Classification, Regression and Segmentation directly from k-Space in Cardiac MRI

TL;DR

This paper introduces KMAE, a Transformer-based model that processes raw k-space data directly for cardiac MRI tasks, achieving competitive results and robustness against undersampling, thus enabling more automated and potentially more accurate cardiac diagnoses.

Contribution

The paper presents the first Transformer-based model that directly processes k-space data for classification, regression, and segmentation in cardiac MRI, bypassing traditional image reconstruction.

Findings

KMAE achieves comparable classification and regression performance to image-based methods.

The model attains a myocardium dice score of 0.884 for segmentation.

KMAE maintains robust performance even with 8× undersampled k-space data.

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* 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.