Keywords: Image Reconstruction, Machine Learning/Artificial Intelligence
Various deep learning methods have been proposed for cardiac cine MRI, including accelerated image reconstruction, cardiac motion estimation and segmentation, which are traditionally considered as separate tasks without exploiting the inter-task correlation. In this study, we propose a unified deep learning model to perform accelerated cine image reconstruction, motion estimation and segmentation simultaneously in an iterative framework, where correlations between tasks are exploited by compensating motion in reconstruction, semi-supervising segmentation using pseudo-labels generated by motion and improving motion estimation using intermediately reconstructed images. Experiment results show that the multi-task model outperformed single-task networks.
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