Skip to main content
Log in

Estimation of motion fields by non-linear registration for local lung motion analysis in 4D CT image data

International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Motivated by radiotherapy of lung cancer non- linear registration is applied to estimate 3D motion fields for local lung motion analysis in thoracic 4D CT images. Reliability of analysis results depends on the registration accuracy. Therefore, our study consists of two parts: optimization and evaluation of a non-linear registration scheme for motion field estimation, followed by a registration-based analysis of lung motion patterns.

Methods

The study is based on 4D CT data of 17 patients. Different distance measures and force terms for thoracic CT registration are implemented and compared: sum of squared differences versus a force term related to Thirion’s demons registration; masked versus unmasked force computation. The most accurate approach is applied to local lung motion analysis.

Results

Masked Thirion forces outperform the other force terms. The mean target registration error is 1.3 ± 0.2 mm, which is in the order of voxel size. Based on resulting motion fields and inter-patient normalization of inner lung coordinates and breathing depths a non-linear dependency between inner lung position and corresponding strength of motion is identified. The dependency is observed for all patients without or with only small tumors.

Conclusions

Quantitative evaluation of the estimated motion fields indicates high spatial registration accuracy. It allows for reliable registration-based local lung motion analysis. The large amount of information encoded in the motion fields makes it possible to draw detailed conclusions, e.g., to identify the dependency of inner lung localization and motion. Our examinations illustrate the potential of registration-based motion analysis.

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

References

  1. Brown H, Prescott R (2006) Applied mixed models in medicine 2nd edn. Wiley, London

    Google Scholar 

  2. Castillo R, Castillo E, Guerra R, Johnson VE, McPhail T, Garg AK, Guerrero T (2009) A framework for evaluation of deformable image registration spatial accuracy using large landmark point sets. Phys Med Biol 54(7): 1849–1870. doi:10.1088/0031-9155/54/7/001

    Article  PubMed  Google Scholar 

  3. 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(2): 711–721

    Article  PubMed  Google Scholar 

  4. Ernst F, Schweikard A (2008) Predicting respiratory motion signals for image-guided radiotherapy using multi-step linear methods (MULIN). Int J Comput Assist Radiol Surg 3(1–2): 85–90

    Article  Google Scholar 

  5. Ernst F, Schweikard A (2009) Forecasting respiratory motion with accurate online support vector regression (SVRpred). Int J Comput Assist Radiol Surg 4(5): 439–447

    Article  PubMed  Google Scholar 

  6. Flampouri S, Jiang SB, Sharp GC, Wolfgang J, Patel AA, Choi NC (2006) Estimation of the delivered patient dose in lung IMRT treatment based on deformable registration of 4D-CT data and Monte Carlo simulations. Phys Med Biol 51(11):2763–2779, doi:10.1088/0031-9155/51/11/006, URL http://dx.doi.org/10.1088/0031-9155/51/11/006

  7. Handels H, Ehrhardt J (2009) Medical image computing for computer-supported diagnostics and therapy. Advances and perspectives. Methods Inf Med 48(1): 11–17

    CAS  PubMed  Google Scholar 

  8. Handels H, Werner R, Schmidt R, Frenzel T, Lu W, Low D, Ehrhardt J (2007) 4D medical image computing and visualization of lung tumor mobility in spatio-temporal CT image data. Int J Med Inform 76(Suppl 3): S433–S439. doi:10.1016/j.ijmedinf.2007.05.003

    Article  PubMed  Google Scholar 

  9. ICRU62 (1999) Prescribing, recording, and reporting photon beam therapy (supplement to ICRU report 50). No. 62 in ICRU report, Bethesda, Md., International Commission on Radiation Units and Measurements

  10. Keall PJ, Mageras GS, Balter JM, Emery RS, Forster KM, Jiang SB, Kapatoes JM, Low DA, Murphy MJ, Murray BR, Ramsey CR, Herk MBV, Vedam SS, Wong JW, Yorke E (2006) The management of respiratory motion in radiation oncology report of AAPM task group 76. Med Phys 33(10): 3874–3900

    Article  PubMed  Google Scholar 

  11. Sornsen de Koste JR, Lagerwaard FJ, Nijssen-Visser MRJ, Graveland WJ, Senan S (2003) Tumor location cannot predict the mobility of lung tumors: a 3D analysis of data generated from multiple CTscans. Int J Radiat Oncol Biol Phys 56(2): 348– 354

    Article  PubMed  Google Scholar 

  12. Li XA, Keall PJ, Orton CG (2007) Point/counterpoint. Respiratory gating for radiation therapy is not ready for prime time. Med Phys 34(3): 867–870

    Article  PubMed  Google Scholar 

  13. Liu HH, Balter P, Tutt T, Choi B, Zhang J, Wang C, Chi M, Luo D, Pan T, Hunjan S, Starkschall G, Rosen I, Prado K, Liao Z, Chang J, Komaki R, Cox JD, Mohan R, Dong L (2007) Assessing respiration-induced tumor motion and internal target volume using four-dimensional computed tomography for radiotherapy of lung cancer. Int J Radiat Oncol Biol Phys 68(2): 531–540. doi:10.1016/j.ijrobp.2006.12.066

    Article  PubMed  Google Scholar 

  14. 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(6): 1254–1263

    Article  PubMed  Google Scholar 

  15. Lu W, Parikh PJ, Naqa IME, Nystrom MM, Hubenschmidt JP, Wahab SH, Mutic S, Singh AK, Christensen GE, Bradley JD, Low DA (2005) Quantitation of the reconstruction quality of a four-dimensional computed tomography process for lung cancer patients. Med Phys 32(4): 890–901

