Chest Wall Motion Tracking By Contactless Optical Single Camera-Based Method Using Virtual Markers, a feasibility study
contactless chest motion tracking by optical camera
Abstract
Purpose: Real time motion tracking of thorax area of patient body is a main issue at various part of medical fields such as radiotherapy. Several strategies were proposed by using different monitoring hardwares. In this work a contactless method using optical camera is proposed to trace breathing motion by implementing virtual markers defined on chest area. A comprehensive algorithm has been developed to analyze the video frames and track each virtual point as real time. Methods: In this wotk, Python program and its OpenCV library has been used for breathing motion, two dimensionally. Utilized database in this work are motion data taken from breathing motion of a real volunteer. The motion data was captured using cellphone optical camera and the gathered data was moved to in-room computer system by means of WiFi. It’s worth mentioning that 15 virutal test points were determiened using Artifical Intelligence concept of Python inside chest area. Results: Final results represent that the performance accuracy of monitoring proposed idea is acceptable. The chest area is determined automatically and will be variable for each patient, uniquely. Various normal and deep breathings was tested as real time at different respiration frequencies. As example, two dimentional motion displacements of a test point, are 4.75 and 7.15 mm for normal and deep breathing, respectively. Conclusion: The main robusts of the proposed motion tracking method are simplicity, contactless and using virtual markers determination, while real infra-red markers are currently used clinically by locating on patient chest skin.
1. Chen, Q., et al., Nanoparticle-Enhanced Radiotherapy to Trigger Robust Cancer Immunotherapy. Advanced Materials, 2019. 31(10): p. 1802228.
2. Lee, H.A., et al., Efficacy and feasibility of surgery and external radiotherapy for hepatocellular carcinoma with portal invasion: A meta-analysis. International Journal of Surgery, 2022. 104: p. 106753.
3. Shirvani, S.M., et al., Biology-guided radiotherapy: redefining the role of radiotherapy in metastatic cancer. The British Journal of Radiology, 2020. 94(1117): p. 20200873.
4. Tan Mbbs, M.F.M.D.L.T., et al., Image-guided Adaptive Radiotherapy in Cervical Cancer. Seminars in Radiation Oncology, 2019. 29(3): p. 284-298.
5. Yock, A.D., et al., Robustness Analysis for External Beam Radiation Therapy Treatment Plans: Describing Uncertainty Scenarios and Reporting Their Dosimetric Consequences. Practical Radiation Oncology, 2019. 9(4): p. 200-207.
6. Lotz, H.T., et al., Tumor motion and deformation during external radiotherapy of bladder cancer. International Journal of Radiation Oncology*Biology*Physics, 2006. 64(5): p. 1551-1558.
7. Seppenwoolde, Y., et al., Precise and real-time measurement of 3D tumor motion in lung due to breathing and heartbeat, measured during radiotherapy. International Journal of Radiation Oncology*Biology*Physics, 2002. 53(4): p. 822-834.
8. Ballhausen, H., et al., Shorter treatment times reduce the impact of intra-fractional motion. Strahlentherapie und Onkologie, 2018. 194(7): p. 664-674.
9. Prescribing, I., Recording and reporting photon beam therapy. ICRU Report 50. Bethesda, MD: International Commission on Radiation Units and Measurements, 1993.
10. De Bari, B., N. Sellal, and F. Mornex, [4D-CT scan and radiotherapy for hepatocellular carcinoma: role in the definition of internal target volume (ITV)]. Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique, 2011. 15(1): p. 43-48.
11. Ge, H., et al., Quantification and Minimization of Uncertainties of Internal Target Volume for Stereotactic Body Radiation Therapy of Lung Cancer. International Journal of Radiation Oncology*Biology*Physics, 2013. 85(2): p. 438-443.
12. Jin, J.-Y., et al., A technique of using gated-CT images to determine internal target volume (ITV) for fractionated stereotactic lung radiotherapy. Radiotherapy and Oncology, 2006. 78(2): p. 177-184.
13. Liu, H.H., et al., Assessing Respiration-Induced Tumor Motion and Internal Target Volume Using Four-Dimensional Computed Tomography for Radiotherapy of Lung Cancer. International Journal of Radiation Oncology*Biology*Physics, 2007. 68(2): p. 531-540.
