Reconstruction of 4D CTA Brain Perfusion Images Using Transformation Methods

  • Iveta Bryjova
  • Jan Kubicek
  • Michal Dembowski
  • Michal Kodaj
  • Marek PenhakerEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 423)


The CT angiography (CTA) method is a mini-invasive diagnostic method for displaying blood vessels using computer tomography (CT) and the concurrent application of a contrast agent (CA). This article focuses on assessing brain perfusion in time based on a 4D reconstruction using one of the image transformation methods—morphing. The proposed methodology is very important for clinical practise. On the base this approach we are able to perform reconstruction of 4D CTA brain perfusion without using contrast substance. It is main difference against conventional procedures which are used during the examination. Patient is not exposed by contrast substance.


CT angiography (CTA) Computer tomography (CT) Contrast agent (CA) Post-processing Morphing Brain perfusion Image segmentation 



The work and the contributions were supported by the project SP2015/179 ‘Biomedicínské inženýrské systémy XI’ and This work is partially supported by the Science and Research Fund 2014 of the Moravia-Silesian Region, Czech Republic and this paper has been elaborated in the framework of the project “Support research and development in the Moravian-Silesian Region 2014 DT 1—Research Teams” (RRC/07/2014). Financed from the budget of the Moravian-Silesian Region.


  1. 1.
    Davis, B., Royalty, K., Kowarschik, M., Rohkohl, C., Oberstar, E., Aagaard-Kienitz, B., Niemann, D., Ozkan, O., Strother, C., MISTRETTA, C.: 4D digital subtraction angiography: implementation and demonstration of feasibility. Am. J. Neuroradiol. 34(10), 1914–1921 (2013). doi: 10.3174/ajnr.A3529
  2. 2.
    Penhaker, M., Matejka, V.: Image registration in neurology applications. In: International Conference on Networking and Information Technology (ICNIT), pp. 550–553 (2010)Google Scholar
  3. 3.
    Kasik, V., Penhaker, M., Novak, V., Pustkova, R., Kutalek, F.: Bio-inspired genetic algorithms on FPGA evolvable hardware. In: 4th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2012. LNAI, vol. 7197, pp. 439–447, Kaohsiung (2012)Google Scholar
  4. 4.
    Baka, N., Metz, C.T., Schultz, C., Neefjes, L., Van Geuns, R.J., Lelieveldt, B.P.F., Niessen, W.J., Van Walsum, T., De Bruijne, M.: Statistical coronary motion models for 2D t/3D registration of X-ray coronary angiography and CTA. In: Medical Image Analysis, vol. 17(6), pp. 698–709 (2013)Google Scholar
  5. 5.
    Scardapane, A., Stabile Ianora, A., Sabbà, C., Moschetta, M., Suppressa, P., Castorani, L., Angelelli, G.: Dynamic 4D MR angiography versus multislice CT angiography in the evaluation of vascular hepatic involvement in hereditary haemorrhagic telangiectasia. Radiol. Med. 117(1), 29–45 (2012). doi: 10.1007/s11547-011-0688-3
  6. 6.
    Ferda, J.: CT angiografie. 1. vyd. Praha: Galén, 2004, xi, 408 s. ISBN 80–726-2281-1Google Scholar
  7. 7.
  8. 8.
    Willems, P.W.A., Brouwer, P.A., Barfett, J.J., Terbrugge, K.G., KRINGS, T.: Detection and classification of cranial dural arteriovenous fistulas using 4D-CT angiography: initial experience. Am. J. Neuroradiol. doi: 10.3174/ajnr.A2248
  9. 9.
    Kubicek, J., Penhaker, M., Pavelova, K., Selamat, A., Hudak, R., Majernik, J.: Segmentation of MRI data to extract the blood vessels based on fuzzy thresholding. In: New Trends in Intelligent Information and Database Systems, pp. 43–52. Springer International Publishing (2015)Google Scholar
  10. 10.
    Frölich, A.M.J., Wolff, S.L., Psychogios, M.N., Klotz, E., Schramm, R., Waser, K., Knauth, M., Schramm, P.: Time-resolved assessment of collateral flow using 4D CT angiography in large-vessel occlusion stroke. Eur. Radiol. 24(2), 390–396 (2014). doi: 10.1007/s00330-013-3024-6
  11. 11.
    Yamaguchi, S., Takeda, M., Mitsuhara, T., Kajihara, S., Mukada, K., Eguchi, K., Kajihara, Y., Takemoto, K., Sugiyama, K., Kurisu, K.: Application of 4D-CTA using 320-row area detector computed tomography on spinal arteriovenous fistulae: initial experience. Neurosurg. Rev. 36(2), 289–296 (2013). doi: 10.1007/s10143-012-0440-z
  12. 12.
    Pustkova, R., Kutalek, F., Penhaker, M., Novak, V.: Measurement and calculation of cerebrospinal fluid in proportion to the skull. In: 9th Roedunet International Conference (RoEduNet), pp. 95–99 (2010)Google Scholar
  13. 13.
    Mendrik, A., Vonken, E., Van Ginneken, B., Smit, E., Annet Waaijer, A., Bertolini, G., Viergever, M.A., Prokop, M.: Automatic segmentation of intracranial arteries and veins in four-dimensional cerebral CT perfusion scans. Med. Phys. 37(6), 2956–2966 (2010). doi: 10.1118/1.3397813
  14. 14.
    Kubicek, J., Penhaker, M.: Fuzzy algorithm for segmentation of images in extraction of objects from MRI. In: International Conference on Advances in Computing, Communications and Informatics (ICACCI, 2014), pp. 1422–1427Google Scholar
  15. 15.
    Kubicek, J., Penhaker, M., Bryjova, I., Kodaj, M.: Articular cartilage defect detection based on image segmentation with colour mapping. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8733, pp. 214–222. Springer, Berlin (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Iveta Bryjova
    • 1
  • Jan Kubicek
    • 1
  • Michal Dembowski
    • 1
  • Michal Kodaj
    • 2
  • Marek Penhaker
    • 1
    Email author
  1. 1.The Department of Cybernetics and Biomedical Engineering, FEIVSB-TU OstravaOstrava-PorubaThe Czech Republic
  2. 2.Podlesí HospitalTřinecThe Czech Republic

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