Research Progress on Generative Adversarial Network in Cross-modal Medical Image Reconstruction
Single-modal medical images contain limited disease-specific information. To analyze and diagnose patients, clinicians often need to integrate multiple modal images. However, due to limited medical resources and treatment time, it may be difficult to obtain multi-modal images. Cross-modal image reco...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Medical Journal of Peking Union Medical College Hospital
2023-11-01
|
| Series: | Xiehe Yixue Zazhi |
| Subjects: | |
| Online Access: | https://xhyxzz.pumch.cn/en/article/doi/10.12290/xhyxzz.2023-0409 |
| _version_ | 1827596552026193920 |
|---|---|
| author | LI Zhuoyuan XU Guohao WANG Junchen WANG Saishuo WANG Chuantao ZHAI Jiliang |
| author_facet | LI Zhuoyuan XU Guohao WANG Junchen WANG Saishuo WANG Chuantao ZHAI Jiliang |
| author_sort | LI Zhuoyuan |
| collection | DOAJ |
| description | Single-modal medical images contain limited disease-specific information. To analyze and diagnose patients, clinicians often need to integrate multiple modal images. However, due to limited medical resources and treatment time, it may be difficult to obtain multi-modal images. Cross-modal image reconstruction can generate medical images for clinical needs, thus assisting clinicians in accurately diagnosing and treating diseases. Traditional cross-modal reconstruction techniques have been applied in some clinical scenarios, but the quality of the reconstructed images needs further improvement. Generative adversarial network (GAN) can recover high-quality and complete image data from low-quality or incomplete medical image data, maximally savings medical equipment resources and accelerating medical treatment speed. This article summarizes the applications of GAN technology in cross-modal image reconstruction across X-ray imaging, computed tomography imaging, magnetic resonance imaging, and positron emission tomography imaging, to provide reference for the development of more advanced cross-modal reconstruction techniques. |
| first_indexed | 2024-03-09T03:12:11Z |
| format | Article |
| id | doaj.art-b7e436fdfc824076b02b0662a4b37f1f |
| institution | Directory Open Access Journal |
| issn | 1674-9081 |
| language | zho |
| last_indexed | 2024-03-09T03:12:11Z |
| publishDate | 2023-11-01 |
| publisher | Editorial Office of Medical Journal of Peking Union Medical College Hospital |
| record_format | Article |
| series | Xiehe Yixue Zazhi |
| spelling | doaj.art-b7e436fdfc824076b02b0662a4b37f1f2023-12-04T02:32:26ZzhoEditorial Office of Medical Journal of Peking Union Medical College HospitalXiehe Yixue Zazhi1674-90812023-11-011461162116910.12290/xhyxzz.2023-0409Research Progress on Generative Adversarial Network in Cross-modal Medical Image ReconstructionLI Zhuoyuan0XU Guohao1WANG Junchen2WANG Saishuo3WANG Chuantao4ZHAI Jiliang5School of Electromechanical and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing 102616, ChinaSchool of Electromechanical and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing 102616, ChinaDepartment of Orthopaedics, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, ChinaSchool of Electromechanical and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing 102616, ChinaSchool of Electromechanical and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing 102616, ChinaDepartment of Orthopaedics, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, ChinaSingle-modal medical images contain limited disease-specific information. To analyze and diagnose patients, clinicians often need to integrate multiple modal images. However, due to limited medical resources and treatment time, it may be difficult to obtain multi-modal images. Cross-modal image reconstruction can generate medical images for clinical needs, thus assisting clinicians in accurately diagnosing and treating diseases. Traditional cross-modal reconstruction techniques have been applied in some clinical scenarios, but the quality of the reconstructed images needs further improvement. Generative adversarial network (GAN) can recover high-quality and complete image data from low-quality or incomplete medical image data, maximally savings medical equipment resources and accelerating medical treatment speed. This article summarizes the applications of GAN technology in cross-modal image reconstruction across X-ray imaging, computed tomography imaging, magnetic resonance imaging, and positron emission tomography imaging, to provide reference for the development of more advanced cross-modal reconstruction techniques.https://xhyxzz.pumch.cn/en/article/doi/10.12290/xhyxzz.2023-0409cross-modal reconstructionradiological imagesgenerative adversarial network |
| spellingShingle | LI Zhuoyuan XU Guohao WANG Junchen WANG Saishuo WANG Chuantao ZHAI Jiliang Research Progress on Generative Adversarial Network in Cross-modal Medical Image Reconstruction Xiehe Yixue Zazhi cross-modal reconstruction radiological images generative adversarial network |
| title | Research Progress on Generative Adversarial Network in Cross-modal Medical Image Reconstruction |
| title_full | Research Progress on Generative Adversarial Network in Cross-modal Medical Image Reconstruction |
| title_fullStr | Research Progress on Generative Adversarial Network in Cross-modal Medical Image Reconstruction |
| title_full_unstemmed | Research Progress on Generative Adversarial Network in Cross-modal Medical Image Reconstruction |
| title_short | Research Progress on Generative Adversarial Network in Cross-modal Medical Image Reconstruction |
| title_sort | research progress on generative adversarial network in cross modal medical image reconstruction |
| topic | cross-modal reconstruction radiological images generative adversarial network |
| url | https://xhyxzz.pumch.cn/en/article/doi/10.12290/xhyxzz.2023-0409 |
| work_keys_str_mv | AT lizhuoyuan researchprogressongenerativeadversarialnetworkincrossmodalmedicalimagereconstruction AT xuguohao researchprogressongenerativeadversarialnetworkincrossmodalmedicalimagereconstruction AT wangjunchen researchprogressongenerativeadversarialnetworkincrossmodalmedicalimagereconstruction AT wangsaishuo researchprogressongenerativeadversarialnetworkincrossmodalmedicalimagereconstruction AT wangchuantao researchprogressongenerativeadversarialnetworkincrossmodalmedicalimagereconstruction AT zhaijiliang researchprogressongenerativeadversarialnetworkincrossmodalmedicalimagereconstruction |