[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

Augmented reality navigation with ultrasound-assisted point cloud registration for percutaneous ablation of liver tumors

  • Original Article
  • Published:
International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

We present a novel augmented reality (AR) surgical navigation method with ultrasound-assisted point cloud registration for percutaneous ablation of liver tumors. A preliminary study is carried out to verify its feasibility.

Methods

Two three-dimensional (3D) point clouds of the liver surface are derived from the preoperative images and intraoperative tracked US images, respectively. To compensate for the soft tissue deformation, the point cloud registration between the preoperative images and the liver is performed using the non-rigid iterative closest point (ICP) algorithm. A 3D AR device based on integral videography technology is designed to accurately display naked-eye 3D images for surgical navigation. Based on the above registration, naked-eye 3D images of the liver surface, planning path, entry points, and tumor can be overlaid in situ through our 3D AR device. Finally, the AR-guided targeting accuracy is evaluated through entry point positioning.

Results

Experiments on both the liver phantom and in vitro pork liver were conducted. Several entry points on the liver surface were used to evaluate the targeting accuracy. The preliminary validation on the liver phantom showed average entry-point errors (EPEs) of 2.34 ± 0.45 mm, 2.25 ± 0.72 mm, 2.71 ± 0.82 mm, and 2.50 ± 1.11 mm at distinct US point cloud coverage rates of 100%, 75%, 50%, and 25%, respectively. The average EPEs of the deformed pork liver were 4.49 ± 1.88 mm and 5.02 ± 2.03 mm at the coverage rates of 100% and 75%, and the average covered-entry-point errors (CEPEs) were 4.96 ± 2.05 mm and 2.97 ± 1.37 mm at 50% and 25%, respectively.

Conclusion

Experimental outcomes demonstrate that the proposed AR navigation method based on US-assisted point cloud registration has achieved an acceptable targeting accuracy on the liver surface even in the case of liver deformation.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

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
Fig. 9

Similar content being viewed by others

References

  1. Mcdermott S, Rcsi F, Gervais DA (2013) Radiofrequency ablation of liver tumors. Semin Intervent Radiol 30(1):49–55. https://doi.org/10.1055/s-0033-1333653

    Article  PubMed  PubMed Central  Google Scholar 

  2. Prater S, Zayas JO (2021) Percutaneous Radiofrequency Ablation of Liver Tumors. [Updated 2021 May 31]. In: StatPearls [Internet]. Treasure Island (FL). https://www.ncbi.nlm.nih.gov/books/NBK557730/#_NBK557730_pubdet_

  3. Venkatesan AM, Gervais DA, Mueller PR (2006) Percutaneous radiofrequency thermal ablation of primary and metastatic hepatic tumors: current concepts and review of the literature. Semin Intervent Radiol 23:73–84. https://doi.org/10.1055/s-2006-939843

    Article  PubMed  PubMed Central  Google Scholar 

  4. Bilchik AJ, Wood TF, Allegra DP (2001) Radiofrequency ablation of unresectable hepatic malignancies: lessons learned. Oncologist 6:24–33. https://doi.org/10.1634/theoncologist.6-1-24

    Article  CAS  PubMed  Google Scholar 

  5. Goldberg SN, Grassi CJ, Cardella JF, Charboneau JW, Dodd GD, Dupuy DE, Gervais D, Gillams AR, Kane RA, Lee FT, Livraghi JT, McGahan J, Phillips DA, Rhim H, Silverman SG (2009) Image-guided tumor ablation: standardization of terminology and reporting criteria. J Vasc Interv Radiol 20:S377–S390. https://doi.org/10.1016/J.JVIR.2009.04.011

    Article  PubMed  Google Scholar 

  6. Maier-Hein L, Tekbas A, Seitel A, Pianka F, Müller SA, Satzl S, Schawo S, Radeleff B, Tetzlaff R, Franz AM, Müller-Stich BP, Wolf I, Kauczor HU, Schmied BM, Meinzer HP (2008) In vivo accuracy assessment of a needle-based navigation system for CT-guided radiofrequency ablation of the liver. Med Phys 35:5385–5396. https://doi.org/10.1118/1.3002315

