Skip to main content

Impact of 4D image quality on the accuracy of target definition

Abstract

Delineation accuracy of target shape and position depends on the image quality. This study investigates whether the image quality on standard 4D systems has an influence comparable to the overall delineation uncertainty. A moving lung target was imaged using a dynamic thorax phantom on three different 4D computed tomography (CT) systems and a 4D cone beam CT (CBCT) system using pre-defined clinical scanning protocols. Peak-to-peak motion and target volume were registered using rigid registration and automatic delineation, respectively. A spatial distribution of the imaging uncertainty was calculated as the distance deviation between the imaged target and the true target shape. The measured motions were smaller than actual motions. There were volume differences of the imaged target between respiration phases. Imaging uncertainties of >0.4 cm were measured in the motion direction which showed that there was a large distortion of the imaged target shape. Imaging uncertainties of standard 4D systems are of similar size as typical GTV–CTV expansions (0.5–1 cm) and contribute considerably to the target definition uncertainty. Optimising and validating 4D systems is recommended in order to obtain the most optimal imaged target shape.

This is a preview of subscription content, access via your institution.

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

References

  1. Wolthaus JWH, Schneider C, Sonke J, van Herk M, Belderbos JSA, Rossi MMG et al (2006) Mid-ventilation CT scan construction from four-dimensional respiration-correlated CT scans for radiotherapy planning of lung cancer patients. Int J Radiat Oncol Biol Phys 65:1560–1571

    Article  PubMed  Google Scholar 

  2. Vedam SS, Keall PJ, Kini VR, Mostafavi H, Shukla HP, Mohan R (2003) Acquiring a four-dimensional computed tomography dataset using an external respiratory signal. Phys Med Biol 48:45–62

    CAS  Article  PubMed  Google Scholar 

  3. Keall PJ, Starkschall G, Shukla H, Forster KM, Ortiz V, Stevens CW et al (2004) Acquiring 4D thoracic CT scans using a multislice helical method. Phys Med Biol 49:2053–2067

    CAS  Article  PubMed  Google Scholar 

  4. Keall P (2004) 4-dimensional computed tomography imaging and treatment planning. Semin Radiat Oncol 14:81–90

    Article  PubMed  Google Scholar 

  5. Persson GF, Nygaard DE, Brink C, Jahn JW, Rosenschöld PM, Specht L et al (2010) Deviations in delineated GTV caused by artefacts in 4DCT. Radiother Oncol 96:61–66

    Article  PubMed  Google Scholar 

  6. Louie AV, Rodrigues G, Olsthoorn J, Palma D, Yu E, Yaremko B et al (2010) Inter-observer and intra-observer reliability for lung cancer target volume delineation in the 4D-CT era. Radiother Oncol 95:166–171

    Article  PubMed  Google Scholar 

  7. Giraud P, Elles S, Helfre S, De Rycke Y, Servois V, Carette MF et al (2002) Conformal radiotherapy for lung cancer: different delineation of the gross tumor volume (GTV) by radiologists and radiation oncologists. Radiother Oncol 62:27–36

    Article  PubMed  Google Scholar 

  8. Gaede S, Olsthoorn J, Louie AV, Palma D, Yu E, Yaremko B et al (2011) An evaluation of an automated 4D-CT contour propagation tool to define an internal gross tumour volume for lung cancer radiotherapy”. Radiother Oncol 101:322–328

    Article  PubMed  Google Scholar 

  9. Vorwerk H, Beckmann G, Bremer M, Degen M, Dietl B, Fietkau R et al (2009) The delineation of target volumes for radiotherapy of lung cancer patients”. Radiother Oncol 91:455–460

    Article  PubMed  Google Scholar 

  10. Steenbakkers RJHM, Duppen JC, Fitton I, Deurloo KEI, Zijp L, Uitterhoeve ALJ et al (2005) Observer variation in target volume delineation of lung cancer related to radiation oncologist-computer interaction: a’Big Brother’ evaluation. Radiother 77:182–190

    Article  Google Scholar 

  11. Steenbakkers RJHM, Duppen JC, Fitton I, Deurloo KEI, Zijp LJ, Comans EFI et al (2006) Reduction of observer variation using matched CT-PET for lung cancer delineation: a three-dimensional analysis. Int J Radiat Oncol Biol Phys 64:435–448

    Article  PubMed  Google Scholar 

  12. Fitton I, Steenbakkers RJHM, Gilhuijs K, Duppen JC, Nowak PJCM, van Herk M et al (2008) Impact of anatomical location on value of CT-PET co-registration for delineation of lung tumors. Int J Radiat Oncol Biol Phys 70:1403–1407

    Article  PubMed  Google Scholar 

  13. Jensen HR, Hansen O, Hjelm-Hansen M, Brink C (2008) Inter- and intrafractional movement of the tumour in extracranial stereotactic radiotherapy of NSCLC. Acta Oncol 47:1432–1437

    Article  PubMed  Google Scholar 

  14. Gottlieb KL, Hansen CR, Hansen O, Westberg J, Brink C (2010) Investigation of respiration induced intra- and inter-fractional tumour motion using a standard Cone Beam CT. Acta Oncol 49:1192–1198

    Article  PubMed  Google Scholar 

  15. Persson GF, Nygaard DE, Olsen M, Juhler-Nøttrup T, Pedersen AN, Specht L et al (2008) Can audio coached 4D CT emulate free breathing during the treatment course? Acta Oncol 47:1397–1405

