Application of deep learning in super-resolution reconstruction of magnetic resonance images
10.3969/j.issn.1005-202X.2024.10.008
- VernacularTitle:深度学习在磁共振图像超分辨率重建中的应用
- Author:
Huichang YU
1
;
Shiyuan LIU
Author Information
1. 上海理工大学健康科学与工程学院,上海 200093
- Keywords:
magnetic resonance imaging;
super-resolution reconstruction;
deep learning;
neural network;
review
- From:
Chinese Journal of Medical Physics
2024;41(10):1243-1248
- CountryChina
- Language:Chinese
-
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.
