MSCT Lung Perfusion Imaging Based on Multi-stage Registration

  • Helen Hong
  • Jeongjin Lee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3773)


We propose a novel subtraction-based method for visualizing segmental and subsegmental pulmonary embolism. For the registration of a pair of CT angiography, a proper geometrical transformation is found through the following steps: First, point-based rough registration is performed for correcting the gross translational mismatch. The center of inertia (COI), apex and hilar point of each unilateral lung are proposed as the reference point. Second, the initial alignment is refined by iterative surface registration. Third, thin-plate spline warping is used to accurately align inner region of lung parenchyma. Finally, enhanced vessels are visualized by subtracting registered pre-contrast images from post-contrast images. To facilitate visualization of parenchymal enhancement, color-coded mapping and image fusion is used. Our method has been successfully applied to four pairs of CT angiography.


Pulmonary Embolism Compute Tomography Angiography Compute Tomography Perfusion Parenchymal Enhancement Compute Tomography Perfusion Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Schoepf, U.J., Costello, P.: CT angiography for diagnosis of pulmonary embolism: state of the art. Radiology 230, 329–337 (2004)CrossRefGoogle Scholar
  2. 2.
    Patel, S., Kazerooni, E.A., Cascade, P.N.: Pulmonary embolism: optimization of small pulmonary artery visualization at multi-detector row CT. Radiology 227, 455–460 (2003)CrossRefGoogle Scholar
  3. 3.
    Schoepf, U.J., Holzknecht, N., Helmberger, T.K., et al.: Subsegmental pulmonary emboli: improved detection with thin-collimation multi-detector row spiral CT. Radiology 222, 483–490 (2002)CrossRefGoogle Scholar
  4. 4.
    Ko, J.P., Naidich, D.P.: Computer-aided diagnosis and the evaluation of lung disease. Journal of Thoraic Imaging 19(3), 136–155 (2004)CrossRefGoogle Scholar
  5. 5.
    Masutani, Y., MacMahon, H., Doi, K.: Computerized detection of pulmonary embolism in spiral CT angiography based on volumetric image analysis. IEEE Trans. on Medical Imaging 21(12), 1517–1523 (2002)CrossRefGoogle Scholar
  6. 6.
    Zhou, C., Hadjiisk, L.M., Sahiner, B., et al.: Computerized detection of pulmonary embolism in 3D computed tomographic images: vessel tracking and segmentation technique. In: Proc. of SPIE Medical Imaging, vol. 5032, pp. 1613–1620 (2003)Google Scholar
  7. 7.
    Pinchon, E., Novak, C.L., Naidich, D.P.: A novel method for pulmonary emboli visualization from high resolution CT images. In: Proc. of SPIE Medical Imaging, vol. 5061 (2004)Google Scholar
  8. 8.
    Herzog, P., Wildberger, J.E., Niethammer, M., et al.: CT perfusion imaging of the lung in pulmonary embolism. Acad. Radiol. 10, 1132–1146 (2003)CrossRefGoogle Scholar
  9. 9.
    Wildberger, J.E., Schoepf, U.J., Mahnken, A.H., et al.: Approaches to CT perfusion imaging in pulmonary embolism. Roentgenology, 64–73 (2005)Google Scholar
  10. 10.
    Chung, M.J., Goo, J.M., Im, J.G., et al.: CT perfusion image of the lung: value in the detection of pulmonary embolism in a porcine model. Investigative Radiology 39(10), 633–640 (2004)CrossRefGoogle Scholar
  11. 11.
    Yim, Y., Hong, H., Shin, Y.G.: Hybrid lung segmentation in chest CT images for computer-aided diagnosis. In: Proc. of HEALTHCOM 2005 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Helen Hong
    • 1
  • Jeongjin Lee
    • 2
  1. 1.School of Electrical Engineering and Computer Science, BK21: Information TechnologySeoul National University 
  2. 2.School of Electrical Engineering and Computer ScienceSeoul National UniversitySeoulKorea

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