14. Liu, H.H., et al., Evaluation of internal lung motion for respiratory-gated radiotherapy using MRI: Part II—margin reduction of internal target volume. International Journal of Radiation Oncology*Biology*Physics, 2004. 60(5): p. 1473-1483.
15. De Rose, F., et al., Organs at risk in lung SBRT. Physica Medica, 2017. 44: p. 131-138.
16. Nishimura, S., et al., Toxicities of Organs at Risk in the Mediastinal and Hilar Regions Following Stereotactic Body Radiotherapy for Centrally Located Lung Tumors. Journal of Thoracic Oncology, 2014. 9(9): p. 1370-1376.
17. Onimaru, R., et al., Tolerance of organs at risk in small-volume, hypofractionated, image-guided radiotherapy for primary and metastatic lung cancers. International Journal of Radiation Oncology*Biology*Physics, 2003. 56(1): p. 126-135.
18. Tsang, Y., et al., Assessment of contour variability in target volumes and organs at risk in lung cancer radiotherapy. Technical Innovations & Patient Support in Radiation Oncology, 2019. 10: p. 8-12.
19. Hanley, J., et al., Deep inspiration breath-hold technique for lung tumors: the potential value of target immobilization and reduced lung density in dose escalation. International Journal of Radiation Oncology*Biology*Physics, 1999. 45(3): p. 603-611.
20. Keall, P. and G. Mageras, Managing respiratory motion in radiation therapy. American Association of Physicists in Medicine, 2004.
21. Mageras, G.S. and E. Yorke, Deep inspiration breath hold and respiratory gating strategies for reducing organ motion in radiation treatment. Semin Radiat Oncol, 2004. 14(1): p. 65-75.
22. Esmaili Torshabi, A. and L. Ghorbanzadeh, A study on stereoscopic x-ray imaging data set on the accuracy of real-time tumor tracking in external beam radiotherapy. Technology in cancer research & treatment, 2017. 16(2): p. 167-177.
23. Ghorbanzadeh, L. and A.E. Torshabi, An Investigation into the Performance of Adaptive Neuro-Fuzzy Inference System for Brain Tumor Delineation Using ExpectationMaximization Cluster Method; a Feasibility Study. Frontiers in Biomedical Technologies, 2016. 3(1-2): p. 8-19.
24. Ghorbanzadeh, L., et al., Development of a synthetic adaptive neuro-fuzzy prediction model for tumor motion tracking in external radiotherapy by evaluating various data clustering algorithms. Technology in cancer research & treatment, 2016. 15(2): p. 334-347.
25. Miandoab, P.S., A.E. Torshabi, and S. Nankali, Investigation of the optimum location of external markers for patient setup accuracy enhancement at external beam radiotherapy. Journal of applied clinical medical physics, 2016. 17(6): p. 32-43.
26. Miandoab, P.S., A.E. Torshabi, and S. Nankali, Extraction of respiratory signal based on image clustering and intensity parameters at radiotherapy with external beam: A comparative study. Journal of biomedical physics & engineering, 2016. 6(4): p. 253.
27. Nankali, S., A.E. Torshabi, and P.S. Miandoab, A feasibility study on ribs as anatomical landmarks for motion tracking of lung and liver tumors at external beam radiotherapy. Technology in cancer research & treatment, 2017. 16(1): p. 99-111.
28. Nankali, S., et al., Investigation on performance accuracy of different surrogates in real time tumor tracking at external beam radiotherapy. Frontiers in Biomedical Technologies, 2015. 2(2): p. 73-79.
29. Parande, S., A Study on Robustness of Various Deformable Image Registration Algorithms on Image Reconstruction Using 4DCT Thoracic Images. Journal of Biomedical Physics & Engineering, 2019. 9(5): p. 559.
30. Samadi Miandoab, P., et al., A simulation study on patient setup errors in external beam radiotherapy using an anthropomorphic 4D phantom. Iranian Journal of Medical Physics, 2016. 13(4): p. 276-288.
31. Samadi Miandoab, P., A. Esmaili Torshabi, and S. Parandeh, Calculation of inter-and intra-fraction motion errors at external radiotherapy using a markerless strategy based on image registration combined with correlation model. Iranian Journal of medical physics, 2019. 16(3): p. 224-231.
32. Samadi-Miyandoab, P., A. Torshabi, and S. Nankali, 2D and 3D Optical Flow Based Interpolation of the 4DCT ImageSequences in the External Beam Radiotherapy. Frontiers in Biomedical Technologies, 2015. 2(2): p. 93-102.
