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Sun J, Cao N, Bi H, Gao L, Xie K, Lin T, Sui J, Ni X. DiffRecon: Diffusion-based CT reconstruction with cross-modal deformable fusion for DR-guided non-coplanar radiotherapy. Comput Biol Med 2024; 179:108868. [PMID: 39043106 DOI: 10.1016/j.compbiomed.2024.108868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 06/03/2024] [Accepted: 07/07/2024] [Indexed: 07/25/2024]
Abstract
In non-coplanar radiotherapy, DR is commonly used for image guiding which needs to fuse intraoperative DR with preoperative CT. But this fusion task performs poorly, suffering from unaligned and dimensional differences between DR and CT. CT reconstruction estimated from DR could facilitate this challenge. Thus, We propose a unified generation and registration framework, named DiffRecon, for intraoperative CT reconstruction based on DR using the diffusion model. Specifically, we use the generation model for synthesizing intraoperative CTs to eliminate dimensional differences and the registration model for aligning synthetic CTs to improve reconstruction. To ensure clinical usability, CT is not only estimated from DR but the preoperative CT is also introduced as prior. We design a dual-encoder to learn prior knowledge and spatial deformation among pre- and intra-operative CT pairs and DR parallelly for 2D/3D feature deformable conversion. To calibrate the cross-modal fusion, we insert cross-attention modules to enhance the 2D/3D feature interaction between dual encoders. DiffRecon has been evaluated by both image quality metrics and dosimetric indicators. The high image synthesis metrics are with RMSE of 0.02±0.01, PSNR of 44.92±3.26, and SSIM of 0.994±0.003. The mean gamma passing rates between rCT and sCT for 1%/1 mm, 2%/2 mm and 3%/3 mm acceptance criteria are 95.2%, 99.4% and 99.9% respectively. The proposed DiffRecon can reconstruct CT accurately from a single DR projection with excellent image generation quality and dosimetric accuracy. These demonstrate that the method can be applied in non-coplanar adaptive radiotherapy workflows.
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Affiliation(s)
- Jiawei Sun
- Changzhou No.2 People's Hospital, the Affiliated Hospital of Nanjing Medical University, Changzhou 213003, China; Jiangsu Province Engineering Research Center of Medical Physics, Changzhou 213003, China; Center of Medical Physics, Nanjing Medical University, Changzhou 213003, China
| | - Nannan Cao
- Changzhou No.2 People's Hospital, the Affiliated Hospital of Nanjing Medical University, Changzhou 213003, China; Jiangsu Province Engineering Research Center of Medical Physics, Changzhou 213003, China; Center of Medical Physics, Nanjing Medical University, Changzhou 213003, China
| | - Hui Bi
- Changzhou No.2 People's Hospital, the Affiliated Hospital of Nanjing Medical University, Changzhou 213003, China; School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213164, China; Key Laboratory of Computer Network and Information Integration, Southeast University, Nanjing 211096, China
| | - Liugang Gao
- Changzhou No.2 People's Hospital, the Affiliated Hospital of Nanjing Medical University, Changzhou 213003, China; Jiangsu Province Engineering Research Center of Medical Physics, Changzhou 213003, China; Center of Medical Physics, Nanjing Medical University, Changzhou 213003, China
| | - Kai Xie
- Changzhou No.2 People's Hospital, the Affiliated Hospital of Nanjing Medical University, Changzhou 213003, China; Jiangsu Province Engineering Research Center of Medical Physics, Changzhou 213003, China; Center of Medical Physics, Nanjing Medical University, Changzhou 213003, China
| | - Tao Lin
- Changzhou No.2 People's Hospital, the Affiliated Hospital of Nanjing Medical University, Changzhou 213003, China; Jiangsu Province Engineering Research Center of Medical Physics, Changzhou 213003, China; Center of Medical Physics, Nanjing Medical University, Changzhou 213003, China
| | - Jianfeng Sui
- Changzhou No.2 People's Hospital, the Affiliated Hospital of Nanjing Medical University, Changzhou 213003, China; Jiangsu Province Engineering Research Center of Medical Physics, Changzhou 213003, China; Center of Medical Physics, Nanjing Medical University, Changzhou 213003, China
| | - Xinye Ni
- Changzhou No.2 People's Hospital, the Affiliated Hospital of Nanjing Medical University, Changzhou 213003, China; Jiangsu Province Engineering Research Center of Medical Physics, Changzhou 213003, China; Center of Medical Physics, Nanjing Medical University, Changzhou 213003, China.
