Development and evaluation of a positioning system for radiotherapy patient based on structured light surface imaging_Journal of Biomedical Engineering

Authors：

WANG Yungang ^1,2 , ZHANG Gongsen ^3,4 , YAN Xianrui ² , YANG Guangjie ^1,5 , WANG Wei ² ,  ZHU Jian ² ,  WANG Linlin ^3,4

1. School of Basic Medicine, Qingdao University, Qingdao, Shandong 266071, P. R. China;
2. Department of Radiation Oncology Physics and Technology, Shandong Cancer Institute (Shandong Cancer Hospital), Jinan 250117, P. R. China;
3. Department of Radiation Oncology, Shandong Cancer Institute (Shandong Cancer Hospital), Jinan 250117, P. R. China;
4. Artificial Intelligence Laboratory, Shandong Cancer Institute (Shandong Cancer Hospital), Jinan 250117, P. R. China;
5. Department of Nuclear medicine, The Affiliated Hospital of Qingdao University, Qingdao, Shandong 266100, P. R. China;

Corresponding author：

ZHU Jian, Email: zhujian@sdfmu.edu.cn; WANG Linlin, Email: wanglinlinatjn@163.com

Keywords：

Radiotherapy positioning; Optical surface; Structured light; Point cloud; Noninvasive

DOI：

10.7507/1001-5515.202304052

Video：

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This paper aims to propose a noninvasive radiotherapy patient positioning system based on structured light surface imaging, and evaluate its clinical feasibility. First, structured light sensors were used to obtain the panoramic point clouds during radiotherapy positioning in real time. The fusion of different point clouds and coordinate transformation were realized based on optical calibration and pose estimation, and the body surface was segmented referring to the preset region of interest (ROI). Then, the global-local registration of cross-source point cloud was achieved based on algorithms such as random sample consensus (RANSAC) and iterative closest point (ICP), to calculate 6 degrees of freedom (DoF) positioning deviation and provide guidance for the correction of couch shifts. The evaluation of the system was carried out based on a rigid adult phantom and volunteers’ body, which included positioning error, correlation analysis, and receiver operating characteristic (ROC) analysis. Using Cone Beam CT (CBCT) as the gold standard, the maximum translation and rotation errors of this system were (1.5 ± 0.9) mm along Vrt direction (chest) and (0.7 ± 0.3) ° along Pitch direction (head and neck). The Pearson correlation coefficient between results of system outputs and CBCT verification distributed in an interval of [0.80, 0.84]. Results of ROC analysis showed that the translational and rotational AUC values were 0.82 and 0.85, respectively. In the 4D freedom accuracy test on the human body of volunteers, the maximum translation and rotation errors were (2.6 ± 1.1) mm (Vrt direction, chest and abdomen) and (0.8 ± 0.4)° (Rtn direction, chest and abdomen) respectively. In summary, the positioning system based on structured light body surface imaging proposed in this article can ensure positioning accuracy without surface markers and additional doses, and is feasible for clinical application.

