Development and application of three-dimensional point cloud radiotherapy real-time monitoring system based on depth camera
Li Chunying1, Lu Zhengda2, Zhang Sai1, Sun Jiawei1, Gao Liugang1, Xie Kai1, Lin Tao1, Sui Jianfeng1, Ni Xinye1
1Department of Radiotherapy, Changzhou No.2 People's Hospital, the Affiliated Hospital of Nanjing Medical University, Changzhou 213003, China; 2School of Biomedical Engineering and Information, Nanjing Medical University, Nanjing 210000, China
Abstract：Objective To develop the real-time radiotherapy monitoring system of three-dimensional (3D) point cloud by using depth camera and verify its feasibility. Methods Taking the depth camera coordinate system as the world coordinate system, the conversion relationship between the simulation CT coordinate system and the world coordinate system was obtained from the calibration module. The patient's simulation CT point cloud was transformed into the world coordinate system through the above relationship, and registered with the patient's surface point cloud obtained in real-time manner by the depth camera to calculate the six-dimensional (6D) error, and complete the positioning verification and fractional internal position error monitoring in radiotherapy. Mean and standard deviation of 6D calculation error, Hausdorff distance of point cloud after registration and the running time of each part of the program were calculated to verify the feasibility of the system. Fifteen real patients were selected to calculate the 6D error between the system and cone beam CT (CBCT). Results In the phantom experiment, the errors of the system in the x, y and z axes were (1.292±0.880)mm, (1.963±1.115)mm, (1.496±1.045)mm, respectively, and the errors in the rotation, pitch and roll directions were 0.201°±0.181°, 0.286°±0.326°, 0.181°±0.192°, respectively. For real patients, the translational error of the system was within 2.6 mm, the rotational error was approximately 1°, and the program run at 1-2 frames/s. The precision and speed met the radiotherapy requirement. Conclusion The 3D point cloud radiotherapy real-time monitoring system based on depth camera can automatically complete the positioning verification before radiotherapy, real-time monitoring of body position during radiotherapy, and provide error visual feedback, which has potential clinical application value.
Li Chunying,Lu Zhengda,Zhang Sai et al. Development and application of three-dimensional point cloud radiotherapy real-time monitoring system based on depth camera[J]. Chinese Journal of Radiation Oncology, 2023, 32(2): 145-151.
[1] 高丽娟, 黄嘉敏, 黄峻, 等. Orfit架和真空袋对宫颈癌放疗摆位误差比较[J].中华放射肿瘤学杂志,2017,26(9):1080-1083. DOI: 10.3760/cma.j.issn.1004-4221.2017.09.020. Gao LJ, Huang JM, Huang J, et al.Setup error of Orfit versus vacuum bag in radiotherapy for cervical cancer[J].Chin J Radiat Oncol,2017,26(9):1080-1083. DOI: 10.3760/cma.j.issn.1004-4221.2017.09.020. [2] Alderliesten T, Heemsbergen WD, Betgen A, et al.Breast-shape changes during radiation therapy after breast-conserving surgery[J]. Phys Imaging Radiat Oncol, 2018,6:71-76. DOI: 10.1016/j.phro.2018.05.006. [3] 张彦新, 符贵山, 徐英杰, 等. 脑转移瘤立体定向放疗分次间和分次内摆位误差及残余误差分析[J].中华放射肿瘤学杂志,2019,28(6):448-451. DOI: 10.3760/cma.j.issn.1004-4221.2019.06.012. Zhang YX, Fu GS, Xu YJ, et al.Analysis of the inter-and intra-fraction setup errors and residual errors during stereotactic radiotherapy for brain metastasis[J].Chin J Radiat Oncol,2019,28(6):448-451. DOI: 10.3760/cma.j.issn.1004-4221.2019.06.012. [4] 彭海燕, 罗焕丽, 毛开金, 等. 表面光学系统Catalyst在宫颈癌放疗中的临床应用[J].中华放射肿瘤学杂志,2019,28(3):198-202. DOI: 10.3760/cma.j.issn.1004-4221.2019.03.008. Peng HY, Luo HL, Mao KJ, et al.Clinical application of catalyst system in cervical cancer radiotherapy[J].Chin J Radiat Oncol,2019,28(3):198-202. DOI: 10.3760/cma.j.issn.1004-4221.2019.03.008. [5] 徐晓捷, 雷亚楠, 张瑞英, 等. 胸段食管癌动态调强IGRT分次摆位误差对剂量学的影响[J].