33. Samadi-Miyandoab, P., et al., The robustness of various intelligent models in patient positioning at external beam radiotherapy. Frontiers in Biomedical Technologies, 2015. 2(1): p. 45-54.
34. Torshabi, A.E., et al., Targeting Accuracy in Real-time Tumor Tracking via External Surrogates: A Comparative Study. Technology in Cancer Research & Treatment, 2010. 9(6): p. 551-561.
35. Torshabi, A.E., et al., An adaptive fuzzy prediction model for real time tumor tracking in radiotherapy via external surrogates. Journal of Applied Clinical Medical Physics, 2013. 14(1): p. 102-114.
36. Brody, W.R., et al., Dual-energy projection radiography: initial clinical experience. AJR Am J Roentgenol, 1981. 137(2): p. 201-5.
37. Patel, R., et al., Markerless motion tracking of lung tumors using dual-energy fluoroscopy. Medical Physics, 2015. 42(1): p. 254-262.
38. Berkhout, W.E.R., [The ALARA-principle. Backgrounds and enforcement in dental practices]. Nederlands tijdschrift voor tandheelkunde, 2015. 122(5): p. 263-270.
39. Musolino, S.V., J. DeFranco, and R. Schlueck, THE ALARA PRINCIPLE IN THE CONTEXT OF A RADIOLOGICAL OR NUCLEAR EMERGENCY. Health Physics, 2008. 94(2).
40. Yeung, A.W.K., The 'As Low As Reasonably Achievable' (ALARA) principle: a brief historical overview and a bibliometric analysis of the most cited publications. Radioprotection, 2019. 54(2): p. 103.
41. Hsu, J.C., et al., Nanoparticle contrast agents for X-ray imaging applications. WIREs Nanomedicine and Nanobiotechnology, 2020. 12(6): p. e1642.
42. Wallyn, J., et al., Biomedical Imaging: Principles, Technologies, Clinical Aspects, Contrast Agents, Limitations and Future Trends in Nanomedicines. Pharmaceutical Research, 2019. 36(6): p. 78.
43. Zaky Harun, A., et al., Evaluation of Contrast-Noise Ratio (CNR) in contrast enhanced CT images using different sizes of gold nanoparticles. Materials Today: Proceedings, 2019. 16: p. 1757-1765.
44. Jackson, P., et al., Evaluation of the effects of gold nanoparticle shape and size on contrast enhancement in radiological imaging. Australasian Physical & Engineering Sciences in Medicine, 2011. 34(2): p. 243-249.
45. Lafrenière, M., et al., Continuous generation of volumetric images during stereotactic body radiation therapy using periodic kV imaging and an external respiratory surrogate. Physica Medica, 2019. 63: p. 25-34.
46. Li, Y., et al., Utilization of Diaphragm Motion to Predict the Displacement of Liver Tumors for Patients Treated with Carbon ion Radiotherapy. Technology in Cancer Research & Treatment, 2023. 22: p. 15330338231164195.
47. Remy, C. and H. Bouchard, Adaptive confidence regions for indirect tracking of moving tumors in radiotherapy. Medical Physics, 2022. 49(7): p. 4273-4283.
48. Wang, G., et al., Correlation of optical surface respiratory motion signal and internal lung and liver tumor motion: a retrospective single-center observational study. Technology in Cancer Research & Treatment, 2022. 21: p. 15330338221112280.
49. Shuangbao, W., et al. SMIS - a real-time stereoscopic medical imaging system. in Proceedings. 17th IEEE Symposium on Computer-Based Medical Systems. 2004.
50. Withers, P.J., et al., X-ray computed tomography. Nature Reviews Methods Primers, 2021. 1(1): p. 18.
51. Vanegas, E., R. Igual, and I. Plaza, Piezoresistive Breathing Sensing System with 3D Printed Wearable Casing. Journal of Sensors, 2019. 2019: p. 2431731.
52. Grlica, J., T. Martinović, and H. Džapo. Capacitive sensor for respiration monitoring. in 2015 IEEE Sensors Applications Symposium (SAS). 2015.
53. Dang, T.T., et al. A Tool Using Ultrasonic Sensor for Measuring Breathing Rate. in 6th International Conference on the Development of Biomedical Engineering in Vietnam (BME6). 2018. Singapore: Springer Singapore.
