Review
Version 1
This version is not peer-reviewed
A Review of Optimization-Based Deep Learning Models for MRI Reconstruction
Version 1
: Received: 25 July 2024 / Approved: 26 July 2024 / Online: 26 July 2024 (05:30:23 CEST)
How to cite: Bian, W.; Tamilselvam, Y. K. A Review of Optimization-Based Deep Learning Models for MRI Reconstruction. Preprints 2024, 2024072135. https://doi.org/10.20944/preprints202407.2135.v1 Bian, W.; Tamilselvam, Y. K. A Review of Optimization-Based Deep Learning Models for MRI Reconstruction. Preprints 2024, 2024072135. https://doi.org/10.20944/preprints202407.2135.v1
Abstract
Magnetic resonance imaging (MRI) is crucial for its superior soft tissue contrast and high spatial resolution. Integrating deep learning algorithms into MRI reconstruction enhances image quality and efficiency, but a comprehensive review of optimization-based deep learning models for MRI reconstruction has been missing. This study fills that gap by examining the latest optimization-based algorithms in deep learning for MRI reconstruction, including gradient descent algorithms, proximal gradient descent algorithms, ADMM, PDHG, and diffusion models combined with gradient descent. Learnable optimization algorithms (LOA) are highlighted for their ability to map optimization processes to structured neural networks, improving model interpretability and performance. The study demonstrates significant advancements in MRI reconstruction through deep learning, with successful clinical applications. These findings provide valuable insights and resources for researchers aiming to advance medical imaging using innovative deep learning techniques.
Keywords
optimization algorithms; MRI reconstruction; deep learning
Subject
Computer Science and Mathematics, Signal Processing
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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