    Article  PubMed  Google Scholar 

  16. Ford EC, Mageras GS, Yorke E, Ling CC (2003) Respiration-correlated spiral CT: a method of measuring respiratory-induced anatomic motion for radiation treatment planning. Med Phys 30:88–97

    CAS  Article  PubMed  Google Scholar 

  17. Li R, Lewis JH, Cerviño LI, Jiang SB (2009) 4D CT sorting based on patient internal anatomy. Phys Med Biol 54:4821–4833

    Article  PubMed  Google Scholar 

  18. Korreman SS, Juhler-Nøttrup T, Boyer AL (2008) Respiratory gated beam delivery cannot facilitate margin reduction, unless combined with respiratory correlated image guidance. Radiother Oncol 86:61–68

    Article  PubMed  Google Scholar 

  19. Wink N, Panknin C, Solberg TD (2006) Phase versus amplitude sorting of 4D-CT data. J Appl Clin Med Phys 7:77–85

    Article  PubMed  Google Scholar 

  20. Abdelnour AF, Nehmeh SA, Pan T, Humm JL, Vernon P, Schöder H et al (2007) Phase and amplitude binning for 4D-CT imaging. Phys Med Biol 52:3515–3529

    CAS  Article  PubMed  Google Scholar 

  21. Nakamura M, Narita Y, Sawada A, Matsugi K, Nakata M, Matsuo Y et al (2009) Impact of motion velocity on four-dimensional target volumes: a phantom study. Med Phys 36:1610–1617

    Article  PubMed  Google Scholar 

  22. Nakamura M, Narita Y, Matsuo Y, Narabayashi M, Nakata M, Yano S et al (2008) Geometrical differences in target volumes between slow CT and 4D CT imaging in stereotactic body radiotherapy for lung tumors in the upper and middle lobe. Med Phys 35:4142–4148

    Article  PubMed  Google Scholar 

  23. Rietzel E, Pan T, Chen GTY (2005) Four-dimensional computed tomography: image formation and clinical protocol. Med Phys 32:874–889

    Article  PubMed  Google Scholar 

  24. Sonke J, Zijp L, Remeijer P, van Herk M (2005) Respiratory correlated cone beam CT. Med Phys 32:1176–1186

    Article  PubMed  Google Scholar 

  25. Kaus MR, Pekar V, Lorenz C, Truyen R, Lobregt S, Weese J (2003) Automated 3-D PDM construction from segmented images using deformable models. IEEE Trans Med Imaging 22:1005–1013

    Article  PubMed  Google Scholar 

  26. Remeijer P, Rasch C, Lebesque JV, van Herk M (1999) A general methodology for three-dimensional analysis of variation in target volume delineation. Med Phys 26:931–940

    CAS  Article  PubMed  Google Scholar 

  27. van Herk M (2004) Errors and margins in radiotherapy. Semin Radiat Oncol 14:52–64

    Article  PubMed  Google Scholar 

  28. Nielsen TB, Hansen VN, Westberg J, Hansen O, Brink C (2012) A dual centre study of setup accuracy for thoracic patients based on Cone-Beam CT data. Radiother Oncol 102:281–286

    Article  PubMed  Google Scholar 

  29. Sonke J, Lebesque J, van Herk M (2008) Variability of four-dimensional computed tomography patient models. Int J Radiat Oncol Biol Phys 70:590–598

    Article  PubMed  Google Scholar 

  30. Rietzel E, Liu AK, Chen GTY, Choi NC (2008) Maximum-intensity volumes for fast contouring of lung tumors including respiratory motion in 4DCT planning. Int J Radiat Oncol Biol Phys 71:1245–1252

    Article  PubMed  Google Scholar 

  31. Wang H, Garden AS, Zhang L, Wei X, Ahamad A, Kuban DA et al (2008) Performance evaluation of automatic anatomy segmentation algorithm on repeat or four-dimensional computed tomography images using deformable image registration method. Int J Radiat Oncol Biol Phys 72:210–219

    Article  PubMed  PubMed Central  Google Scholar 

  32. Pevsner A, Davis B, Joshi S, Hertanto A, Mechalakos J, Yorke E et al (2006) Evaluation of an automated deformable image matching method for quantifying lung motion in respiration-correlated CT images. Med Phys 33:369–376

    CAS  Article  PubMed  Google Scholar 

  33. Wijesooriya K, Weiss E, Dill V, Dong L, Mohan R, Joshi S et al (2008) Quantifying the accuracy of automated structure segmentation in 4D CT images using a deformable image registration algorithm. Med Phys 35:1251–1260

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

This work is supported by the Region of Southern Denmark and by CIRRO—The Lundbeck Foundation Center for Interventional Research in Radiation Oncology and by The Danish Council for Strategic Research.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Tine Bjørn Nielsen.

Ethics declarations

Conflicts of interest

None.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Nielsen, T.B., Hansen, C.R., Westberg, J. et al. Impact of 4D image quality on the accuracy of target definition. Australas Phys Eng Sci Med 39, 103–112 (2016). https://doi.org/10.1007/s13246-015-0400-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13246-015-0400-3

Keywords

  • 4D-CT
  • Image artefact
  • 4D-CBCT
  • Target motion
  • Motion management
  • NSCLC