Accelerated parallel MRI using memory efficient and robust monotone
operator learning (MOL)
- URL: http://arxiv.org/abs/2304.01351v1
- Date: Mon, 3 Apr 2023 20:26:59 GMT
- Title: Accelerated parallel MRI using memory efficient and robust monotone
operator learning (MOL)
- Authors: Aniket Pramanik, Mathews Jacob
- Abstract summary: The main focus of this paper is to determine the utility of the monotone operator learning framework in the parallel MRI setting.
The benefits of this approach include similar guarantees as compressive sensing algorithms including uniqueness, convergence, and stability.
We validate the proposed scheme by comparing it with different unrolled algorithms in the context of accelerated parallel MRI for static and dynamic settings.
- Score: 24.975981795360845
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Model-based deep learning methods that combine imaging physics with learned
regularization priors have been emerging as powerful tools for parallel MRI
acceleration. The main focus of this paper is to determine the utility of the
monotone operator learning (MOL) framework in the parallel MRI setting. The MOL
algorithm alternates between a gradient descent step using a monotone
convolutional neural network (CNN) and a conjugate gradient algorithm to
encourage data consistency. The benefits of this approach include similar
guarantees as compressive sensing algorithms including uniqueness, convergence,
and stability, while being significantly more memory efficient than unrolled
methods. We validate the proposed scheme by comparing it with different
unrolled algorithms in the context of accelerated parallel MRI for static and
dynamic settings.
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