    Article  PubMed  Google Scholar 

  7. Fichtinger G, Deguet A, Fischer G, Iordachita I, BaloghJohns E, Masamune K, Taylor RH, Fayad LM, Oliveira MD, Zinreich SJ (2005) Image overlay for CT-guided needle insertions. Comput Aided Surg 10:241–255. https://doi.org/10.3109/10929080500230486

    Article  PubMed  Google Scholar 

  8. Khan MF, Dogan S, Maataoui A, Wesarg S, Gurung J, Ackermann H, Schiemann M, Wimmer-Greinecker G, Vogl TJ (2006) Navigation-based needle puncture of a cadaver using a hybrid tracking navigational system. Invest Radiol 41:713–720. https://doi.org/10.1097/01.rli.0000236910.75905.cc

    Article  PubMed  Google Scholar 

  9. Krücker J, Xu S, Glossop N, Viswanathan A, Borgert J, Schulz H, Wood BJ (2007) Electromagnetic tracking for thermal ablation and biopsy guidance: clinical evaluation of spatial accuracy. J Vasc Interv Radiol 18:1141–1150. https://doi.org/10.1016/j.jvir.2007.06.014

    Article  PubMed  PubMed Central  Google Scholar 

  10. Maier-Hein L, Pianka F, Seitel A, Müller SA, Tekbas A, Seitel M, Wolf I, Schmied BM, Meinzer HP (2007) Precision Targeting of Liver Lesions with a Needle-based Soft Tissue Navigation System. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics) 4792 LNCS:42–49. Doi: https://doi.org/10.1007/978-3-540-75759-7_6

  11. Nicolau SA, Pennec X, Soler L, Ayache N (2007) Clinical Evaluation of a Respiratory Gated Guidance System for Liver Punctures. In: Ayache N, Ourselin S, Maeder A (eds) Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2007. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 77–85. https://link.springer.com/chapter/10.1007%2F978-3-540-75759-7_10

  12. Zhang H, Banovac F, Lin R, Glossop N, Wood BJ, Lindisch D, Levy E, Cleary K (2006) Electromagnetic tracking for abdominal interventions in computer aided surgery. Comput Aided Surg 11:127–136. https://doi.org/10.3109/10929080600751399

    Article  PubMed  PubMed Central  Google Scholar 

  13. Oldrini G, Taste-George H, Renard-Oldrini S, Baumann AS, Marchesi V, Troufléau P, Peiffert D, Didot-Moisei A, Boyer B, Grignonc B, Henrot P (2015) Implantation of fiducial markers in the liver for stereotactic body radiation therapy: feasibility and results. Diagn Interv Imaging 96:589–592. https://doi.org/10.1016/j.diii.2014.01.010

    Article  CAS  PubMed  Google Scholar 

  14. Engstrand J, Toporek G, Harbut P, Jonas E, Nilsson H, Freedman J (2017) Stereotactic CT-guided percutaneous microwave ablation of liver tumors with the use of high-frequency jet ventilation: an accuracy and procedural safety study. Am J Roentgenol 208:193–200. https://doi.org/10.2214/AJR.15.15803

    Article  Google Scholar 

  15. Li J, Deng Z, Shen N, He Z, Feng L, Li Y, Yao J (2021) A fully automatic surgical registration method for percutaneous abdominal puncture surgical navigation. Comput Biol Med 136:104663. https://doi.org/10.1016/j.compbiomed.2021.104663

    Article  PubMed  Google Scholar 

  16. Alam F, Rahman SU, Ullah S, Gulati K (2018) Medical image registration in image guided surgery: issues, challenges and research opportunities. Biocybern Biomed Eng 38:71–89. https://doi.org/10.1016/J.BBE.2017.10.001

    Article  Google Scholar 

  17. Gomes-Fonseca J, Queirós S, Morais P, Pinho ACM, Fonseca JC, Correia-Pinto J, Lima E, Vilaça JL (2019) Surface-based registration between CT and US for image-guided percutaneous renal access – a feasibility study. Med Phys 46:1115–1126. https://doi.org/10.1002/mp.13369

    Article  PubMed  Google Scholar 

  18. Chan A, Parent E, Mahood J, Lou E (2021) 3D ultrasound navigation system for screw insertion in posterior spine surgery: a phantom study. Int J Comput Assist Radiol Surg. https://doi.org/10.1007/s11548-021-02516-9

