Motivation: CMR is the golden standard for cardiac diagnosis, and medical data annotation is time-consuming. Thus, screening techniques from unlabeled data can help streamline the cardiac diagnosis process. Goal(s): This work aims to enable cardiac function assessment from unlabeled cardiac MR images using an unsupervised approach with masked image modeling. Approach: Our model creates a robust latent space by reconstructing sparse 2D+T planes (SAX, 2CH, 3CH, and 4CH views) with 70% masking, which can be further disentangled into distinct cardiac temporal states. Results: t-SNE visualization and kNN clustering analysis confirm the association between latent space and cardiac phenotypes, highlighting strong temporal feature extraction. Impact: This method offers a scalable approach for cardiac screening by creating a latent space as well as distinct time-segment embeddings, enabling diverse preliminary analysis of cardiac function and potentially advancing research in cardiovascular disease applications.
Zhang et al. (Tue,) studied this question.