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Reconstruction of Coronary Artery Centrelines from X-Ray Angiography Using a Mixture of Student’s t-Distributions

  • Serkan ÇimenEmail author
  • Ali Gooya
  • Nishant Ravikumar
  • Zeike A. Taylor
  • Alejandro F. Frangi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9902)

Abstract

Three-dimensional reconstructions of coronary arteries can overcome some of the limitations of 2D X-ray angiography, namely artery overlap/foreshortening and lack of depth information. Model-based arterial reconstruction algorithms usually rely on 2D coronary artery segmentations and require good robustness to outliers. In this paper, we propose a novel probabilistic method to reconstruct coronary artery centrelines from retrospectively gated X-ray images based on a probabilistic mixture model. Specifically, 3D coronary artery centrelines are described by a mixture of Student’s t-distributions, and the reconstruction is formulated as maximum-likelihood estimation of the mixture model parameters, given the 2D segmentations of arteries from 2D X-ray images. Our method provides robustness against the erroneously segmented parts in the 2D segmentations by taking advantage of the inherent robustness of t-distributions. We validate our reconstruction results using synthetic phantom and clinical X-ray angiography data. The results show that the proposed method can cope with imperfect and noisy segmentation data.

Notes

Acknowledgements

This project was partly supported by the Marie Curie Individual Fellowship (625745, A. Gooya).

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© Springer International Publishing AG 2016

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Authors and Affiliations

  • Serkan Çimen
    • 1
    • 2
    Email author
  • Ali Gooya
    • 1
    • 2
  • Nishant Ravikumar
    • 1
    • 3
  • Zeike A. Taylor
    • 1
    • 3
  • Alejandro F. Frangi
    • 1
    • 2
  1. 1.Center for Computational Imaging and Simulation Technologies in BiomedicineSheffieldUK
  2. 2.Department of Electronic and Electrical EngineeringUniversity of SheffieldSheffieldUK
  3. 3.Department of Mechanical EngineeringUniversity of SheffieldSheffieldUK

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