Skip to main content
Log in

BEM-based simulation of lung respiratory deformation for CT-guided biopsy

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

Abstract

Purpose

Accurate and real-time prediction of the lung and lung tumor deformation during respiration are important considerations when performing a peripheral biopsy procedure. However, most existing work focused on offline whole lung simulation using 4D image data, which is not applicable in real-time image-guided biopsy with limited image resources. In this paper, we propose a patient-specific biomechanical model based on the boundary element method (BEM) computed from CT images to estimate the respiration motion of local target lesion region, vessel tree and lung surface for the real-time biopsy guidance.

Methods

This approach applies pre-computation of various BEM parameters to facilitate the requirement for real-time lung motion simulation. The resulting boundary condition at end inspiratory phase is obtained using a nonparametric discrete registration with convex optimization, and the simulation of the internal tissue is achieved by applying a tetrahedron-based interpolation method depend on expert-determined feature points on the vessel tree model. A reference needle is tracked to update the simulated lung motion during biopsy guidance.

Results

We evaluate the model by applying it for respiratory motion estimations of ten patients. The average symmetric surface distance (ASSD) and the mean target registration error (TRE) are employed to evaluate the proposed model. Results reveal that it is possible to predict the lung motion with ASSD of \(1.9\pm 0.8\) mm and a mean TRE of \(2.5\pm 2.1\) mm at largest over the entire respiratory cycle. In the CT-/electromagnetic-guided biopsy experiment, the whole process was assisted by our BEM model and final puncture errors in two studies were 3.1 and 2.0 mm, respectively.

Conclusion

The experiment results reveal that both the accuracy of simulation and real-time performance meet the demands of clinical biopsy guidance.

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

Access this article

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. Kuntz A, Torres LG, Feins RH, Webster RJ, Alterovitz R (2015) Motion planning for a three-stage multilumen transoral lung access system. IEEE Int Conf Intell Robot Syst. doi:10.1109/IROS.2015.7353829

    Google Scholar 

  2. Arenberg D (2009) Electromagnetic navigation guided bronchoscopy. Cancer Imaging 9:89–95. doi:10.1102/1470-7330.2009.0016

    PubMed  PubMed Central  Google Scholar 

  3. Detterbeck FC, Lewis SZ, Diekemper R, Addrizzo-Harris D, Alberts WM (2013) Executive summary: diagnosis and management of lung cancer, 3rd ed: American College of chest physicians evidence-based clinical practice guidelines. Chest J 143:7S–37S. doi:10.1378/chest.12-2377

    Article  CAS  Google Scholar 

  4. Eom J, Xu XG, De S, Shi C (2010) Predictive modeling of lung motion over the entire respiratory cycle using measured pressure-volume data, 4DCT images, and finite-element analysis. Med Phys 37:4389–4400. doi:10.1118/1.3455276

    Article  PubMed  PubMed Central  Google Scholar 

  5. Fichtinger G, Deguet A, Masamune K, Balogh E, Fischer GS, Mathieu H, Taylor RH, Zinreich SJ, Fayad LM (2005) Image overlay guidance for needle insertion in CT scanner. IEEE Trans Biomed Eng 52:1415–1424. doi:10.1109/TBME.2005.851493

    Article  PubMed  Google Scholar 

  6. Hanumara NC, Walsh CJ, Slocum AH, Gupta R, Shepard JA (2007) Human factors design for intuitive operation of a low-cost, image-guided, tele-robotic biopsy assistant. In: Annual international conference of the IEEE engineering in medicine and biology proceedings, pp 1257–1260

  7. Yang W, Sun W, Li Q, Yao Y, Lv T, Zeng J, Liang W, Zhou X, Song Y (2015) Diagnostic accuracy of CT-guided transthoracic needle biopsy for solitary pulmonary nodules. PLoS ONE 10:e0131373. doi:10.1371/journal.pone.0131373

    Article  PubMed  PubMed Central  Google Scholar 

  8. Zhou Y, Thiruvalluvan K, Krzeminski L, Moore WH, Xu Z, Liang Z (2013) CT-guided robotic needle biopsy of lung nodules with respiratory motion—experimental system and preliminary test. Int J Med Robot Comput Assist Surg 9:317–330. doi:10.1002/rcs.1441

    Article  Google Scholar 

  9. Keall PJ, Mageras GS, Balter JM, Emery RS, Forster KM, Jiang SB, Kapatoes JM, Low D a, Murphy MJ, Murray BR, Ramsey CR, Van Herk MB, Vedam SS, Wong JW, Yorke E (2006) The management of respiratory motion in radiation oncology report of AAPM Task Group 76. Med Phys 33:3874–3900. doi:10.1118/1.2349696

    Article  PubMed  Google Scholar 

  10. Yan H, Yin F-F, Zhu G-P, Ajlouni M, Kim JH (2006) The correlation evaluation of a tumor tracking system using multiple external markers. Med Phys 33:4073–4084. doi:10.1118/1.2358830

