A Brief Overview of Optimization-Based Algorithms for MRI Reconstruction Using Deep Learning

Wanyu Bian
6/3/2024
eess.IVcs.CVmath.OC

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Abstract

Magnetic resonance imaging (MRI) is renowned for its exceptional soft tissue contrast and high spatial resolution, making it a pivotal tool in medical imaging. The integration of deep learning algorithms offers significant potential for optimizing MRI reconstruction processes. Despite the growing body of research in this area, a comprehensive survey of optimization-based deep learning models tailored for MRI reconstruction has yet to be conducted. This review addresses this gap by presenting a thorough examination of the latest optimization-based algorithms in deep learning specifically designed for MRI reconstruction. The goal of this paper is to provide researchers with a detailed understanding of these advancements, facilitating further innovation and application within the MRI community.

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Cite this paper

@article{bian2024brief,
  title  = {A Brief Overview of Optimization-Based Algorithms for MRI Reconstruction Using Deep Learning},
  author = {Wanyu Bian},
  year   = {2024},
  eprint = {2406.02626},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2406.02626v1}
}

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