Keywords: AI/ML Image Reconstruction, Cardiovascular, Self-Supervised learning, Feature learning
Motivation: Most existing deep learning-based MR image reconstruction methods are supervised learning, relying on fully-sampled images, which is challenging to acquire in practice.
Goal(s): We aim to leverage undersampled data in a self-supervised reconstruction framework to enhance expressibility and model performance.
Approach: We use information maximization methods to learn sampling-invariant features from undersampled images and incorporate them in a self-supervised reconstruction network.
Results: The proposed method can learn sampling-invariant features from undersampled data, which enhance the reconstruction performance, enabling self-supervised MR image reconstruction for up to 16× undersampling.
Impact: The proposed self-supervised feature learning strategy can extract sampling-invariant features from undersampled images, effectively assisting the reconstruction of undersampled cardiac cine MR imaging without requiring fully-sampled images. This feature learning strategy may also be advantageous for other downstream tasks.
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