    Article  PubMed  Google Scholar 

  16. Macklem PT, Eidelman D (1990) Reexamination of the elastic properties of emphysematous lungs. Respiration 57(3): 187–192

    Article  CAS  PubMed  Google Scholar 

  17. Mexner V, Wolthaus JWH, van Herk M, Damen EMF, Sonke JJ (2009) Effects of respiration-induced density variations on dose distributions in radiotherapy of lung cancer. Int J Radiat Oncol Biol Phys 74(4):1266–1275, doi:10.1016/j.ijrobp.2009.02.073, http://dx.doi.org/10.1016/j.ijrobp.2009.02.073

    Google Scholar 

  18. Modersitzki J (2003) Numerical methods for image registration. Oxford University Press, Oxford

    Book  Google Scholar 

  19. Onimaru R, Shirato H, Fujino M, Suzuki K, Yamazaki K, Nishimura M, Dosaka-Akita H, Miyasaka K (2005) The effect of tumor location and respiratory function on tumor movement estimated by real-time tracking radiotherapy (RTRT) system. Int J Radiat Oncol Biol Phys 63(1): 164–169. doi:10.1016/j.ijrobp.2005.01.025

    Article  PubMed  Google Scholar 

  20. Plathow C, Fink C, Ley S, Puderbach M, Eichinger M, Zuna I, Schmähl A, Kauczor HU (2004) Measurement of tumor diameter-dependent mobility of lung tumors by dynamic MRI. Radiother Oncol 73(3):349–354, doi:10.1016/j.radonc.2004.07.017, http://dx.doi.org/10.1016/j.radonc.2004.07.017

  21. Reinhardt JM, Christensen GE, Hoffman EA, Ding K, Cao K (2007) Registration-derived estimates of local lung expansion as surrogates for regional ventilation. Inf Process Med Imaging 20: 763–774

    Article  PubMed  Google Scholar 

  22. Sarrut D, Boldea V, Miguet S, Ginestet C (2006) Simulation of four-dimensional CT images from deformable registration between inhale and exhale breath-hold CT scans. Med Phys 33(3): 605–617

    Article  PubMed  Google Scholar 

  23. Sarrut D, Delhay B, Villard PF, Boldea V, Beuve M, Clarysse P (2007) A comparison framework for breathing motion estimation methods from 4-D imaging. IEEE Trans Med Imaging 26(12): 1636–1648

    Article  PubMed  Google Scholar 

  24. Schmidt-Richberg A, Handels H, Ehrhardt J (2009) Integrated segmentation and non-linear registration for organ segmentation and motion field estimation in 4D CT data. Methods Inf Med 48(4): 344–349. doi:10.3414/ME9234

    CAS  PubMed  Google Scholar 

  25. Stevens CW, Munden RF, Forster KM, Kelly JF, Liao Z, Starkschall G, Tucker S, Komaki R (2001) Respiratory-driven lung tumor motion is independent of tumor size, tumor location, and pulmonary function. Int J Radiat Oncol Biol Phys 51(1): 62–68

    Article  CAS  PubMed  Google Scholar 

  26. Thirion JP (1998) Image matching as a diffusion process: an analogy with Maxwell’s demons. Med Image Anal 2(3): 243–260

    Article  CAS  PubMed  Google Scholar 

  27. Urschler M, Kluckner S, Bischof H (2007) A framework for comparison and evaluation of nonlinear intra-subject image registration algorithms. Insight Journal-ISC/NA-MIC workshop on open science at MICCAI http://hdl.handle.net/1926/561

  28. Vercauteren T, Pennec X, Perchant A, Ayache N (2009) Diffeomorphic demons: efficient non-parametric image registration. Neuroimage 45(1 Suppl): S61–S72. doi:10.1016/j.neuroimage.2008.10.040

    Article  PubMed  Google Scholar 

  29. Vik T, Kabus S, von Berg J, Ens K, Dries S, Klinder T, Lorenz C (2008) Validation and comparison of registration methods for free-breathing 4D lung-CT. In: Medical Imaging 2008: Image Processing. Proceedings SPIE, San Diego, USA, vol 6914, pp 2P1–10

  30. Werner R, Ehrhardt J, Frenzel T, Säring D, Lu W, Low D, Handels H (2007) Motion artifact reducing reconstruction of 4D CT image data for the analysis of respiratory dynamics. Methods Inf Med 46(3): 254–260. doi:10.1160/ME9040

    CAS  PubMed  Google Scholar 

  31. 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(5): 1500–1511

    Article  PubMed  Google Scholar 

  32. Werner R, Ehrhardt J, Schmidt-Richberg A, Handels H (2009) Validation and comparison of a biophysical modeling approach and non-linear registration for estimation of lung motion fields in thoracic 4D CT data. In: SPIE 2009, SPIE medical imaging

  33. Xu Q, Hamilton RJ (2006) A novel respiratory detection method based on automated analysis of ultrasound diaphragm video. Med Phys 33(4): 916–921

    Article  PubMed  Google Scholar 

  34. Yamamoto T, Langner U, Loo BW, Shen J, Keall PJ (2008) Retrospective analysis of artifacts in four-dimensional CT images of 50 abdominal and thoracic radiotherapy patients. Int J Radiat Oncol Biol Phys 72(4): 1250–1258. doi:10.1016/j.ijrobp.2008.06.1937

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to René Werner.

Additional information

This work is supported by German Research Foundation (DFG, HA 2355/9-1).

Rights and permissions

Reprints and permissions

About this article

Cite this article

Werner, R., Ehrhardt, J., Schmidt-Richberg, A. et al. Estimation of motion fields by non-linear registration for local lung motion analysis in 4D CT image data. Int J CARS 5, 595–605 (2010). https://doi.org/10.1007/s11548-010-0418-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-010-0418-7

Keywords

Navigation