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.

翻译：传统的临床心脏磁共振（CMR）流程依赖于顺序的“先重建后分析”范式，这迫使引入一个不适定的中间步骤，导致可避免的伪影和信息瓶颈。这产生了一个根本性的数学悖论：它试图从欠采样的k空间中恢复高维像素阵列（即图像），而非直接提取诊断实际所需的低维生理标签。为释放k空间的直接诊断潜力，我们提出k-MTR（k空间多任务表征学习），这是一个k空间表征学习框架，将欠采样k空间数据与全采样图像对齐到一个共享的语义流形中。通过利用对42,000名受试者的大规模受控模拟，k-MTR迫使k空间编码器在潜在空间内直接恢复因欠采样而丢失的解剖信息，从而为下游分析绕过显式的逆问题。我们证明，这种潜在对齐使得密集的潜在空间能够直接从欠采样频率中嵌入高级生理语义。在连续表型回归、疾病分类和解剖分割任务中，k-MTR相较于最先进的图像域基线方法实现了极具竞争力的性能。通过展示精确的空间几何结构和多任务特征可以直接从k空间表征中成功恢复，k-MTR为任务感知的心脏MRI工作流程提供了一个稳健的架构蓝图。