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arXiv 2406.026262024-06-03eess.IVcs.CVmath.OC

基于优化的深度学习MRI重建算法简述

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

neuro42（神经42）

Wanyu Bian

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AI总结：

本文综述了专为MRI重建设计的最新基于优化的深度学习算法，填补了该领域缺乏全面综述的空白，旨在促进MRI社区的进一步创新与应用。

摘要：

磁共振成像（MRI）以其卓越的软组织对比度和高空间分辨率而闻名，是医学成像的关键工具。深度学习算法的整合为优化MRI重建过程提供了巨大潜力。尽管该领域研究日益增多，但针对MRI重建的基于优化的深度学习模型的全面综述尚未开展。本文填补了这一空白，深入探讨了专为MRI重建设计的最新基于优化的深度学习算法，旨在为研究人员提供对这些进展的详细理解，促进MRI领域的进一步创新与应用。

英文摘要：

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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