    Article  PubMed  PubMed Central  Google Scholar 

  19. Amberg B, Romdhani S, Vetter T (2007) Optimal step nonrigid ICP algorithms for surface registration. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. https://doi.org/10.1109/CVPR.2007.383165

    Article  Google Scholar 

  20. Ma L, Zhao Z, Chen F, Zhang B, Fu L, Liao H (2017) Augmented reality surgical navigation with ultrasound-assisted registration for pedicle screw placement: a pilot study. Int J CARS 12:2205–2215. https://doi.org/10.1007/s11548-017-1652-z

    Article  Google Scholar 

  21. Arun KS, Huang TS, Blostein SD (1987) Least-squares fitting of two 3-D point sets. IEEE Trans Pattern Anal Mach Intell PAMI 9:698–700. https://doi.org/10.1109/TPAMI.1987.4767965

    Article  CAS  Google Scholar 

  22. Besl PJ, McKay ND (1992) A method for registration of 3-D shapes. IEEE Trans Pattern Anal Mach Intell 14:239–256. https://doi.org/10.1109/34.121791

    Article  Google Scholar 

  23. Pieper S, Halle M, Kikinis R (2044) 3D Slicer. IEEE Int Symp Biomed Imaging Macro to Nano (IEEE Cat No 04EX821) 2:632–635. Doi: https://doi.org/10.1109/ISBI.2004.1398617

  24. Vezhnevets V, Konouchine V (2005) “GrowCut”-Interactive Multi-Label N-D Image Segmentation by Cellular Automata

  25. Park J, Park S, Kim J, Yoon S, Song S, Liu Z, Song B, Kauweloa K, Webster M, Sandhu A, Mell L, Jiang S, Mundt A, Song W (2012) Liver motion during cone beam computed tomography guided stereotactic body radiation therapy. Med Phys 39(10):6431–6442. https://doi.org/10.1118/1.4754658

    Article  PubMed  Google Scholar 

  26. Paulsson A, Yom S, Anwar M, Pinnaduwage D, Sudhyadhom A, Gottschalk A, Chang A, Descovich M (2017) Respiration-induced intraorgan deformation of the liver: implications for treatment planning in patients treated with fiducial tracking. Technol Cancer Res Treat 16(6):776–782. https://doi.org/10.1177/1533034616687193

    Article  PubMed  PubMed Central  Google Scholar 

  27. Myronenko A, Song X (2010) Point set registration: coherent point drift. IEEE Trans Pattern Anal Mach Intell 32(12):2262–2275. https://doi.org/10.1109/TPAMI.2010.46

    Article  PubMed  Google Scholar 

  28. Cai L, Gao J, Zhao D (2020) A review of the application of deep learning in medical image classification and segmentation. Ann Transl Med 8:713. https://doi.org/10.21037/atm.2020.02.44

    Article  PubMed  PubMed Central  Google Scholar 

  29. Beyer LP, Wiggermann P (2017) Planning and guidance: new tools to enhance the human skills in interventional oncology. Diagn Interv Imaging 98:583–588. https://doi.org/10.1016/J.DIII.2017.07.004

    Article  CAS  PubMed  Google Scholar 

  30. Denys A, Lachenal Y, Duran R, Chollet-Rivier M, Bize P (2014) Use of high-frequency jet ventilation for percutaneous tumor ablation. Cardiovasc Intervent Radiol 37:140–146. https://doi.org/10.1007/s00270-013-0620-4

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

This work was supported in part by National Natural Science Foundation of China (81901844, 82027807, 81930119, 82090052), Beijing Municipal Natural Science Foundation (L192013, 7212202).

Author information

Author notes

  1. Longfei Ma and Hanying Liang have contributed equally to this work.

Authors and Affiliations

  1. Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, 100084, China

    Longfei Ma, Hanying Liang, Boxuan Han, Xinran Zhang & Hongen Liao

  2. Hepato-Pancreato-Biliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, 102218, China

    Shizhong Yang

Authors
  1. Longfei Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hanying Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Boxuan Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shizhong Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xinran Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hongen Liao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hongen Liao.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

This article does not contain patient data.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ma, L., Liang, H., Han, B. et al. Augmented reality navigation with ultrasound-assisted point cloud registration for percutaneous ablation of liver tumors. Int J CARS 17, 1543–1552 (2022). https://doi.org/10.1007/s11548-022-02671-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-022-02671-7

Keywords

Search

Navigation