Skip to main content
SpringerLink
Log in

Respiratory motion tracking of the thoracoabdominal surface based on defect-aware point cloud registration

  • Original Article
  • Published:
Biomedical Engineering Letters Aims and scope Submit manuscript

Abstract

The performance of conventional lung puncture surgery is a complex undertaking due to the surgeon’s reliance on visual assessment of respiratory conditions and the manual execution of the technique while the patient maintains breath-holding. However, the failure to correctly perform a puncture technique can lead to negative outcomes, such as the development of sores and pneumothorax. In this work, we proposed a novel approach for monitoring respiratory motion by utilizing defect-aware point cloud registration and descriptor computation. Through a thorough examination of the attributes of the inputs, we suggest the incorporation of a defect detection branch into the registration network. Additionally, we developed two modules with the aim of augmenting the quality of the extracted features. A coarse-to-fine respiratory phase recognition approach based on descriptor computation is devised for the respiratory motion tracking. The efficacy of the suggested registration method is demonstrated through experimental findings conducted on both publicly accessible datasets and thoracoabdominal point cloud datasets. We obtained state-of-the-art registration results on ModelNet40 datasets, with 1.584\(^\circ\) on rotation mean absolute error and 0.016 mm on translation mean absolute error, respectively. The experimental findings conducted on a thoracoabdominal point cloud dataset indicate that our method exhibits efficacy and efficiency, achieving a frame matching rate of 2 frames per second and a phase recognition accuracy of 96.3%. This allows identifying matching frames from template point clouds that display different parts of a patient’s thoracoabdominal surface while breathing regularly to distinguish breathing stages and track breathing.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Nakamura K, Matsumoto K, Inoue C, Matsusue E, Fujii S. Computed tomography-guided lung biopsy: a review of techniques for reducing the incidence of complications. Interv Radiol. 2021;6:83–92.

    Article  Google Scholar 

  2. Zhang L, Li C, Fan Y, Zhang X, Zhao J. Physician-friendly tool center point calibration method for robot-assisted puncture surgery. Sensors. 2021;21:366.

    Article  Google Scholar 

  3. Guimarães MD, Marchiori E, Hochhegger B, Chojniak R, Gross JL. Ct-guided biopsy of lung lesions: defining the best needle option for a specific diagnosis. Clinics. 2014;69:335–40.

    Article  Google Scholar 

  4. Hiraki T, et al. Ct fluoroscopy-guided biopsy of 1,000 pulmonary lesions performed with 20-gauge coaxial cutting needles: diagnostic yield and risk factors for diagnostic failure. Chest. 2009;136:1612–7.

    Article  Google Scholar 

  5. Paulson EK, Sheafor DH, Enterline DS, McAdams HP, Yoshizumi TT. Ct fluoroscopy-guided interventional procedures: techniques and radiation dose to radiologists. Radiology. 2001;220:161–7.

    Article  Google Scholar 

  6. Priola A, et al. Accuracy of CT-guided transthoracic needle biopsy of lung lesions: factors affecting diagnostic yield. Radiol Med (Torino). 2007;112:1142–59.

    Article  Google Scholar 

  7. Zhang W, et al. Study on automatic ultrasound scanning of lumbar spine and visualization system for path planning in lumbar puncture surgery. Math Biosci Eng. 2023;20:613–23.

    Article  Google Scholar 

  8. Matsui Y, et al. Robotic systems in interventional oncology: a narrative review of the current status. Int J Clin Oncol. 2023;29:1–8.

    Google Scholar 

  9. Tao J, Mai S. A method to identify respiratory phase in needle biopsy on thorax using point clouds. In: 4th International conference on biometric engineering and applications; 2021, pp. 61–65.

  10. Nicolau S et al. Peter, S. (ed.) A structured light system to guide percutaneous punctures in interventional radiology. (ed.Peter, S.) Optical and digital image processing, vol. 7000, pp.364–374 (SPIE, 2008).

  11. Silverstein E, Snyder M. Comparative analysis of respiratory motion tracking using microsoft kinect v2 sensor. J Appl Clin Med Phys. 2018;19:193–204.

    Article  Google Scholar 

  12. Besl PJ, McKay ND. Method for registration of 3-d shapes. Sens Fusion IV Control Parad Data Struct. 1992;1611:586–606.