54. Islam, S.M., O. Boric-Lubecke, and V.M. Lubekce, Concurrent respiration monitoring of multiple subjects by phase-comparison monopulse radar using independent component analysis (ICA) with JADE algorithm and direction of arrival (DOA). IEEE Access, 2020. 8: p. 73558-73569.
55. Addison, A.P., et al., Noncontact respiratory monitoring using depth sensing cameras: A review of current literature. Sensors, 2021. 21(4): p. 1135.
56. Li, M.H., A. Yadollahi, and B. Taati, Noncontact Vision-Based Cardiopulmonary Monitoring in Different Sleeping Positions. IEEE Journal of Biomedical and Health Informatics, 2017. 21(5): p. 1367-1375.
57. Massaroni, C., et al., Contactless Monitoring of Breathing Patterns and Respiratory Rate at the Pit of the Neck: A Single Camera Approach. Journal of Sensors, 2018. 2018: p. 4567213.
58. Mateu-Mateus, M., et al., Camera-Based Method for Respiratory Rhythm Extraction From a Lateral Perspective. IEEE Access, 2020. 8: p. 154924-154939.
59. Romano, C., et al. Non-Contact Respiratory Monitoring Using an RGB Camera for Real-World Applications. Sensors, 2021. 21, DOI: 10.3390/s21155126.
60. Tarassenko, L., et al., Non-contact video-based vital sign monitoring using ambient light and auto-regressive models. Physiological Measurement, 2014. 35(5): p. 807.
61. Schweikard, A., H. Shiomi, and J. Adler, Respiration tracking in radiosurgery without fiducials. The International Journal of Medical Robotics and Computer Assisted Surgery, 2005. 1(2): p. 19-27.
62. Massaroni, C., et al., Contactless methods for measuring respiratory rate: A review. IEEE Sensors Journal, 2020. 21(11): p. 12821-12839.
63. Gilbert, R., et al., Changes in tidal volume, frequency, and ventilation induced by their measurement. Journal of Applied Physiology, 1972. 33(2): p. 252-254.
64. Agarwal, A., S. Gupta, and D.K. Singh. Review of optical flow technique for moving object detection. in 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I). 2016.
65. Kale, K., S. Pawar, and P. Dhulekar. Moving object tracking using optical flow and motion vector estimation. in 2015 4th international conference on reliability, infocom technologies and optimization (ICRITO)(trends and future directions). 2015. IEEE.
66. Klappstein, J., et al. Moving object segmentation using optical flow and depth information. in Pacific-Rim Symposium on Image and Video Technology. 2009. Springer.
67. Revathi, R. and M. Hemalatha, Certain Approach of Object Tracking using Optical Flow Techniques. International Journal of Computer Applications, 2012. 53(8).
2. Lee, H.A., et al., Efficacy and feasibility of surgery and external radiotherapy for hepatocellular carcinoma with portal invasion: A meta-analysis. International Journal of Surgery, 2022. 104: p. 106753.
3. Shirvani, S.M., et al., Biology-guided radiotherapy: redefining the role of radiotherapy in metastatic cancer. The British Journal of Radiology, 2020. 94(1117): p. 20200873.
4. Tan Mbbs, M.F.M.D.L.T., et al., Image-guided Adaptive Radiotherapy in Cervical Cancer. Seminars in Radiation Oncology, 2019. 29(3): p. 284-298.
5. Yock, A.D., et al., Robustness Analysis for External Beam Radiation Therapy Treatment Plans: Describing Uncertainty Scenarios and Reporting Their Dosimetric Consequences. Practical Radiation Oncology, 2019. 9(4): p. 200-207.
6. Lotz, H.T., et al., Tumor motion and deformation during external radiotherapy of bladder cancer. International Journal of Radiation Oncology*Biology*Physics, 2006. 64(5): p. 1551-1558.
7. Seppenwoolde, Y., et al., Precise and real-time measurement of 3D tumor motion in lung due to breathing and heartbeat, measured during radiotherapy. International Journal of Radiation Oncology*Biology*Physics, 2002. 53(4): p. 822-834.
8. Ballhausen, H., et al., Shorter treatment times reduce the impact of intra-fractional motion. Strahlentherapie und Onkologie, 2018. 194(7): p. 664-674.
9. Prescribing, I., Recording and reporting photon beam therapy. ICRU Report 50. Bethesda, MD: International Commission on Radiation Units and Measurements, 1993.
