Registration of CT with PET: A Comparison of Intensity-Based Approaches

  • Gisèle Pereira
  • Inês Domingues
  • Pedro Martins
  • Pedro H. AbreuEmail author
  • Hugo Duarte
  • João Santos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11255)


The integration of functional imaging modality provided by Positron Emission Tomography (PET) and associated anatomical imaging modality provided by Computed Tomography (CT) has become an essential procedure both in the evaluation of different types of malignancy and in radiotherapy planning. The alignment of these two exams is thus of great importance. In this research work, three registration approaches (1) intensity-based registration, (2) rigid translation followed by intensity-based registration and (3) coarse registration followed by fine-tuning were evaluated and compared. To characterize the performance of these methods, 161 real volume scans from patients involved in Hodgkin Lymphoma staging were used: CT volumes used for radiotherapy planning were registered with PET volumes before any treatment. Registration results achieved 78%, 60%, and 91% of accuracy for methods (1), (2) and (3), respectively. Registration methods validation was extended to a corresponding landmarks points distance calculation. Methods (1), (2) and (3) achieved a median improvement registration rate of 66% mm, 51% mm and 70% mm, respectively. The accuracy of the proposed methods was further confirmed by extending our experiments to other multimodal datasets and in a monomodal dataset with different acquisition conditions.


PET CT Registration Cancer Treatment planning 



This article is a result of the project NORTE-01-0145-FEDER-000027, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF).


  1. 1.
    Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008)CrossRefGoogle Scholar
  2. 2.
    Burnet, N., Thomas, S., Burton, K., Jefferies, S.: Defining the tumour and target volumes for radiotherapy. Cancer Imaging 4(2), 153–161 (2004)CrossRefGoogle Scholar
  3. 3.
    Chen, C., Chou, Y., Tagawa, N., Do, Y.: Computer-aided detection and diagnosis in medical imaging. Comput. Math. Methods Med. 2013, 2 p. (2013)Google Scholar
  4. 4.
    Clark, K., et al.: The cancer imaging archive (TCIA): maintaining and operating a public information repository. J. Digit. Imaging 26(6), 1045–1057 (2013)CrossRefGoogle Scholar
  5. 5.
    Domingues, I., Amorim, J., Abreu, P., Duarte, H., Santos, J.: Evaluation of oversampling data balancing techniques in the context of ordinal classification. In: International Joint Conference on Neural Networks (IJCNN) (2018)Google Scholar
  6. 6.
    El-Gamal, F., Elmogy, M., Atwan, A.: Current trends in medical image registration and fusion. Egypt. Inform. J. 17(1), 99–124 (2016)CrossRefGoogle Scholar
  7. 7.
    Jelercic, S., Rajer, M.: The role of PET-CT in radiotherapy planning of solid tumours. Radiol. Oncol. 49(1), 1–9 (2015)CrossRefGoogle Scholar
  8. 8.
    Jin, S., Li, D., Wang, H., Yin, Y.: Registration of PET and CT images based on multiresolution gradient of mutual information demons algorithm for positioning esophageal cancer patients. J. Appl. Clin. Med. Phys. 14(1), 50–61 (2013)CrossRefGoogle Scholar
  9. 9.
    Jung, Y.: Feature driven volume visualization of medical imaging data. Doctor of philosophy, University of Sydney (2015)Google Scholar
  10. 10.
    Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Marinelli, M., Positano, V., Tucci, F., Neglia, D., Landini, L.: Automatic PET-CT image registration method based on mutual information and genetic algorithms. Sci. World J. 2012, 12 p. (2012)Google Scholar
  12. 12.
    Mattes, D., Haynor, D., Vesselle, H., Lewellen, T., Eubank, W.: PET-CT image registration in the chest using free-form deformations. IEEE Trans. Med. Imaging 22(1), 120–128 (2003)CrossRefGoogle Scholar
  13. 13.
    Murphy, K., et al.: Evaluation of registration methods on thoracic CT: the EMPIRE10 challenge. IEEE Trans. Med. Imaging 30(11), 1901–1920 (2011)CrossRefGoogle Scholar
  14. 14.
    Nogueira, M., Abreu, P., Martins, P., Machado, P., Duarte, H., Santos, J.: An artificial neural networks approach for assessment treatment response in oncological patients using PET/CT images. BMC Med. Imaging 17(1), 13 (2017)CrossRefGoogle Scholar
  15. 15.
    Oliveira, F., Tavares, J.: Medical image registration: a review. Comput. Methods Biomech. Biomed. Eng. 17(2), 73–93 (2014)CrossRefGoogle Scholar
  16. 16.
    Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)CrossRefGoogle Scholar
  17. 17.
    Pereira, G.: Deep Learning techniques for the evaluation of response to treatment in Hodgkin Lymphoma. M.Sc. in biomedical engineering, University of Coimbra (2018)Google Scholar
  18. 18.
    Qi, X.S.: Image-guided radiation therapy. In: Maqbool, M. (ed.) An Introduction to Medical Physics. BMPBE, pp. 131–173. Springer, Cham (2017). Scholar
  19. 19.
    Rueckert, D., Sonoda, L., Hayes, C., Hill, D., Leach, M., Hawkes, D.: Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans. Med. Imaging 18(8), 712–721 (1999)CrossRefGoogle Scholar
  20. 20.
    Sheikhbahaei, S., Mena, E., Pattanayak, P., Taghipour, M., Solnes, L., Subramaniam, R.: Molecular imaging and precision medicine: PET/CT and therapy response assessment in oncology. PET Clin. 12(1), 105–118 (2017)CrossRefGoogle Scholar
  21. 21.
    Shekhar, R., et al.: Automated 3-dimensional elastic registration of whole-body PET and CT from separate or combined scanners. J. Nucl. Med. 46(9), 1488–1496 (2005)Google Scholar
  22. 22.
    Siegel, R., Miller, K., Jemal, A.: Cancer statistics, 2017. CA: Cancer J. Clin. 67(1), 7–30 (2017)Google Scholar
  23. 23.
    Suh, J., Kwon, O., Scheinost, D., Sinusas, A., Cline, G., Papademetris, X.: CT-PET weighted image fusion for separately scanned whole body rat. Med. Phys. 39(1), 533–542 (2012)CrossRefGoogle Scholar
  24. 24.
    Thirion, J.: Image matching as a diffusion process: an analogy with Maxwell’s demons. Med. Image Anal. 2(3), 243–260 (1998)CrossRefGoogle Scholar
  25. 25.
    Townsend, D., Carney, J., Yap, J., Hall, N.: PET/CT today and tomorrow. J. Nucl. Med. 45(1), 4–14 (2004)Google Scholar
  26. 26.
    Trajkovii, M., Hedley, M., Trajkovic, M., Hedley, M.: FAST corner detection. Image Vis. Comput. 16(2), 75–87 (1998)CrossRefGoogle Scholar
  27. 27.
    Viergever, M., Maintz, J., Klein, S., Murphy, K., Staring, M., Pluim, J.: A survey of medical image registration-under review. Med. Image Anal. 33, 140–144 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Gisèle Pereira
    • 1
  • Inês Domingues
    • 1
    • 2
  • Pedro Martins
    • 1
  • Pedro H. Abreu
    • 1
    Email author
  • Hugo Duarte
    • 2
  • João Santos
    • 2
  1. 1.CISUC, Department of Informatics EngineeringUniversity of CoimbraCoimbraPortugal
  2. 2.IPO-Porto Research CentrePortoPortugal

Personalised recommendations