食管疾病,2021,3(2):85-88. DOI: 10.15926/j.cnki.issn2096-7381.2021.02.002. Xu XJ, Lei YN, Zhang RY, et al.Effect of fractional setup error of image-guided radiotherapy dynamic intensity modulation on dosimetry in thoracic esophageal carcinoma[J].J Esophageal Dis,2021,3(2):85-88. DOI: 10.15926/j.cnki.issn2096-7381.2021.02.002. [6] Placht S, Stancanello J, Schaller C, et al.Fast time-of-flight camera based surface registration for radiotherapy patient positioning[J]. Med Phys, 2012,39(1):4-17. DOI: 10.1118/1.3664006. [7] Kusters M, Louwe R, Biemans-van Kastel L, et al. Camera-based independent couch height verification in radiation oncology[J]. J Appl Clin Med Phys, 2015,16(5):442-446. DOI: 10.1120/jacmp.v16i5.5500. [8] Sarmadi H, Muñoz-Salinas R, Álvaro Berbís M, et al.3D reconstruction and alignment by consumer RGB-D sensors and fiducial planar markers for patient positioning in radiation therapy[J]. Comput Methods Programs Biomed, 2019,180:105004. DOI: 10.1016/j.cmpb.2019.105004. [9] Bellekens B, Spruyt V, Berkvens R, et al. A survey of rigid 3D pointcloud registration algorithms[R/OL].(2014-08- 28)[2021-12-01].https://www.researchgate.net/publication/265186421_A_Survey_of_Rigid_3D_Pointcloud_Registration_Algorithms. [10] 林桂潮, 唐昀超, 邹湘军, 等. 融合高斯混合模型和点到面距离的点云配准[J].计算机辅助设计与图形学学报,2018,30(4):642-650. DOI: 10.3724/SP.J.1089.2018.16472. Lin GC, Tang YC, Zou XJ, et al.Point cloud registration algorithm combined gaussian mixture model and point-to-plane metric[J].J Comput-Aided Des Comput Graphics,2018,30(4):642-650. DOI: 10.3724/SP.J.1089. 2018.16472. [11] 宋成航, 李晋儒, 刘冠杰. 利用特征点采样一致性改进ICP算法点云配准方法[J].北京测绘,2021,35(3):317-322. DOI: 10.19580/j.cnki.1007-3000.2021.03.007. Song CH, Li JR, Liu GJ.Point cloud registration method using feature point sampling consistency initial alignment and improved ICP algorithm[J].Beijing Surv Mapp,2021,35(3):317-322. DOI: 10.19580/j.cnki. 1007-3000.2021.03.007. [12] 刘梦迪, 潘晓, 高珊珊, 等. 结合DBSCAN聚类的室内场景分割[J].计算机辅助设计与图形学学报,2019,31(7):1183-1193. DOI: 10.3724/SP.J.1089.2019.17519. Liu MD, Pan X, Gao SS, et al.Segmentation for indoor scenes based on DBSCAN clustering[J]. J Comput-Aided Des Comput Graphics,2019,31(7):1183-1193. DOI: 10.3724/SP.J.1089.2019.17519. [13] He DC, Zhu ZJ, Zhang XY, et al.Positioning error analysis of the fraxion localization system in the intracranial stereotactic radiotherapy of tumors[J]. Clin Transl Oncol, 2021,23(1):43-47. DOI: 10.1007/s12094-020-02382-y. [14] 张超. 调强放疗摆位误差的研究进展[J].重庆医学,2021,50(4):684-688. DOI: 10.3969/j.issn.1671-8348.2021. 04.032. Zhang C.Advances in setup errors of intensity modulated radiation therapy[J].Chongqing Med,2021,50(4):684-688. DOI: 10.3969/j.issn.1671-8348.2021.04.032. [15] Fayad H, Pan T, Clement JF, et al.Technical note: correlation of respiratory motion between external patient surface and internal anatomical landmarks[J]. Med Phys, 2011,38(6):3157-3164. DOI: 10.1118/1.358 9131. [16] Hou P, Sun RC, Yu SM, et al.Correlation between thoracic-abdominal surface and tumor motion based on 3D point cloud: a preliminary study[C] // IEEE. 2018 IEEE International Conference on Information and Automation (ICIA),[S.l.]: IEEE,2019. [17] Hoisak JDP, Paxton AB, Waghorm BJ, et al.Surface Guided Radiation Therapy[M]. New York: CRC Press,2020. [18] Walter F, Freislederer P, Belka C, et al.Evaluation of daily patient positioning for radiotherapy with a commercial 3D surface-imaging system (Catalyst™)[J]. Radiat Oncol, 2016,11(1):154. DOI: 10.1186/s13014-016-0728-1. [19] Kügele M, Mannerberg A, Nørring Bekke S, et al.Surface guided radiotherapy (SGRT) improves breast cancer patient setup accuracy[J]. J Appl Clin Med Phys, 2019,20(9):61-68. DOI: 10.1002/acm2.12700. [20] Bry V, Licon AL, McCulloch J, et al. Quantifying false positional corrections due to facial motion using SGRT with open-face masks[J]. J Appl Clin Med Phys, 2021,22(4):172-183. DOI: 10.1002/acm2.13170. [21] Alderliesten T, Sonke JJ, Betgen A, et al.Accuracy evaluation of a 3-dimensional surface imaging system for guidance in deep-inspiration breath-hold radiation therapy[J]. Int J Radiat Oncol Biol Phys, 2013,85(2):536-542. DOI: 10.1016/j.ijrobp.2012.04.004. [22] Covington EL, Stanley DN, Fiveash JB, et al.Surface guided imaging during stereotactic radiosurgery with automated delivery[J]. J Appl Clin Med Phys, 2020,21(12):90-95. DOI: 10.1002/acm2.13066.
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