mlsanityData to 7 Oct 2026

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

arXiv 2406.02626 ↗First posted 3 Jun 2024eess.IV cs.CV math.OCcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 32 of its 63 references that are in the map. First posted on arXiv 3 Jun 2024.

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32 of 63 references.

References · 32

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20 more
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Citing papers per year: 2024: 3.

3 papers.

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