Application and prospect for generative adversarial networks in cross-modality reconstruction of medical images.
10.11817/j.issn.1672-7347.2022.220189
- Author:
Jie SUN
1
;
Shichen JIN
1
;
Rong SHI
1
;
Chuantao ZUO
2
;
Jiehui JIANG
3
Author Information
1. Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai 200444.
2. PET Center, Huashan Hospital Affiliated to Fudan University, Shanghai 200040, China. zuochuantao@ fudan.edu.cn.
3. Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai 200444. jiangjiehui@shu edu.cn.
- Publication Type:Journal Article
- Keywords:
CT prediction;
MRI prediction;
cross-modality reconstruction;
generative adversarial networks;
positron emission computed tomography prediction
- MeSH:
Brain/diagnostic imaging*;
Image Processing, Computer-Assisted/methods*;
Magnetic Resonance Imaging/methods*;
Positron-Emission Tomography;
Tomography, X-Ray Computed
- From:
Journal of Central South University(Medical Sciences)
2022;47(8):1001-1008
- CountryChina
- Language:English
-
Abstract:
Cross-modality reconstruction of medical images refers to predicting the image from one modality to another so as to achieve more accurate personalized medicine. Generative adversarial networks is the most commonly used deep learning technique in cross-modality reconstruction. It can generate realistic images by learning features from implicit distributions that follow the distributions of real data and then reconstruct the image of another modality rapidly. With the sharp increase in clinical demand for multi-modality medical image, this technology has been widely used in the task of cross modal reconstruction between different medical image modalities, such as magnetic resonance imaging, computed tomography and positron emission computed tomography. It can achieve accurate and efficient cross-modality image reconstruction in different parts of the body, such as the brain, heart, etc. In addition, although GAN has achieved some success in cross-modality reconstruction, its stability, generalization ability, and accuracy still need further research and improvement.
