No Image, No Problem: End-to-End Multi-Task Cardiac Analysis from Undersampled k-Space

TL;DR

This paper introduces k-MTR, a novel framework that directly extracts diagnostic information from undersampled k-space data in cardiac MRI, bypassing traditional image reconstruction and improving multi-task analysis accuracy.

Contribution

The paper presents a new k-space representation learning method that aligns undersampled k-space with fully-sampled images, enabling direct physiological analysis without explicit image reconstruction.

Findings

k-MTR achieves competitive performance in phenotype regression, disease classification, and segmentation.

The framework effectively recovers spatial and physiological information directly from k-space.

It provides a robust blueprint for task-aware cardiac MRI workflows.

Abstract

Conventional clinical CMR pipelines rely on a sequential "reconstruct-then-analyze" paradigm, forcing an ill-posed intermediate step that introduces avoidable artifacts and information bottlenecks. This creates a fundamental mathematical paradox: it attempts to recover high-dimensional pixel arrays (i.e., images) from undersampled k-space, rather than directly extracting the low-dimensional physiological labels actually required for diagnosis. To unlock the direct diagnostic potential of k-space, we propose k-MTR (k-space Multi-Task Representation), a k-space representation learning framework that aligns undersampled k-space data and fully-sampled images into a shared semantic manifold. Leveraging a large-scale controlled simulation of 42,000 subjects, k-MTR forces the k-space encoder to restore anatomical information lost to undersampling directly within the latent space, bypassing the explicit inverse problem for downstream analysis. We demonstrate that this latent alignment enables the dense latent space embedded with high-level physiological semantics directly from undersampled frequencies. Across continuous phenotype regression, disease classification, and anatomical segmentation, k-MTR achieves highly competitive performance against state-of-the-art image-domain baselines. By showcasing that precise spatial geometries and multi-task features can be successfully recovered directly from the k-space representations, k-MTR provides a robust architectural blueprint for task-aware cardiac MRI workflows.