10. De Bari, B., N. Sellal, and F. Mornex, [4D-CT scan and radiotherapy for hepatocellular carcinoma: role in the definition of internal target volume (ITV)]. Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique, 2011. 15(1): p. 43-48.
11. Ge, H., et al., Quantification and Minimization of Uncertainties of Internal Target Volume for Stereotactic Body Radiation Therapy of Lung Cancer. International Journal of Radiation Oncology*Biology*Physics, 2013. 85(2): p. 438-443.
12. Jin, J.-Y., et al., A technique of using gated-CT images to determine internal target volume (ITV) for fractionated stereotactic lung radiotherapy. Radiotherapy and Oncology, 2006. 78(2): p. 177-184.
13. Liu, H.H., et al., Assessing Respiration-Induced Tumor Motion and Internal Target Volume Using Four-Dimensional Computed Tomography for Radiotherapy of Lung Cancer. International Journal of Radiation Oncology*Biology*Physics, 2007. 68(2): p. 531-540.
14. Liu, H.H., et al., Evaluation of internal lung motion for respiratory-gated radiotherapy using MRI: Part II—margin reduction of internal target volume. International Journal of Radiation Oncology*Biology*Physics, 2004. 60(5): p. 1473-1483.
15. De Rose, F., et al., Organs at risk in lung SBRT. Physica Medica, 2017. 44: p. 131-138.
16. Nishimura, S., et al., Toxicities of Organs at Risk in the Mediastinal and Hilar Regions Following Stereotactic Body Radiotherapy for Centrally Located Lung Tumors. Journal of Thoracic Oncology, 2014. 9(9): p. 1370-1376.
17. Onimaru, R., et al., Tolerance of organs at risk in small-volume, hypofractionated, image-guided radiotherapy for primary and metastatic lung cancers. International Journal of Radiation Oncology*Biology*Physics, 2003. 56(1): p. 126-135.
18. Tsang, Y., et al., Assessment of contour variability in target volumes and organs at risk in lung cancer radiotherapy. Technical Innovations & Patient Support in Radiation Oncology, 2019. 10: p. 8-12.
19. Hanley, J., et al., Deep inspiration breath-hold technique for lung tumors: the potential value of target immobilization and reduced lung density in dose escalation. International Journal of Radiation Oncology*Biology*Physics, 1999. 45(3): p. 603-611.
20. Keall, P. and G. Mageras, Managing respiratory motion in radiation therapy. American Association of Physicists in Medicine, 2004.
21. Mageras, G.S. and E. Yorke, Deep inspiration breath hold and respiratory gating strategies for reducing organ motion in radiation treatment. Semin Radiat Oncol, 2004. 14(1): p. 65-75.
22. Esmaili Torshabi, A. and L. Ghorbanzadeh, A study on stereoscopic x-ray imaging data set on the accuracy of real-time tumor tracking in external beam radiotherapy. Technology in cancer research & treatment, 2017. 16(2): p. 167-177.
23. Ghorbanzadeh, L. and A.E. Torshabi, An Investigation into the Performance of Adaptive Neuro-Fuzzy Inference System for Brain Tumor Delineation Using ExpectationMaximization Cluster Method; a Feasibility Study. Frontiers in Biomedical Technologies, 2016. 3(1-2): p. 8-19.
24. Ghorbanzadeh, L., et al., Development of a synthetic adaptive neuro-fuzzy prediction model for tumor motion tracking in external radiotherapy by evaluating various data clustering algorithms. Technology in cancer research & treatment, 2016. 15(2): p. 334-347.
25. Miandoab, P.S., A.E. Torshabi, and S. Nankali, Investigation of the optimum location of external markers for patient setup accuracy enhancement at external beam radiotherapy. Journal of applied clinical medical physics, 2016. 17(6): p. 32-43.
26. Miandoab, P.S., A.E. Torshabi, and S. Nankali, Extraction of respiratory signal based on image clustering and intensity parameters at radiotherapy with external beam: A comparative study. Journal of biomedical physics & engineering, 2016. 6(4): p. 253.
27. Nankali, S., A.E. Torshabi, and P.S. Miandoab, A feasibility study on ribs as anatomical landmarks for motion tracking of lung and liver tumors at external beam radiotherapy. Technology in cancer research & treatment, 2017. 16(1): p. 99-111.