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Wang Z, Cao N, Sun J, Zhang H, Zhang S, Ding J, Xie K, Gao L, Ni X. Uncertainty estimation- and attention-based semi-supervised models for automatically delineate clinical target volume in CBCT images of breast cancer. Radiat Oncol 2024; 19:66. [PMID: 38811994 DOI: 10.1186/s13014-024-02455-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 05/14/2024] [Indexed: 05/31/2024] Open
Abstract
OBJECTIVES Accurate segmentation of the clinical target volume (CTV) of CBCT images can observe the changes of CTV during patients' radiotherapy, and lay a foundation for the subsequent implementation of adaptive radiotherapy (ART). However, segmentation is challenging due to the poor quality of CBCT images and difficulty in obtaining target volumes. An uncertainty estimation- and attention-based semi-supervised model called residual convolutional block attention-uncertainty aware mean teacher (RCBA-UAMT) was proposed to delineate the CTV in cone-beam computed tomography (CBCT) images of breast cancer automatically. METHODS A total of 60 patients who undergone radiotherapy after breast-conserving surgery were enrolled in this study, which involved 60 planning CTs and 380 CBCTs. RCBA-UAMT was proposed by integrating residual and attention modules in the backbone network 3D UNet. The attention module can adjust channel and spatial weights of the extracted image features. The proposed design can train the model and segment CBCT images with a small amount of labeled data (5%, 10%, and 20%) and a large amount of unlabeled data. Four types of evaluation metrics, namely, dice similarity coefficient (DSC), Jaccard, average surface distance (ASD), and 95% Hausdorff distance (95HD), are used to assess the model segmentation performance quantitatively. RESULTS The proposed method achieved average DSC, Jaccard, 95HD, and ASD of 82%, 70%, 8.93, and 1.49 mm for CTV delineation on CBCT images of breast cancer, respectively. Compared with the three classical methods of mean teacher, uncertainty-aware mean-teacher and uncertainty rectified pyramid consistency, DSC and Jaccard increased by 7.89-9.33% and 14.75-16.67%, respectively, while 95HD and ASD decreased by 33.16-67.81% and 36.05-75.57%, respectively. The comparative experiment results of the labeled data with different proportions (5%, 10% and 20%) showed significant differences in the DSC, Jaccard, and 95HD evaluation indexes in the labeled data with 5% versus 10% and 5% versus 20%. Moreover, no significant differences were observed in the labeled data with 10% versus 20% among all evaluation indexes. Therefore, we can use only 10% labeled data to achieve the experimental objective. CONCLUSIONS Using the proposed RCBA-UAMT, the CTV of breast cancer CBCT images can be delineated reliably with a small amount of labeled data. These delineated images can be used to observe the changes in CTV and lay the foundation for the follow-up implementation of ART.
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Affiliation(s)
- Ziyi Wang
- Department of Radiotherapy Oncology, Changzhou No. 2 People's Hospital, Nanjing Medical University, Gehu Road 68#, Wujin District, Changzhou, 213003, Jiangsu, China
- Jiangsu Province Engineering Research Center of Medical Physics, Changzhou, 213003, China
- Medical Physics Research Center, Nanjing Medical University, Changzhou, 213003, China
- Key Laboratory of Medical Physics in Changzhou, Changzhou, 213003, China
| | - Nannan Cao
- Department of Radiotherapy Oncology, Changzhou No. 2 People's Hospital, Nanjing Medical University, Gehu Road 68#, Wujin District, Changzhou, 213003, Jiangsu, China
- Jiangsu Province Engineering Research Center of Medical Physics, Changzhou, 213003, China
- Medical Physics Research Center, Nanjing Medical University, Changzhou, 213003, China
- Key Laboratory of Medical Physics in Changzhou, Changzhou, 213003, China
| | - Jiawei Sun
- Department of Radiotherapy Oncology, Changzhou No. 2 People's Hospital, Nanjing Medical University, Gehu Road 68#, Wujin District, Changzhou, 213003, Jiangsu, China
- Jiangsu Province Engineering Research Center of Medical Physics, Changzhou, 213003, China
- Medical Physics Research Center, Nanjing Medical University, Changzhou, 213003, China
- Key Laboratory of Medical Physics in Changzhou, Changzhou, 213003, China
| | - Heng Zhang
- Department of Radiotherapy Oncology, Changzhou No. 2 People's Hospital, Nanjing Medical University, Gehu Road 68#, Wujin District, Changzhou, 213003, Jiangsu, China
- Jiangsu Province Engineering Research Center of Medical Physics, Changzhou, 213003, China
- Medical Physics Research Center, Nanjing Medical University, Changzhou, 213003, China
- Key Laboratory of Medical Physics in Changzhou, Changzhou, 213003, China
| | - Sai Zhang
- Department of Radiotherapy Oncology, Changzhou No. 2 People's Hospital, Nanjing Medical University, Gehu Road 68#, Wujin District, Changzhou, 213003, Jiangsu, China
- Jiangsu Province Engineering Research Center of Medical Physics, Changzhou, 213003, China
- Medical Physics Research Center, Nanjing Medical University, Changzhou, 213003, China
- Key Laboratory of Medical Physics in Changzhou, Changzhou, 213003, China
| | - Jiangyi Ding
- Department of Radiotherapy Oncology, Changzhou No. 2 People's Hospital, Nanjing Medical University, Gehu Road 68#, Wujin District, Changzhou, 213003, Jiangsu, China