1.	Langen K M, Willoughby T R, Meeks S L, et al. Observations on real-time prostate gland motion using electromagnetic tracking. Int J Radiat Oncol Biol Phys, 2008, 71(4): 1084-1090.
2.	Ricotti R, Ciardo D, Fattori G, et al. Intra-fraction respiratory motion and baseline drift during breast helical tomotherapy. Radiotherapy and Oncology, 2017, 122(1): 79-86.
3.	Sidhu S, Sidhu N P, Lapointe C, et al. The effects of intrafraction motion on dose homogeneity in a breast phantom with physical wedges, enhanced dynamic wedges, and ssIMRT. Int J Radiat Oncol Biol Phys, 2006, 66(1): 64-75.
4.	Yue N J, Li X, Beriwal S, et al. The intrafraction motion induced dosimetric impacts in breast 3D radiation treatment: a 4DCT based study. Medical Physics, 2007, 34(7): 2789-2800.
5.	Murphy M J, Balter J, Balter S, et al. The management of imaging dose during image-guided radiotherapy: report of the AAPM Task Group 75. Medical Physics, 2007, 34(10): 4041-4063.
6.	Batista V, Meyer J, Kügele M, et al. Clinical paradigms and challenges in surface guided radiation therapy: Where do we go from here?. Radiotherapy and Oncology, 2020, 153: 34-42.
7.	Zhao B, Park Y K, Gu X, et al. Surface guided motion management in glottic larynx stereotactic body radiation therapy. Radiotherapy and Oncology, 2020, 153: 236-242.
8.	Freislederer P, Kügele M, Öllers M, et al. Recent advances in surface guided radiation therapy. Radiation Oncology, 2020, 15(1): 187.
9.	Nazir S, Bert J, Fayad H, et al. Technical note: Surface imaging for real-time patient positioning in external radiation therapy. Medical Physics, 2021, 48(12): 8037-8044.
10.	Placht S, Stancanello J, Schaller C, et al. Fast time-of-flight camera based surface registration for radiotherapy patient positioning. Medical Physics, 2012, 39(1): 4-17.
11.	Schaller C, Penne J, Hornegger J. Time-of-flight sensor for respiratory motion gating. Medical physics, 2008, 35(7): 3090-3093.
12.	Zhang Z. A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(11): 1330-1334.
13.	Rusu R B, Blodow N, Beetz M. Fast point feature histograms (FPFH) for 3D registration//2009 IEEE International Conference on Robotics and Automation, IEEE, 2009: 3212-3217.
14.	Besl P J, Mckay H D. A method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1992, 14(2): 239-256.
15.	Chen Y, Medioni G. Object modeling by registration of multiple range images. Proceedings of the 1991 IEEE International Conference on Robotics and Automation, 1991, 3: 2724-2729.
16.	Islam M K, Purdie T G, Norrlinger B D, et al. Patient dose from kilovoltage cone beam computed tomography imaging in radiation therapy. Medical physics, 2006, 33(6): 1573-1582.
17.	Amer A, Marchant T, Sykes J, et al. Doses from cone beam CT integrated to a radiotherapy treatment machine. Clinical Oncology, 2005: 11635590.
18.	Cossmann P H, Stuessi A, von Briel C. 22 cone-beam CT experience in Aarau. Radiotherapy and Oncology, 2005, 76(2): S9.
19.	The International Commission on Radiological Protection, ICRP Publication 60: 1990 Recommendations of the International Commission on Radiological Protection. Ann ICRP, 1991: 208439033.
20.	毛玲丽, 刘红冬, 阳露, 等. MRI引导放射治疗设备研究进展. 中国医学影像技术, 2019, 35(4): 605-609.
21.	Alderliesten T, Sonke J J, Betgen A, et al. Accuracy evaluation of a 3-dimensional surface imaging system for guidance in deep-inspiration breath-hold radiation therapy. Int J Radiat Oncol Biol Phys, 2013, 85(2): 536-542.
22.	Gierga D, Turcotte J C, Sedlacek D E, et al. A voluntary breath-hold treatment technique for the left breast with unfavorable cardiac anatomy using surface imaging. Int J Radiat Oncol Biol Phys, 2012, 84(5): e663-e668.
23.	Walter F, Freislederer P, Belka C, et al. Evaluation of daily patient positioning for radiotherapy with a commercial 3D surface-imaging system (Catalyst™). Radiat Oncol, 2016, 11(1): 154.
24.	Stieler F, Wenz F, Shi M, et al. A novel surface imaging system for patient positioning and surveillance during radiotherapy. A phantom study and clinical evaluation. Strahlenther Onkol. 2013, 189(11): 938-944.
25.	Aoki Y, Goforth H, Srivatsan R A, et al. PointNetLK: robust & efficient point cloud registration using pointnet//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach: IEEE, 2019: 7156-7165.
26.	Pais G D, Ramalingam S, Govindu V M, et al. 3DRegNet: a deep neural network for 3D point registration. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle: IEEE, 2020: 7191-7201.
27.	Al-Hallaq H, Batista V, Kügele M, et al. The role of surface-guided radiation therapy for improving patient safety. Radiotherapy and Oncology, 2021, 163: 229-236.
28.	李春迎, 陆正大, 和睦, 等. 三维可视化引导放疗摆位系统的研发及临床应用. 中华放射医学与防护杂志, 2021, 41(7): 492-498.

