Deep-learning-based optimization of k-space undersampling in self-supervised MRI reconstruction

Motivation: Self-supervised deep learning has shown good performance in reconstructing undersampled k-space. While recent developments focus on improving reconstruction performance for a given undersampling pattern, there is limited research aiming to learn and optimize k-space sampling strategies to offer a performance gain in self-supervised reconstruction. Goal(s): To design a deep learning framework to optimize the sampling pattern in self-supervised MRI reconstruction. Approach: An Auto Mask Module was optimized simultaneously with the self-supervised reconstruction module in an end-to-end framework. Results: The proposed method can achieve better reconstruction results than self-supervised methods based on fixed masks. Impact: The proposed method can produce better self-supervised reconstruction results by optimizing the k-space undersampling pattern.