    Article  PubMed  Google Scholar 

  11. Low DA, Nystrom M, Kalinin E, Parikh P, Dempsey JF, Bradley JD, Mutic S, Wahab SH, Islam T, Christensen G, Politte DG, Whiting BR (2003) A method for the reconstruction of four-dimensional synchronized CT scans acquired during free breathing. Med Phys 30:1254–1263. doi:10.1118/1.1576230

    Article  PubMed  Google Scholar 

  12. Ozhasoglu C, Murphy MJ (2002) Issues in respiratory motion compensation during external-beam radiotherapy. Int J Radiat Oncol Biol Phys 52:1389–1399. doi:10.1016/S0360-3016(01)02789-4

    Article  PubMed  Google Scholar 

  13. Dawson LA, Jaffray DA (2007) Advances in image-guided radiation therapy. J Clin Oncol 25:938–946. doi:10.1200/JCO.2006.09.9515

    Article  PubMed  Google Scholar 

  14. Guerrero T, Sanders K, Castillo E, Zhang Y, Bidaut L, Pan T, Komaki R (2006) Dynamic ventilation imaging from four-dimensional computed tomography. Phys Med Biol 51:777–791. doi:10.1088/0031-9155/51/4/002

    Article  PubMed  Google Scholar 

  15. Vinogradskiy YY, Castillo R, Castillo E, Chandler A, Martel MK, Guerrero T (2012) Use of weekly 4DCT-based ventilation maps to quantify changes in lung function for patients undergoing radiation therapy. Med Phys 39:289–298. doi:10.1118/1.3668056

    Article  PubMed  Google Scholar 

  16. Ding K, Bayouth JE, Buatti JM, Christensen GE, Reinhardt JM (2010) 4DCT-based measurement of changes in pulmonary function following a course of radiation therapy. Med Phys 37:1261–1272. doi:10.1118/1.3312210

    Article  PubMed  PubMed Central  Google Scholar 

  17. Ehrhardt J, Werner R, Säring D, Frenzel T, Lu W, Low D, Handels H (2007) An optical flow based method for improved reconstruction of 4D CT data sets acquired during free breathing. Med Phys 34:711–721. doi:10.1118/1.2431245

    Article  PubMed  Google Scholar 

  18. Werner R, Schmidt-Richberg A, Handels H, Ehrhardt J (2014) Estimation of lung motion fields in 4D CT data by variational non-linear intensity-based registration: a comparison and evaluation study. Phys Med Biol 59:4247–4260. doi:10.1088/0031-9155/59/15/4247

    Article  PubMed  Google Scholar 

  19. Murphy K, Van Ginneken B, Reinhardt JM, Kabus S, Ding K, Deng X, Cao K, Du K, Christensen GE, Garcia V, Vercauteren T, Ayache N, Commowick O, Malandain G, Glocker B, Paragios N, Navab N, Gorbunova V, Sporring J, De Bruijne M, Han X, Heinrich MP, Schnabel JA, Jenkinson M, Lorenz C, Modat M, McClelland JR, Ourselin S, Muenzing SEA, Viergever MA, De Nigris D, Collins DL, Arbel T, Peroni M, Li R, Sharp GC, Schmidt-Richberg A, Ehrhardt J, Werner R, Smeets D, Loeckx D, Song G, Tustison N, Avants B, Gee JC, Staring M, Klein S, Stoel BC, Urschler M, Werlberger M, Vandemeulebroucke J, Rit S, Sarrut D, Pluim JPW (2011) Evaluation of registration methods on thoracic CT: the EMPIRE10 challenge. IEEE Trans Med Imaging 30:1901–1920. doi:10.1109/TMI.2011.2158349

    Article  PubMed  Google Scholar 

  20. Weistrand O, Svensson S (2015) The ANACONDA algorithm for deformable image registration in radiotherapy. Med Phys 42:40–53. doi:10.1118/1.4894702

    Article  PubMed  Google Scholar 

  21. Sundaram TA, Gee JC (2005) Towards a model of lung biomechanics: pulmonary kinematics via registration of serial lung images. Med Image Anal 9:524–537. doi:10.1016/j.media.2005.04.002

    Article  PubMed  Google Scholar 

  22. Fuerst B, Mansi T, Carnis F, Sälzle M, Zhang J, Declerck J, Boettger T, Bayouth J, Navab N, Kamen A (2015) Patient-specific biomechanical model for the prediction of lung motion from 4-D CT images. IEEE Trans Med Imaging 34:599–607. doi:10.1109/TMI.2014.2363611

  23. Werner R, Ehrhardt J, Schmidt R, Handels H (2009) Patient-specific finite element modeling of respiratory lung motion using 4D CT image data. Med Phys 36:1500–1511. doi:10.1118/1.3101820