- Jiangsu Province Engineering Research Center of Medical Physics, Changzhou, 213003, China
- Medical Physics Research Center, Nanjing Medical University, Changzhou, 213003, China
- Key Laboratory of Medical Physics in Changzhou, Changzhou, 213003, China
| | - Kai Xie
- Department of Radiotherapy Oncology, Changzhou No. 2 People's Hospital, Nanjing Medical University, Gehu Road 68#, Wujin District, Changzhou, 213003, Jiangsu, China
- Jiangsu Province Engineering Research Center of Medical Physics, Changzhou, 213003, China
- Medical Physics Research Center, Nanjing Medical University, Changzhou, 213003, China
- Key Laboratory of Medical Physics in Changzhou, Changzhou, 213003, China
| | - Liugang Gao
- Department of Radiotherapy Oncology, Changzhou No. 2 People's Hospital, Nanjing Medical University, Gehu Road 68#, Wujin District, Changzhou, 213003, Jiangsu, China
- Jiangsu Province Engineering Research Center of Medical Physics, Changzhou, 213003, China
- Medical Physics Research Center, Nanjing Medical University, Changzhou, 213003, China
- Key Laboratory of Medical Physics in Changzhou, Changzhou, 213003, China
| | - Xinye Ni
- Department of Radiotherapy Oncology, Changzhou No. 2 People's Hospital, Nanjing Medical University, Gehu Road 68#, Wujin District, Changzhou, 213003, Jiangsu, China.
- Jiangsu Province Engineering Research Center of Medical Physics, Changzhou, 213003, China.
- Medical Physics Research Center, Nanjing Medical University, Changzhou, 213003, China.
- Key Laboratory of Medical Physics in Changzhou, Changzhou, 213003, China.
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Sun H, Yang Z, Zhu J, Li J, Gong J, Chen L, Wang Z, Yin Y, Ren G, Cai J, Zhao L. Pseudo-medical image-guided technology based on 'CBCT-only' mode in esophageal cancer radiotherapy. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 245:108007. [PMID: 38241802 DOI: 10.1016/j.cmpb.2024.108007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 11/03/2023] [Accepted: 01/03/2024] [Indexed: 01/21/2024]
Abstract
Purpose To minimize the various errors introduced by image-guided radiotherapy (IGRT) in the application of esophageal cancer treatment, this study proposes a novel technique based on the 'CBCT-only' mode of pseudo-medical image guidance. Methods The framework of this technology consists of two pseudo-medical image synthesis models in the CBCT→CT and the CT→PET direction. The former utilizes a dual-domain parallel deep learning model called AWM-PNet, which incorporates attention waning mechanisms. This model effectively suppresses artifacts in CBCT images in both the sinogram and spatial domains while efficiently capturing important image features and contextual information. The latter leverages tumor location and shape information provided by clinical experts. It introduces a PRAM-GAN model based on a prior region aware mechanism to establish a non-linear mapping relationship between CT and PET image domains. As a result, it enables the generation of pseudo-PET images that meet the clinical requirements for radiotherapy. Results The NRMSE and multi-scale SSIM (MS-SSIM) were utilized to evaluate the test set, and the results were presented as median values with lower quartile and upper quartile ranges. For the AWM-PNet model, the NRMSE and MS-SSIM values were 0.0218 (0.0143, 0.0255) and 0.9325 (0.9141, 0.9410), respectively. The PRAM-GAN model produced NRMSE and MS-SSIM values of 0.0404 (0.0356, 0.0476) and 0.9154 (0.8971, 0.9294), respectively. Statistical analysis revealed significant differences (p < 0.05) between these models and others. The numerical results of dose metrics, including D98 %, Dmean, and D2 %, validated the accuracy of HU values in the pseudo-CT images synthesized by the AWM-PNet. Furthermore, the Dice coefficient results confirmed statistically significant differences (p < 0.05) in GTV delineation between the pseudo-PET images synthesized using the PRAM-GAN model and other compared methods. Conclusion The AWM-PNet and PRAM-GAN models have the capability to generate accurate pseudo-CT and pseudo-PET images, respectively. The pseudo-image-guided technique based on the 'CBCT-only' mode shows promising prospects for application in esophageal cancer radiotherapy.
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Affiliation(s)
- Hongfei Sun
- Department of Radiation Oncology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Zhi Yang
- Department of Radiation Oncology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Jiarui Zhu
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Jie Li
- Department of Radiation Oncology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Jie Gong
- Department of Radiation Oncology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Liting Chen
- Department of Radiation Oncology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Zhongfei Wang
- Department of Radiation Oncology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Yutian Yin
- Department of Radiation Oncology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Ge Ren
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China.
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China.
| | - Lina Zhao
- Department of Radiation Oncology, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
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