Local motion correction for lung tumours in PET/CT—first results

  • Ralph A. BundschuhEmail author
  • Axel Martínez-Möller
  • Markus Essler
  • Stephan G. Nekolla
  • Sibylle I. Ziegler
  • Markus Schwaiger
Original Article



Respiratory motion of lung lesions is a limiting factor of quantification of positron emission tomography (PET) data. As some important applications of PET such as therapy monitoring and radiation therapy treatment planning require precise quantification, it is necessary to correct PET data for motion artefacts.


The method is based on list-mode data. First, the motion of the lesion was detected by a centre of mass approach. In the second step, data were sorted corresponding to the breathing state. A volume of interest (VOI) around the lesion was defined manually, and the motion of the lesion in this VOI was measured with reference to the end-expiration image. Then, all voxels in the VOI were shifted according to the measured lesion motion. After optimisation of parameters and verification of the method using a computer-controlled motion phantom, it was applied to nine patients with solitary lesions of the lung.


Fifty percent difference in measured lesion volume and 26% in mean activity concentration were found comparing PET data before and after applying the correction algorithm when simulating a motion amplitude of 28 mm in phantom studies. For patients, maximum changes of 27% in volume and 13% in mean standardised uptake values (SUV) were found.


As respiratory motion is affecting quantification of PET images, correction algorithms are essential for applications that require precise quantification. We described a method which improves the quantification of moving lesions by a local motion correction using list-mode data without increasing acquisition time or reduced signal-to-noise ratio of the images.


Tumour targeting Localisation Dual-modality PET/CT Image processing Lung cancer PET 



We acknowledge the excellent technical assistance of Brigitte Dzewas, Helga Fernolendt, Coletta Kruschke and Anna Winter from the PET/CT staff. The authors are grateful to Marianne Angelberger for editorial assistance.


