Abstract

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Title:: Application of deep learning in super-resolution reconstruction of magnetic resonance images

Author(s):: YU Huichang1; LIU Shiyuan2; 1. School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China 2. Department of Diagnostic Radiology, Shanghai Changzheng Hospital, Naval Medical University, Shanghai 200003, China

Keywords:: Keywords: magnetic resonance imaging super-resolution reconstruction deep learning neural network review

Abstract:: Abstract: Magnetic resonance imaging (MRI) is a significant non-invasive diagnostic technique in medical imaging. Due to limitations in MRI hardware and scanning time, some MRI images have relatively low spatial resolution. The rise of deep learning technology offers a new approach to improve the resolution of MRI images. The study outlines the background of MRI super-resolution reconstruction, delves into the applications of various deep learning methods in MRI super-resolution reconstruction and offers a detailed analysis of these methods, evaluating their working principles, advantages, and performance efficiency in image reconstruction. Additionally, it also discusses the key challenges of deep learning technology in MRI super-resolution reconstruction, and provides prospects for future research trends.