Advances in medical magnetic resonance image synthesis based on deep learning
10.3969/j.issn.1005-202X.2025.10.003
- VernacularTitle:基于深度学习的医学MR图像合成研究进展
- Author:
Shi CAO
1
;
Gao GONG
;
Junyi GAO
;
Yongkun YANG
;
Chaomin CHEN
;
Guoguang LIU
;
Guangzhi SUN
Author Information
1. 南京医科大学附属泰州人民医院放疗中心,江苏 泰州 225300
- Publication Type:Journal Article
- Keywords:
medical image synthesis;
generative deep learning model;
magnetic resonance imaging;
computed tomography scanning;
review
- From:
Chinese Journal of Medical Physics
2025;42(10):1273-1279
- CountryChina
- Language:Chinese
-
Abstract:
The superiority of magnetic resonance(MR)images in soft tissue imaging makes them indispensable for medical diagnosis and radiotherapy,but factors such as acquisition cost and contraindications limit their widespread application.In contrast,computed tomography(CT)scanning has the advantages of fast imaging speed and low cost.Herein,this review summarizes the research progress of generative deep learning models in the field of medical CT to MR image synthesis,and especially analyzes the technical characteristics,performance advantages,and challenges of various MR image synthesis methods from clinical scenarios such as spinal lesions,acute ischemic stroke,and tumor segmentation.Furthermore,the application value and future research prospects of medical image synthesis are discussed.
