Linguraru, M.G., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. MICCAI 2024. Lecture Notes in Computer Science, vol 15001. Springer, Cham. https://doi.org/10.1007/978-3-031-72378-0_22
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