  1. 1.
    Bradley JD, Perez CA, Dehdashti F, Siegel BA. Implementing biologic target volumes in radiation treatment planning for non-small-cell lung cancer. J Nucl Med 2004;45:96S–101S.PubMedGoogle Scholar
  2. 2.
    Baardwijk Av, Baumert BG, Bosmans G, Kroonenburgh Mv, Stroobants S, Gregoire V, et al. The current status of FDG-PET in tumour volume definition in radiotherapy treatment planning. Cancer Treat Rev 2006;32:245–60.PubMedCrossRefGoogle Scholar
  3. 3.
    Hoekstra CJ, Hoekstra OS, Stroobants SG, Vansteenkiste J, Nuyts J, Smit EF, et al. Methods to monitor response to chemotherapy in non-small-cell lung cancer with 18F-FDG PET. J Nucl Med 2002;43:1304–09.PubMedGoogle Scholar
  4. 4.
    Schwarz JD, Bader M, Jenicke L, Hemminger G, Jänicke F, Avril N. Early prediction of response to chemotherapy in metastatic breast cancer using sequential 18F-FDG PET. J Nucl Med 2005;46:1144–50.Google Scholar
  5. 5.
    Ott K, Weber WA, Lordick F, Becker K, Busch R, Herrmann K, et al. Metabolic imaging predicts response, survival, and recurrence in adenocarcinomas of the esophagogastric junction. J Clin Oncol 2006;24:4692–8.PubMedCrossRefGoogle Scholar
  6. 6.
    Nestle U, Kremp S, Schaefer-Schuler A, Sebastian-Welsch C, Hellwig D, Rübe C, et al. Comparison of different methods for delineation of 18F-FDG PET-positive tissue for target volume definition in radiotherapy of patients with non-small cell lung cancer. J Nucl Med 2005;46:1342–48.PubMedGoogle Scholar
  7. 7.
    Goerres GW, Kamel E, Seifert B, Burger C, Buck A, Hany TF, et al. Accuracy of image coregistration of pulmonary lesions in patients with non-small cell lung cancer using an integrated PET/CT system. J Nucl Med 2002;43:1469–75.PubMedGoogle Scholar
  8. 8.
    Ashamalla H, Rafla S, Parikh K, Mokhtar B, Goswami G, Kambam S, et al. The contribution of integrated PET/CT to the evolving definition of treatment volumes in radiation treatment planning in lung cancer. Int J Radiat Oncol Biol Phys 2005;63:1016–23.PubMedGoogle Scholar
  9. 9.
    Plathow C, Ley S, Fink C, Puderbach M, Hosch W, Schmähl A, et al. Analysis of intrathoracic tumor mobility during whole breathing cycle by dynamic MRI. Int J Radiat Oncol Biol Phys 2004;59:952–59.PubMedCrossRefGoogle Scholar
  10. 10.
    Nehmeh SA, Erdi YE, Ling CC, Rosenzweig KE, Schoder H, Larson SM, et al. Effects of respiratory gating on quantifying PET images of lung cancer. J Nucl Med 2002;43:876–81.PubMedGoogle Scholar
  11. 11.
    Nehmeh SA, Erdi YE, Ling CC, Rosenzweig KE, Squire OD, Braban LE, et al. Effect of respiratory gating on reducing lung motion artifacts in PET imaging of lung cancer. Med Phys 2002;29:366–71.PubMedCrossRefGoogle Scholar
  12. 12.
    Visvikis D, Barret O, Fryer TD, Lamare F, Turzo A, Biazis Y, et al. Evaluation of respiratory motion effects in comparison with other parameters affecting PET image quality. IEEE Nuclear Science Symposium; 2004.Google Scholar
  13. 13.
    Martinez-Moller A, Zikic D, Botnar RM, Bundschuh RA, Howe W, Ziegler SI, et al. Dual cardiac-respiratory gated PET: implementation and results from a feasibility study. Eur J Nucl Med Mol Imaging 2007;34:1447–54.PubMedCrossRefGoogle Scholar
  14. 14.
    Detorie NC, Kesner AL, Solberg TD, Dahlbom M. Evaluation of image noise in respiratory gated PET. IEEE Trans Nucl Sci 2007;54:66–70.CrossRefGoogle Scholar
  15. 15.
    Hutton BF, Kyme AZ, Lau YH, Skerrett DW, Fulton RR. Hybrid 3-D reconstruction/registration algorithm for correction of head motion in emission tomography. IEEE Trans Nucl Sci 2002;49:188–94.CrossRefGoogle Scholar
  16. 16.
    Livieratos L, Stegger L, Bloomfield PM, Schafers K, Bailey DL, Camici PG. Rigid-body transformation of list-mode projection data for respiratory motion correction in cardiac PET. Phys Med Biol 2005;50:3313–22.PubMedCrossRefGoogle Scholar
  17. 17.
    Schäfers KP, Dawood M, Lang N, Büther F, Schäfers M, Schober O. Motion correction in PET/CT. Nuklearmedizin 2005;5a:S46–S50.Google Scholar
  18. 18.
    Lamare F, Cresson T, Savean J, Cheze Le Rest C, Reader AJ, Visvikis D. Respiratory motion correction for PET oncology applications using affine transformation of list mode data. Phys Med Biol 2007;52:121–40.PubMedCrossRefGoogle Scholar
  19. 19.
    Martinez MJ, Bercier Y, Schwaiger M, Ziegler SI. PET/CT biograph sensation 16: performance improvement using faster electronics. Nuklearmedizin 2006;3:126–33.Google Scholar
  20. 20.
    Bundschuh RA, Martinez-Moeller A, Essler M, Martinez MJ, Nekolla SG, Ziegler SI, et al. Postacquisition detection of tumor motion in the lung and upper abdomen using list-mode PET data: a feasibility study. J Nucl Med 2007;48:758–63.PubMedCrossRefGoogle Scholar
  21. 21.
    Erdi YE, Mawlawi O, Larson SM, Imbriaco M, Yeung H, Finn R, et al. Segmentation of lung lesion volume by adaptive positron emission tomography image thresholding. Cancer 1997;80:2505–9.PubMedCrossRefGoogle Scholar
  22. 22.
    Martinez-Möller A, Souvatzoglou M, Navab N, Schwaiger M, Nekolla SG. Artifacts from misaligned CT in cardiac perfusion PET/CT studies: frequency, effects, and potential solutions. J Nucl Med 2007;48:188–93.PubMedGoogle Scholar

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  • Ralph A. Bundschuh
    • 1
    Email author
  • Axel Martínez-Möller
    • 1
  • Markus Essler
    • 1
  • Stephan G. Nekolla
    • 1
  • Sibylle I. Ziegler
    • 1
  • Markus Schwaiger
    • 1
  1. 1.Nuklearmedizinische Klinik und PoliklinikKlinikum rechts der Isar der Technischen Universität MünchenMunichGermany

Personalised recommendations