Non-coplanar CBCT image reconstruction using a generative adversarial network for non-coplanar radiotherapy

被引:0
|
作者
Wei, Ran [1 ]
Song, Zhiyue [1 ]
Pan, Ziqi [1 ]
Cao, Ying [1 ]
Song, Yongli [1 ]
Dai, Jianrong [1 ]
机构
[1] Chinese Acad Med Sci & Peking Union Med Coll, Canc Hosp, Natl Canc Ctr, Dept Radiat Oncol,Natl Clin Res Ctr Canc, Beijing 100021, Peoples R China
基金
中国国家自然科学基金;
关键词
cone-beam computed tomography; deep learning; image reconstruction; limited angle; non-coplanar radiotherapy; STEREOTACTIC RADIOSURGERY; LIMITED-ANGLE; QUALITY; OPTIMIZATION; REGISTRATION; VERIFICATION; UNCERTAINTY; ACQUISITION; SYSTEM;
D O I
10.1002/acm2.14487
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
PurposeTo develop a non-coplanar cone-beam computed tomography (CBCT) image reconstruction method using projections within a limited angle range for non-coplanar radiotherapy.MethodsA generative adversarial network (GAN) was utilized to reconstruct non-coplanar CBCT images. Data from 40 patients with brain tumors and two head phantoms were used in this study. In the training stage, the generator of the GAN used coplanar CBCT and non-coplanar projections as the input, and an encoder with a dual-branch structure was utilized to extract features from the coplanar CBCT and non-coplanar projections separately. Non-coplanar CBCT images were then reconstructed using a decoder by combining the extracted features. To improve the reconstruction accuracy of the image details, the generator was adversarially trained using a patch-based convolutional neural network as the discriminator. A newly designed joint loss was used to improve the global structure consistency rather than the conventional GAN loss. The proposed model was evaluated using data from eight patients and two phantoms at four couch angles (+/- 45 degrees, +/- 90 degrees) that are most commonly used for brain non-coplanar radiotherapy in our department. The reconstructed accuracy was evaluated by calculating the root mean square error (RMSE) and an overall registration error epsilon, computed by integrating the rigid transformation parameters.ResultsIn both patient data and phantom data studies, the qualitative and quantitative metrics results indicated that +/- 45 degrees couch angle models performed better than +/- 90 degrees couch angle models and had statistical differences. In the patient data study, the mean RMSE and epsilon values of couch angle at 45 degrees, -45 degrees, 90 degrees, and -90 degrees were 58.5 HU and 0.42 mm, 56.8 HU and 0.41 mm, 73.6 HU and 0.48 mm, and 65.3 HU and 0.46 mm, respectively. In the phantom data study, the mean RMSE and epsilon values of couch angle at 45 degrees, -45 degrees, 90 degrees, and -90 degrees were 91.2 HU and 0.46 mm, 95.0 HU and 0.45 mm, 114.6 HU and 0.58 mm, and 102.9 HU and 0.52 mm, respectively.ConclusionsThe results show that the reconstructed non-coplanar CBCT images can potentially enable intra-treatment three-dimensional position verification for non-coplanar radiotherapy.
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页数:16
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