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Towards K-Space Cardiac Foundation Models for Reconstruction-Free Multitask CMR Analysis

MCML Authors

Niklas Bubeck

→ Group Daniel Rückert
Artificial Intelligence in Healthcare and Medicine

Daniel Rückert

Prof. Dr.

Director

Artificial Intelligence in Healthcare and Medicine

Abstract

Before a cardiac MR image is reconstructed, the heart is represented in k-space, which encodes all information needed for analysis—including tissue structure, motion, and functional dynamics. However, information extraction (e.g., downstream analysis tasks such as segmentation or biomarker quantification) is usually performed in the image domain rather than in the k-space domain. This means that the quality of the information extraction is fundamentally limited by the image reconstruction quality. At the same time, the push toward unified models for diverse cardiac downstream tasks has accelerated, driven by advances in efficient representation learning. However, most work has focused on the image domain, overlooking the k-space potential as a direct, information-dense source for end-to-end, multi-task cardiac analysis. As a result, the development of robust and expressive k-space representations and their impact on downstream cardiac assessment remain significantly underexplored. To address this gap, we propose k-space Multi-Task Representation (k-MTR) learning, which enables solving different downstream tasks directly from undersampled k-space. By aligning the k-space and image-domain embeddings, k-MTR establishes a unified representation that simultaneously captures local anatomical detail, global spectral structure, and rich physiological signatures. We show that k-MTR matches or exceeds state-of-the-art image-based and k-space–based baselines across three clinically relevant tasks-disease classification, phenotype regression, and segmentation, providing the first systematic evidence that k-space alone can support comprehensive cardiac analysis. k-MTR represents a pivotal step toward scalable, reconstruction-free cardiac foundation models. The code will be made publicly available after the review process.

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