28. Nankali, S., et al., Investigation on performance accuracy of different surrogates in real time tumor tracking at external beam radiotherapy. Frontiers in Biomedical Technologies, 2015. 2(2): p. 73-79.
29. Parande, S., A Study on Robustness of Various Deformable Image Registration Algorithms on Image Reconstruction Using 4DCT Thoracic Images. Journal of Biomedical Physics & Engineering, 2019. 9(5): p. 559.
30. Samadi Miandoab, P., et al., A simulation study on patient setup errors in external beam radiotherapy using an anthropomorphic 4D phantom. Iranian Journal of Medical Physics, 2016. 13(4): p. 276-288.
31. Samadi Miandoab, P., A. Esmaili Torshabi, and S. Parandeh, Calculation of inter-and intra-fraction motion errors at external radiotherapy using a markerless strategy based on image registration combined with correlation model. Iranian Journal of medical physics, 2019. 16(3): p. 224-231.
32. Samadi-Miyandoab, P., A. Torshabi, and S. Nankali, 2D and 3D Optical Flow Based Interpolation of the 4DCT ImageSequences in the External Beam Radiotherapy. Frontiers in Biomedical Technologies, 2015. 2(2): p. 93-102.
33. Samadi-Miyandoab, P., et al., The robustness of various intelligent models in patient positioning at external beam radiotherapy. Frontiers in Biomedical Technologies, 2015. 2(1): p. 45-54.
34. Torshabi, A.E., et al., Targeting Accuracy in Real-time Tumor Tracking via External Surrogates: A Comparative Study. Technology in Cancer Research & Treatment, 2010. 9(6): p. 551-561.
35. Torshabi, A.E., et al., An adaptive fuzzy prediction model for real time tumor tracking in radiotherapy via external surrogates. Journal of Applied Clinical Medical Physics, 2013. 14(1): p. 102-114.
36. Brody, W.R., et al., Dual-energy projection radiography: initial clinical experience. AJR Am J Roentgenol, 1981. 137(2): p. 201-5.
37. Patel, R., et al., Markerless motion tracking of lung tumors using dual-energy fluoroscopy. Medical Physics, 2015. 42(1): p. 254-262.
38. Berkhout, W.E.R., [The ALARA-principle. Backgrounds and enforcement in dental practices]. Nederlands tijdschrift voor tandheelkunde, 2015. 122(5): p. 263-270.
39. Musolino, S.V., J. DeFranco, and R. Schlueck, THE ALARA PRINCIPLE IN THE CONTEXT OF A RADIOLOGICAL OR NUCLEAR EMERGENCY. Health Physics, 2008. 94(2).
40. Yeung, A.W.K., The 'As Low As Reasonably Achievable' (ALARA) principle: a brief historical overview and a bibliometric analysis of the most cited publications. Radioprotection, 2019. 54(2): p. 103.
41. Hsu, J.C., et al., Nanoparticle contrast agents for X-ray imaging applications. WIREs Nanomedicine and Nanobiotechnology, 2020. 12(6): p. e1642.
42. Wallyn, J., et al., Biomedical Imaging: Principles, Technologies, Clinical Aspects, Contrast Agents, Limitations and Future Trends in Nanomedicines. Pharmaceutical Research, 2019. 36(6): p. 78.
43. Zaky Harun, A., et al., Evaluation of Contrast-Noise Ratio (CNR) in contrast enhanced CT images using different sizes of gold nanoparticles. Materials Today: Proceedings, 2019. 16: p. 1757-1765.
44. Jackson, P., et al., Evaluation of the effects of gold nanoparticle shape and size on contrast enhancement in radiological imaging. Australasian Physical & Engineering Sciences in Medicine, 2011. 34(2): p. 243-249.
45. Lafrenière, M., et al., Continuous generation of volumetric images during stereotactic body radiation therapy using periodic kV imaging and an external respiratory surrogate. Physica Medica, 2019. 63: p. 25-34.
46. Li, Y., et al., Utilization of Diaphragm Motion to Predict the Displacement of Liver Tumors for Patients Treated with Carbon ion Radiotherapy. Technology in Cancer Research & Treatment, 2023. 22: p. 15330338231164195.
47. Remy, C. and H. Bouchard, Adaptive confidence regions for indirect tracking of moving tumors in radiotherapy. Medical Physics, 2022. 49(7): p. 4273-4283.
