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

  • René Werner
  • Jan Ehrhardt
  • Alexander Schmidt-Richberg
  • Anabell Heiß
  • Heinz Handels
Original Article

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.

Keywords

Non-linear registration 4D CT Respiratory motion Motion analysis 

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References

  1. 1.
    Brown H, Prescott R (2006) Applied mixed models in medicine 2nd edn. Wiley, LondonGoogle Scholar
  2. 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 CrossRefPubMedGoogle Scholar
  3. 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): 711–721CrossRefPubMedGoogle Scholar
  4. 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): 85–90CrossRefGoogle Scholar
  5. 5.
    Ernst F, Schweikard A (2009) Forecasting respiratory motion with accurate online support vector regression (SVRpred). Int J Comput Assist Radiol Surg 4(5): 439–447CrossRefPubMedGoogle Scholar
  6. 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. 7.
    Handels H, Ehrhardt J (2009) Medical image computing for computer-supported diagnostics and therapy. Advances and perspectives. Methods Inf Med 48(1): 11–17PubMedGoogle Scholar
  8. 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 CrossRefPubMedGoogle Scholar
  9. 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 MeasurementsGoogle Scholar
  10. 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): 3874–3900CrossRefPubMedGoogle Scholar
  11. 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– 354CrossRefPubMedGoogle Scholar
  12. 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): 867–870CrossRefPubMedGoogle Scholar
  13. 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 CrossRefPubMedGoogle Scholar
  14. 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): 1254–1263CrossRefPubMedGoogle Scholar
  15. 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): 890–901CrossRefPubMedGoogle Scholar
  16. 16.
    Macklem PT, Eidelman D (1990) Reexamination of the elastic properties of emphysematous lungs. Respiration 57(3): 187–192CrossRefPubMedGoogle Scholar
  17. 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. 18.
    Modersitzki J (2003) Numerical methods for image registration. Oxford University Press, OxfordCrossRefGoogle Scholar
  19. 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 CrossRefPubMedGoogle Scholar
  20. 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. 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: 763–774CrossRefPubMedGoogle Scholar
  22. 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): 605–617CrossRefPubMedGoogle Scholar
  23. 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): 1636–1648CrossRefPubMedGoogle Scholar
  24. 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 PubMedGoogle Scholar
  25. 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): 62–68CrossRefPubMedGoogle Scholar
  26. 26.
    Thirion JP (1998) Image matching as a diffusion process: an analogy with Maxwell’s demons. Med Image Anal 2(3): 243–260CrossRefPubMedGoogle Scholar
  27. 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. 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 CrossRefPubMedGoogle Scholar
  29. 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 2P1–10Google Scholar
  30. 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 PubMedGoogle Scholar
  31. 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): 1500–1511CrossRefPubMedGoogle Scholar
  32. 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 imagingGoogle Scholar
  33. 33.
    Xu Q, Hamilton RJ (2006) A novel respiratory detection method based on automated analysis of ultrasound diaphragm video. Med Phys 33(4): 916–921CrossRefPubMedGoogle Scholar
  34. 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 CrossRefPubMedGoogle Scholar

Copyright information

© CARS 2010

Authors and Affiliations

  • René Werner
    • 1
  • Jan Ehrhardt
    • 1
  • Alexander Schmidt-Richberg
    • 1
  • Anabell Heiß
    • 2
  • Heinz Handels
    • 1
  1. 1.Department of Medical InformaticsUniversity Medical Center Hamburg-EppendorfHamburgGermany
  2. 2.Faculty of EngineeringWestcoast University of Applied SciencesHeideGermany

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