1. Langen K M, Willoughby T R, Meeks S L, et al. Observations on real-time prostate gland motion using electromagnetic tracking. Int J Radiat Oncol Biol Phys, 2008, 71(4): 1084-1090.
2. Ricotti R, Ciardo D, Fattori G, et al. Intra-fraction respiratory motion and baseline drift during breast helical tomotherapy. Radiotherapy and Oncology, 2017, 122(1): 79-86.
3. Sidhu S, Sidhu N P, Lapointe C, et al. The effects of intrafraction motion on dose homogeneity in a breast phantom with physical wedges, enhanced dynamic wedges, and ssIMRT. Int J Radiat Oncol Biol Phys, 2006, 66(1): 64-75.
4. Yue N J, Li X, Beriwal S, et al. The intrafraction motion induced dosimetric impacts in breast 3D radiation treatment: a 4DCT based study. Medical Physics, 2007, 34(7): 2789-2800.
5. Murphy M J, Balter J, Balter S, et al. The management of imaging dose during image-guided radiotherapy: report of the AAPM Task Group 75. Medical Physics, 2007, 34(10): 4041-4063.
6. Batista V, Meyer J, Kügele M, et al. Clinical paradigms and challenges in surface guided radiation therapy: Where do we go from here?. Radiotherapy and Oncology, 2020, 153: 34-42.
7. Zhao B, Park Y K, Gu X, et al. Surface guided motion management in glottic larynx stereotactic body radiation therapy. Radiotherapy and Oncology, 2020, 153: 236-242.
8. Freislederer P, Kügele M, Öllers M, et al. Recent advances in surface guided radiation therapy. Radiation Oncology, 2020, 15(1): 187.
9. Nazir S, Bert J, Fayad H, et al. Technical note: Surface imaging for real-time patient positioning in external radiation therapy. Medical Physics, 2021, 48(12): 8037-8044.
10. Placht S, Stancanello J, Schaller C, et al. Fast time-of-flight camera based surface registration for radiotherapy patient positioning. Medical Physics, 2012, 39(1): 4-17.
11. Schaller C, Penne J, Hornegger J. Time-of-flight sensor for respiratory motion gating. Medical physics, 2008, 35(7): 3090-3093.
12. Zhang Z. A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(11): 1330-1334.
13. Rusu R B, Blodow N, Beetz M. Fast point feature histograms (FPFH) for 3D registration//2009 IEEE International Conference on Robotics and Automation, IEEE, 2009: 3212-3217.
14. Besl P J, Mckay H D. A method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1992, 14(2): 239-256.
15. Chen Y, Medioni G. Object modeling by registration of multiple range images. Proceedings of the 1991 IEEE International Conference on Robotics and Automation, 1991, 3: 2724-2729.
16. Islam M K, Purdie T G, Norrlinger B D, et al. Patient dose from kilovoltage cone beam computed tomography imaging in radiation therapy. Medical physics, 2006, 33(6): 1573-1582.
17. Amer A, Marchant T, Sykes J, et al. Doses from cone beam CT integrated to a radiotherapy treatment machine. Clinical Oncology, 2005: 11635590.
18. Cossmann P H, Stuessi A, von Briel C. 22 cone-beam CT experience in Aarau. Radiotherapy and Oncology, 2005, 76(2): S9.
19. The International Commission on Radiological Protection, ICRP Publication 60: 1990 Recommendations of the International Commission on Radiological Protection. Ann ICRP, 1991: 208439033.
20. 毛玲丽, 刘红冬, 阳露, 等. MRI引导放射治疗设备研究进展. 中国医学影像技术, 2019, 35(4): 605-609.
21. Alderliesten T, Sonke J J, Betgen A, et al. Accuracy evaluation of a 3-dimensional surface imaging system for guidance in deep-inspiration breath-hold radiation therapy. Int J Radiat Oncol Biol Phys, 2013, 85(2): 536-542.
22. Gierga D, Turcotte J C, Sedlacek D E, et al. A voluntary breath-hold treatment technique for the left breast with unfavorable cardiac anatomy using surface imaging. Int J Radiat Oncol Biol Phys, 2012, 84(5): e663-e668.
23. Walter F, Freislederer P, Belka C, et al. Evaluation of daily patient positioning for radiotherapy with a commercial 3D surface-imaging system (Catalyst™). Radiat Oncol, 2016, 11(1): 154.
24. Stieler F, Wenz F, Shi M, et al. A novel surface imaging system for patient positioning and surveillance during radiotherapy. A phantom study and clinical evaluation. Strahlenther Onkol. 2013, 189(11): 938-944.
25. Aoki Y, Goforth H, Srivatsan R A, et al. PointNetLK: robust & efficient point cloud registration using pointnet//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach: IEEE, 2019: 7156-7165.
26. Pais G D, Ramalingam S, Govindu V M, et al. 3DRegNet: a deep neural network for 3D point registration. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle: IEEE, 2020: 7191-7201.
27. Al-Hallaq H, Batista V, Kügele M, et al. The role of surface-guided radiation therapy for improving patient safety. Radiotherapy and Oncology, 2021, 163: 229-236.
28. 李春迎, 陆正大, 和睦, 等. 三维可视化引导放疗摆位系统的研发及临床应用. 中华放射医学与防护杂志, 2021, 41(7): 492-498.

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