Cardiac Magnetic Resonance (CMR) imaging serves as the gold-standard for
evaluating cardiac morphology and function. Typically, a multi-view CMR stack,
covering short-axis (SA) and 2/3/4-chamber long-axis (LA) views, is acquired
for a thorough cardiac assessment. However, efficiently streamlining the
complex, high-dimensional 3D+T CMR data and distilling compact, coherent
representation remains a challenge. In this work, we introduce a whole-heart
self-supervised learning framework that utilizes masked imaging modeling to
automatically uncover the correlations between spatial and temporal patches
throughout the cardiac stacks. This process facilitates the generation of
meaningful and well-clustered heart representations without relying on the
traditionally required, and often costly, labeled data. The learned heart
representation can be directly used for various downstream tasks. Furthermore,
our method demonstrates remarkable robustness, ensuring consistent
representations even when certain CMR planes are missing/flawed. We train our
model on 14,000 unlabeled CMR data from UK BioBank and evaluate it on 1,000
annotated data. The proposed method demonstrates superior performance to
baselines in tasks that demand comprehensive 3D+T cardiac information, e.g.
cardiac phenotype (ejection fraction and ventricle volume) prediction and
multi-plane/multi-frame CMR segmentation, highlighting its effectiveness in
extracting comprehensive cardiac features that are both anatomically and
pathologically relevant.

我们提出了一种全心自监督学习框架，利用掩蔽成像建模自动发现心脏图像中空间和时间补丁之间的相关性，以生成有意义且高度聚类的心脏表示，该表示可以直接用于多个下游任务，而且在缺失或有缺陷的心磁共振平面上也具有鲁棒性，该方法在综合 3D+T 心脏信息方面具有出色的性能。