48. Wang, G., et al., Correlation of optical surface respiratory motion signal and internal lung and liver tumor motion: a retrospective single-center observational study. Technology in Cancer Research & Treatment, 2022. 21: p. 15330338221112280.
49. Shuangbao, W., et al. SMIS - a real-time stereoscopic medical imaging system. in Proceedings. 17th IEEE Symposium on Computer-Based Medical Systems. 2004.
50. Withers, P.J., et al., X-ray computed tomography. Nature Reviews Methods Primers, 2021. 1(1): p. 18.
51. Vanegas, E., R. Igual, and I. Plaza, Piezoresistive Breathing Sensing System with 3D Printed Wearable Casing. Journal of Sensors, 2019. 2019: p. 2431731.
52. Grlica, J., T. Martinović, and H. Džapo. Capacitive sensor for respiration monitoring. in 2015 IEEE Sensors Applications Symposium (SAS). 2015.
53. Dang, T.T., et al. A Tool Using Ultrasonic Sensor for Measuring Breathing Rate. in 6th International Conference on the Development of Biomedical Engineering in Vietnam (BME6). 2018. Singapore: Springer Singapore.
54. Islam, S.M., O. Boric-Lubecke, and V.M. Lubekce, Concurrent respiration monitoring of multiple subjects by phase-comparison monopulse radar using independent component analysis (ICA) with JADE algorithm and direction of arrival (DOA). IEEE Access, 2020. 8: p. 73558-73569.
55. Addison, A.P., et al., Noncontact respiratory monitoring using depth sensing cameras: A review of current literature. Sensors, 2021. 21(4): p. 1135.
56. Li, M.H., A. Yadollahi, and B. Taati, Noncontact Vision-Based Cardiopulmonary Monitoring in Different Sleeping Positions. IEEE Journal of Biomedical and Health Informatics, 2017. 21(5): p. 1367-1375.
57. Massaroni, C., et al., Contactless Monitoring of Breathing Patterns and Respiratory Rate at the Pit of the Neck: A Single Camera Approach. Journal of Sensors, 2018. 2018: p. 4567213.
58. Mateu-Mateus, M., et al., Camera-Based Method for Respiratory Rhythm Extraction From a Lateral Perspective. IEEE Access, 2020. 8: p. 154924-154939.
59. Romano, C., et al. Non-Contact Respiratory Monitoring Using an RGB Camera for Real-World Applications. Sensors, 2021. 21, DOI: 10.3390/s21155126.
60. Tarassenko, L., et al., Non-contact video-based vital sign monitoring using ambient light and auto-regressive models. Physiological Measurement, 2014. 35(5): p. 807.
61. Schweikard, A., H. Shiomi, and J. Adler, Respiration tracking in radiosurgery without fiducials. The International Journal of Medical Robotics and Computer Assisted Surgery, 2005. 1(2): p. 19-27.
62. Massaroni, C., et al., Contactless methods for measuring respiratory rate: A review. IEEE Sensors Journal, 2020. 21(11): p. 12821-12839.
63. Gilbert, R., et al., Changes in tidal volume, frequency, and ventilation induced by their measurement. Journal of Applied Physiology, 1972. 33(2): p. 252-254.
64. Agarwal, A., S. Gupta, and D.K. Singh. Review of optical flow technique for moving object detection. in 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I). 2016.
65. Kale, K., S. Pawar, and P. Dhulekar. Moving object tracking using optical flow and motion vector estimation. in 2015 4th international conference on reliability, infocom technologies and optimization (ICRITO)(trends and future directions). 2015. IEEE.
66. Klappstein, J., et al. Moving object segmentation using optical flow and depth information. in Pacific-Rim Symposium on Image and Video Technology. 2009. Springer.
67. Revathi, R. and M. Hemalatha, Certain Approach of Object Tracking using Optical Flow Techniques. International Journal of Computer Applications, 2012. 53(8).
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| Issue | Vol 13 No 1 (2026) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/fbt.v13i1.20770 | |
| Keywords | ||
| Motion Capture Optical Devices Virtual Markers Targeted Radiotherapy | ||
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How to Cite
1.
Bijari MA, Esmaili Torshabi A. Chest Wall Motion Tracking By Contactless Optical Single Camera-Based Method Using Virtual Markers, a feasibility study. Frontiers Biomed Technol. 2026;13(1):104-116.
