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Model-based deep learning methods that combine imaging physics with learnedregularization priors have been emerging as powerful tools for parallel MRIacceleration. The main focus of this paper is to determine the utility of themonotone operator learning (MOL) framework in the parallel MRI setting. The MOLalgorithm alternates between a gradient descent step using a monotoneconvolutional neural network (CNN) and a conjugate gradient algorithm toencourage data consistency. The benefits of this approach include similarguarantees as compressive sensing algorithms including uniqueness, convergence,and stability, while being significantly more memory efficient than unrolledmethods. We validate the proposed scheme by comparing it with differentunrolled algorithms in the context of accelerated parallel MRI for static anddynamic settings.

Accelerated parallel MRI using memory efficient and robust monotone operator learning (MOL)