  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* 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.
