Methods for enhancing image quality of soft tissue regions in synthetic CT based on cone-beam CT.
10.7507/1001-5515.202407078
- Author:
Ziwei FU
1
;
Yechen ZHU
1
;
Zijian ZHANG
2
;
Xin GAO
3
Author Information
1. Division of Life Sciences and Medicine, School of Biomedical Engineering (Suzhou), University of Science and Technology of China, Hefei 230026, P. R. China.
2. Xiangya Hospital, Central South University, Changsha 410008, P. R. China.
3. Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu 215163, P. R. China.
- Publication Type:Journal Article
- Keywords:
Adaptive radiation therapy;
Cone-beam CT;
Multi-task learning;
Synthetic CT
- MeSH:
Cone-Beam Computed Tomography/methods*;
Humans;
Image Processing, Computer-Assisted/methods*;
Artifacts;
Algorithms;
Bone and Bones/diagnostic imaging*;
Neural Networks, Computer
- From:
Journal of Biomedical Engineering
2025;42(1):113-122
- CountryChina
- Language:Chinese
-
Abstract:
Synthetic CT (sCT) generated from CBCT has proven effective in artifact reduction and CT number correction, facilitating precise radiation dose calculation. However, the quality of different regions in sCT images is severely imbalanced, with soft tissue region exhibiting notably inferior quality compared to others. To address this imbalance, we proposed a Multi-Task Attention Network (MuTA-Net) based on VGG-16, specifically focusing the enhancement of image quality in soft tissue region of sCT. First, we introduced a multi-task learning strategy that divides the sCT generation task into three sub-tasks: global image generation, soft tissue region generation and bone region segmentation. This approach ensured the quality of overall sCT image while enhancing the network's focus on feature extraction and generation for soft tissues region. The result of bone region segmentation task guided the fusion of sub-tasks results. Then, we designed an attention module to further optimize feature extraction capabilities of the network. Finally, by employing a results fusion module, the results of three sub-tasks were integrated, generating a high-quality sCT image. Experimental results on head and neck CBCT demonstrated that the sCT images generated by the proposed MuTA-Net exhibited a 12.52% reduction in mean absolute error in soft tissue region, compared to the best performance among the three comparative methods, including ResNet, U-Net, and U-Net++. It can be seen that MuTA-Net is suitable for high-quality sCT image generation and has potential application value in the field of CBCT guided adaptive radiation therapy.
