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Digital Subtraction CT Lung Perfusion Image Based on 3D Affine Registration

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Part of the Lecture Notes in Computer Science book series (LNIP,volume 3663)

Abstract

We propose a fast and robust registration technique for accurately imaging lung perfusion and efficiently detecting pulmonary embolism in chest CT angiography. For the registration of a pair of CT scans, a proper geometrical transformation is found through the following steps: First, the initial registration using an optimal cube is performed for correcting the gross translational mismatch. Second, the initial alignment is refined by iterative surface registration. For fast and robust convergence of the distance measure to the optimal value, a 3D distance map is generated by the narrow-band distance propagation. Third, 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 ten patients of pre- and post-contrast images in chest CT angiography. Experimental results show that the performance of our method is very promising compared with conventional method with the aspects of its visual inspection, accuracy and processing time.

Keywords

  • Pulmonary Embolism
  • Compute Tomography Angiography
  • Compute Tomography Perfusion
  • Lung Perfusion
  • Contrast Volume

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.

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  • DOI: 10.1007/11550518_49
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References

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© 2005 Springer-Verlag Berlin Heidelberg

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Hong, H., Lee, J. (2005). Digital Subtraction CT Lung Perfusion Image Based on 3D Affine Registration. In: Kropatsch, W.G., Sablatnig, R., Hanbury, A. (eds) Pattern Recognition. DAGM 2005. Lecture Notes in Computer Science, vol 3663. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550518_49

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  • DOI: https://doi.org/10.1007/11550518_49

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28703-2

  • Online ISBN: 978-3-540-31942-9

  • eBook Packages: Computer ScienceComputer Science (R0)