Keywords: Image Reconstruction, Machine Learning/Artificial IntelligenceMRI has been revolutionized by compressed sensing algorithms, which offer guaranteed uniqueness, convergence, and stability. In the recent years, model-based deep learning methods have been emerging as more powerful alternatives for image recovery. The main focus of this paper is to introduce a model based algorithm with similar theoretical guarantees as CS methods. The proposed deep equilibrium formulation is significantly more memory-efficient than unrolled methods, which allows us to apply it to 3D or 2D+time problems that current unrolled algorithms cannot handle. Our results also show that the approach is more robust to input perturbations than current unrolled methods.
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