    Article  PubMed  Google Scholar 

  24. Al-Mayah A, Moseley J, Velec M, Brock K (2008) Effect of friction and material compressibility on deformable modeling of human lung. Biomed Simul Int Symp 5104:98–106. doi:10.1007/978-3-540-70521-5_11

    Article  Google Scholar 

  25. Li M, Castillo E, Zheng X-L, Luo H-Y, Castillo R, Wu Y, Guerrero T (2013) Modeling lung deformation: a combined deformable image registration method with spatially varying Young’s modulus estimates. Med Phys 40:81902. doi:10.1118/1.4812419

    Article  Google Scholar 

  26. Heinrich MP, Papiez BW, Schnabel JA, Handels H (2014) Non-parametric discrete registration with convex optimisation. Biomed Image Regist Springer Int Publ 8545:51–61. doi:10.1007/978-3-319-08554-8_6

    Google Scholar 

  27. Wang B, Tian X, Wang Q, Yang Y, Xie H, Zhang S, Gu L (2015) Pulmonary nodule detection in CT images based on shape constraint CV model. Med Phys 42:1241–1254. doi:10.1118/1.4907961

    Article  PubMed  Google Scholar 

  28. Glocker B, Komodakis N, Tziritas G, Navab N, Paragios N (2008) Dense image registration through MRFs and efficient linear programming. Med Image Anal 12:731–741. doi:10.1016/j.media.2008.03.006

    Article  PubMed  Google Scholar 

  29. James DL, Pai DK (1999) Accurate real time deformable objects. In: Proceedings of 26th annual conference on computer graphics and interactive techniques—SIGGRAPH ’99, pp 65–72. doi:10.1145/311535.311542

  30. Kim J, Choi C, De S, Srinivasan MA (2007) Virtual surgery simulation for medical training using multi-resolution organ models. Int J Med Robot Comput Assist Surg 3:149–158. doi:10.1002/rcs.140

    Article  Google Scholar 

  31. Si H (2015) TetGen, a quality tetrahedral mesh generator. AMC Trans Math Softw 41:11. doi:10.1007/3-540-29090-7_9

    Google Scholar 

  32. Vandemeulebroucke J, Rit S, Kybic J, Clarysse P, Sarrut D (2011) Spatiotemporal motion estimation for respiratory-correlated imaging of the lungs. Med Phys 38:166–178. doi:10.1118/1.3523619

    Article  PubMed  Google Scholar 

  33. Murphy K, van Ginneken B, Klein S, Staring M, de Hoop BJ, Viergever MA, Pluim JPW (2011) Semi-automatic construction of reference standards for evaluation of image registration. Med Image Anal 15:71–84. doi:10.1016/j.media.2010.07.005

    Article  CAS  PubMed  Google Scholar 

  34. Heimann T, Van Ginneken B, Styner MA, Arzhaeva Y, Aurich V, Bauer C, Beck A, Becker C, Beichel R, Bekes G, Bello F, Binnig G, Bischof H, Bornik A, Cashman PMM, Chi Y, Córdova A, Dawant BM, Fidrich M, Furst JD, Furukawa D, Grenacher L, Hornegger J, Kainmüller D, Kitney RI, Kobatake H, Lamecker H, Lange T, Lee J, Lennon B, Li R, Li S, Meinzer HP, Németh G, Raicu DS, Rau AM, Van Rikxoort EM, Rousson M, Ruskó L, Saddi KA, Schmidt G, Seghers D, Shimizu A, Slagmolen P, Sorantin E, Soza G, Susomboon R, Waite JM, Wimmer A, Wolf I (2009) Comparison and evaluation of methods for liver segmentation from CT datasets. IEEE Trans Med Imaging 28:1251–1265. doi:10.1109/TMI.2009.2013851

    Article  PubMed  Google Scholar 

  35. Fortmeier D, Wilms M, Mastmeyer A, Handels H (2015) Direct visuo-haptic 4D volume rendering using respiratory motion models. IEEE Trans Haptics 8:371–383. doi:10.1109/TOH.2015.2445768

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

This research is partially supported by the National Key research and development program (2016YFC0106200), 863 national research fund (2015AA043203), the Chinese NSFC research fund (61190120, 61190124 and 61271318) as well as Fujian Provincial Department of Science and Technology (2016Y0069). The authors thank the Léon Bérard Cancer Center & CREATIS laboratory for providing the 4DCT data.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Lixu Gu.

Ethics declarations

Conflict of interest

The authors declares that they have no conflict of interest.

Ethical standard

All procedures performed in studies involving human participants were in accordance with the 8 ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, D., Chen, W., Huang, L. et al. BEM-based simulation of lung respiratory deformation for CT-guided biopsy. Int J CARS 12, 1585–1597 (2017). https://doi.org/10.1007/s11548-017-1603-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-017-1603-8

Keywords

Navigation