    Article  Google Scholar 

  13. Goffin L, et al. Design and in vivo evaluation of a robotized needle insertion system for small animals. IEEE Trans Biomed Eng. 2013;60:2193–204.

    Article  Google Scholar 

  14. Sayeh S, Wang Respiratory motion tracking for robotic radiosurgery. Treating tumors that move with respiration 2007; pp. 15–29.

  15. Massaroni C, et al. Optoelectronic plethysmography in clinical practice and research: a review. Respiration. 2017;93:339–54.

    Article  Google Scholar 

  16. Segal A, Haehnel D, Thrun S. Generalized-ICP. Robot Sci Syst. 2009;2:435.

    Google Scholar 

  17. Hoisak JD, Pawlicki T. The role of optical surface imaging systems in radiation therapy. Semin Radiat Oncol. 2018;28:185–93.

    Article  Google Scholar 

  18. Rehouma H, Noumeir R, Essouri S, Jouvet P. Advancements in methods and camera-based sensors for the quantification of respiration. Sensors. 2020;20:7252.

    Article  Google Scholar 

  19. Wang Y, et al. Dynamic graph CNN for learning on point clouds. ACM Trans Graph(tog). 2019;38:1–12.

    Google Scholar 

  20. Wang R, Yan J, Yang X Learning combinatorial embedding networks for deep graph matching. In: Proceedings of the IEEE/CVF international conference on computer vision 2019; pp. 3056–3065.

  21. Fu K, Liu S, Luo X, Wang M Robust point cloud registration framework based on deep graph matching. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition 2021; pp. 8893–8902.

  22. Qi CR, Su H, Mo K, Guibas LJ. Pointnet: Deep learning on point sets for 3d classification and segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition 2017; pp. 652–660.

  23. Jonker R, Volgenant A. A shortest augmenting path algorithm for dense and sparse linear assignment problems. Computing. 1987;38:325–40.

    Article  MathSciNet  Google Scholar 

  24. Lien C-W, Vhaduri S. Challenges and opportunities of biometric user authentication in the age of IOT: a survey. ACM Comput Surv 2023.

  25. Hou P, Sun R, Yu S, Sun L. Design of a holistic thoracoabdominal phantom for respiration tracking test in robotic radiosurgery. In: 2018 3rd International conference on advanced robotics and mechatronics (ICARM) 2018; pp. 318–322.

  26. Yew ZJ, Lee GH. Rpm-net: Robust point matching using learned features. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition 2020; pp. 11824–11833.

  27. Wu Z. et al. 3d shapenets: A deep representation for volumetric shapes. In: Proceedings of the IEEE conference on computer vision and pattern recognition 2015; pp. 1912–1920.

  28. Zhou Q-Y, Park J, Koltun V. Open3d: a modern library for 3d data processing. arXiv preprint arXiv:1801.09847 2018.

  29. Zhou Q-Y, Park J, Koltun V. Fast global registration. Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part II 14 2016; pp. 766–782.

  30. Yuan C, Yu X, Luo Z. 3d point cloud matching based on principal component analysis and iterative closest point algorithm. In: 2016 International conference on audio, language and image processing (ICALIP) 2016; pp. 404–408.

Download references

Funding

This work was supported by the Science, Technology and Innovation Commission of Shenzhen Municipality (Grant numbers JCYJ20220818101001003 and JSGG20220831094200001).

Author information

Authors and Affiliations

  1. Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518071, Guangdong, China

    Xiaoyu Wang, Tianbo Liu & Songping Mai

Authors
  1. Xiaoyu Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tianbo Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Songping Mai
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Xiaoyu Wang, Tianbo Liu and Songping Mai. The first draft of the manuscript was written by Xiaoyu Wang and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Songping Mai.

Ethics declarations

Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

Ethical approval

This is a study that only uses simulation model, so no ethical approval is required.

Consent to participate

This study does not include human subjects.

Consent to publish

This study does not include human subjects.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, X., Liu, T. & Mai, S. Respiratory motion tracking of the thoracoabdominal surface based on defect-aware point cloud registration. Biomed. Eng. Lett. (2024). https://doi.org/10.1007/s13534-024-00390-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13534-024-00390-3

Keywords

Search

Navigation