Abstract
Cardiac Magnetic Resonance Imaging (CMR) is the gold standard for diagnosing cardiovascular diseases. Clinical diagnoses predominantly rely on magnitude-only Digital Imaging and Communications in Medicine (DICOM) images, omitting crucial phase information that might provide additional diagnostic benefits. In contrast, k-space is complex-valued and encompasses both magnitude and phase information, while humans cannot directly perceive. In this work, we propose KMAE, a Transformer-based model specifically designed to process k-space data directly, eliminating conventional intermediary conversion steps to the image domain. KMAE can handle critical cardiac disease classification, relevant phenotype regression, and cardiac morphology segmentation tasks. We utilize this model to investigate the potential of k-space-based diagnosis in cardiac MRI. Notably, this model achieves competitive classification and regression performance compared to image-domain methods e.g. Masked Autoencoders (MAEs) and delivers satisfactory segmentation performance with a myocardium dice score of 0.884. Last but not least, our model exhibits robust performance with consistent results even when the k-space is 8\(\times \) undersampled. We encourage the MR community to explore the untapped potential of k-space and pursue end-to-end, automated diagnosis with reduced human intervention. Codes are available at https://github.com/ruochenli99/KMAE_cardiac.
R. Li and J. Pan—Equal contribution.
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Acknowledgements and Disclosure of Interests
This research has been conducted using the UK Biobank Resource under Application Number 87802. This work is funded by the European Research Council (ERC) project Deep4MI (884622). The authors have no competing interests to declare that are relevant to the content of this article.
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Li, R., Pan, J., Zhu, Y., Ni, J., Rueckert, D. (2025). Classification, Regression and Segmentation Directly from K-Space in Cardiac MRI. In: Xu, X., Cui, Z., Rekik, I., Ouyang, X., Sun, K. (eds) Machine Learning in Medical Imaging. MLMI 2024. Lecture Notes in Computer Science, vol 15241. Springer, Cham. https://doi.org/10.1007/978-3-031-73284-3_4
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