Advertisement

Fuzzy Statistical Unsupervised Learning Based Total Lesion Metabolic Activity Estimation in Positron Emission Tomography Images

  • Jose George
  • Kathleen Vunckx
  • Sabine Tejpar
  • Christophe M. Deroose
  • Johan Nuyts
  • Dirk Loeckx
  • Paul Suetens
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7009)

Abstract

Accurate tumor lesion activity estimation is critical for tumor staging and follow up studies. Positron emission tomography (PET) successfully images and quantifies the lesion metabolic activity. Recently, PET images were modeled as a fuzzy Gaussian mixture to delineate tumor lesions accurately. Nonetheless, on the course of accurate delineation, chances are high to potentially end up with activity underestimation, due to the limited PET resolution, the reconstruction images suffer from partial volume effects (PVE). In this work, we propose a statistical lesion activity computation (SLAC) approach to robustly estimate the total lesion activity (TLA) directly from the modeled Gaussian partial volume mixtures. To evaluate the proposed method, synthetic lesions were simulated and reconstructed. TLA was estimated from 3 state-of-the-art PET delineation schemes for comparison. All schemes were evaluated with reference to the ground truth knowledge. The experimental results convey that the SLAC is robust enough for clinical use.

Keywords

Positron emission tomography tumor activity estimation finite mixture models Gaussian distribution partial volume modeling linear combination (LC) of random variables 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Weber, W.A., Figlin, R.: Monitoring cancer treatment with PET/CT: Does it make a difference? JNM 48, 36S–44S (2007)Google Scholar
  2. 2.
    Kelloff, G.J., Hoffman, J.M., Johnson, B., et al.: Progress and promise of FDG PET imaging for cancer patient management and oncologic drug development. Clin. Cancer Res. 11, 2785–2808 (2005)CrossRefGoogle Scholar
  3. 3.
    Hatt, M., Visvikis, D., Albarghach, N., Tixier, F., Pradier, O., le Rest, C.C.: Prognostic value of 18F-FDG PET image-based parameters in oesophageal cancer and impact of tumour delineation methodology. EJNMMI 38, 1191–1202 (2011)Google Scholar
  4. 4.
    Wahl, R.L., Jacene, H., Kasamon, Y., Lodge, M.A.: From RECIST to PERCIST: evolving considerations for PET response criteria in solid tumors. JNM 50, 122S–150S (2009)Google Scholar
  5. 5.
    Lucignani, G., Larson, S.M.: Doctor, What does my future hold? The prognostic values of FDG-PET in solid tumours. EJNMMI 37, 1032–1038 (2010)Google Scholar
  6. 6.
    Caillol, H., Hillion, A., Pieczynski, W.: Fuzzy Random Fields and Unsupervised Image Segmentation. IEEE TGRS 31, 801–810 (1993)Google Scholar
  7. 7.
    Caillol, H., Pieczynski, W., Hillion, A.: Estimation of fuzzy Gaussian mixture and unsupervised statistical image segmentation. IEEE TIP 6, 425–440 (1997)Google Scholar
  8. 8.
    Van Leemput, K., Maes, F., Vandermeulen, D., Suetens, P.: A unifying framework for partial volume segmentation of brain MR images. IEEE TMI 22, 105–119 (2003)zbMATHGoogle Scholar
  9. 9.
    Hatt, M., le Rest, C.C., Turzo, A., Roux, C., Visvikis, D.: A fuzzy locally adaptive bayesian segmentation approach for volume determination in PET. IEEE TMI 28, 881–893 (2009)Google Scholar
  10. 10.
    Hatt, M., le Rest, C.C., Descourt, P., Dekker, A., De Ruysscher, D., Oellers, M., Lambin, P., Pradier, O., Visvikis, D.: Accurate automatic delineation of heterogeneous functional volumes in positron emission tomography for oncology applications. Int. J. Radiation Oncology 77, 301–308 (2010)CrossRefGoogle Scholar
  11. 11.
    Figueiredo, M.A.T., Jain, A.K.: Unsupervised Learning of Finite Mixture Models. IEEE PAMI 24, 381–396 (2002)CrossRefGoogle Scholar
  12. 12.
    Geets, X., Lee, J.A., Bol, A., Lonneux, M., Grégoire, V.: A gradient-based method for segmenting FDG-PET images: methodology and validation. EJNMMI 34, 1427–1438 (2007)Google Scholar
  13. 13.
    Van Dalen, J.A., Hoffmann, A.L., Dicken, V., Vogel, W.V., Wiering, B., Ruers, T.J., Karssemeijer, N., Oyen, W.J.G.: A novel iterative method for lesion delineation and volumetric quantification with FDG PET. Nucl. Med. Communications 28, 485–493 (2007)CrossRefGoogle Scholar
  14. 14.
    Segars, W.P.: Development of a new dynamic NURBS-based cardiac-torso (NCAT) phantom. PhD Dissertation, The University of North Carolina (May 2001)Google Scholar
  15. 15.
    Reilhac, A., Lartizien, C., Costes, N., Sans, S., Comtat, C., Gunn, R.N., Evans, A.C.: PET-SORTEO: A monte carlo-based simulator with high count rate capabilities. IEEE Trans. Nucl. Sci. 51, 46–52 (2004)CrossRefGoogle Scholar
  16. 16.
    Hudson, H.M., Larkin, R.S.: Accelerated image reconstruction using ordered subsets of projection data. IEEE TMI 13, 601–609 (1994)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jose George
    • 1
  • Kathleen Vunckx
    • 2
  • Sabine Tejpar
    • 3
  • Christophe M. Deroose
    • 2
  • Johan Nuyts
    • 2
  • Dirk Loeckx
    • 1
  • Paul Suetens
    • 1
    • 4
  1. 1.Center for Processing Speech and Images, Department of Electrical EngineeringKatholieke Universiteit LeuvenBelgium
  2. 2.Department of Nuclear MedicineKatholieke Universiteit LeuvenBelgium
  3. 3.Department of GastroenterologyKatholieke Universiteit LeuvenBelgium
  4. 4.IBBT-K.U.Leuven Future Health DepartmentBelgium

Personalised recommendations