Preprint
Accelerated parallel MRI using memory efficient and robust monotone operator learning (MOL)
ArXiv.org
04/03/2023
DOI: 10.48550/arxiv.2304.01351
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.
Details
- Title: Subtitle
- Accelerated parallel MRI using memory efficient and robust monotone operator learning (MOL)
- Creators
- Aniket PramanikMathews Jacob
- Resource Type
- Preprint
- Publication Details
- ArXiv.org
- DOI
- 10.48550/arxiv.2304.01351
- ISSN
- 2331-8422
- Language
- English
- Date posted
- 04/03/2023
- Academic Unit
- Electrical and Computer Engineering; Radiology; Iowa Neuroscience Institute; Radiation Oncology
- Record Identifier
- 9984385052802771
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