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Cardiovascular Informatics

  • I. A. KakadiarisEmail author
  • U. Kurkure
  • A. Bandekar
  • S. O’Malley
  • M. Naghavi

Abstract

As cardiac imaging technology advances, large amounts of imaging data are being produced which are not being mined sufficiently by current diagnostic tools for early detection and diagnosis of cardiovascular disease. We aim to develop a computational framework to mine cardiac imaging data and provide quantitative measures for developing a new risk assessment method. In this chapter, we present novel methods to quantify pericardial fat in non-contrast cardiac computed tomography images automatically, and to detect and quantify neovascularization in the coronary vessels using intra-vascular ultrasound imaging.

Keywords

Heart Attack Vulnerable Plaque Image Analysis Method Probabilistic Atlas Frame Gating 
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.

Notes

Acknowledgment

We would like to thank all members of the Ultimate IVUS team for their valuable assistance. This work was supported in part by NSF Grant IIS-0431144 and an NSF Graduate Research Fellowship (SMO). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF.

References

  1. 1.
    A. Bandekar, M. Naghavi, and I. Kakadiaris. Automated pericardial fat quantification in CT data. In Proc. Int. Conf. of the IEEE Engineering in Medicine and Biology Society, pages932–936, New York, NY, 2006.Google Scholar
  2. 2.
    A.N. Bandekar. A Unified Knowledge-based Segmentation Framework for Medical Images. PhD thesis, University of Houston, Dec. 2006.Google Scholar
  3. 3.
    R. Cooperand et al. Trends and disparities in coronary heart disease, stroke, and other cardiovascular diseases in the United States: Findings of the National Conference on Cardiovascular Disease Prevention. Circulation, 102(25):3137–3147, 2000.CrossRefGoogle Scholar
  4. 4.
    D.E. Goertz et al. Subharmonic contrast intravascular ultrasound for vasa vasorum imaging. Ultrasound Med Biol, 33(12):1859–1872, December 2007.CrossRefGoogle Scholar
  5. 5.
    I. Kakadiaris, S. O’Malley, M. Vavuranakis, S. Carlier, R. Metcalfe, C. Hartley, E. Falk, and M. Naghavi. Signal processing approaches to risk assessment in coronary artery disease. IEEE Signal Processing Magazine, 23(6):59–62, 2006.CrossRefGoogle Scholar
  6. 6.
    M. Naghavi et al. From vulnerable plaque to vulnerable patientpart III: Executive summary of the screening for heart attack prevention and education (SHAPE) task force report. The American Journal of Cardiology, 98(2):2–15, July 2006.CrossRefGoogle Scholar
  7. 7.
    M. Naghavi et al. From vulnerable plaque to vulnerable patient: A call for new definitions and risk assessment strategies: Part I. Circulation, 108(14):1664–1672, October 2003. (In Press).Google Scholar
  8. 8.
    M. Naghavi et al. From vulnerable plaque to vulnerable patient: A call for new definitions and risk assessment strategies: Part II. Circulation, 108(15):1772–1778, October 2003.CrossRefGoogle Scholar
  9. 9.
    S. O’Malley, S. Carlier, M. Naghavi, and I. Kakadiaris. Image-based frame gating of IVUS pullbacks: A surrogate for ecg. In Proc. IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing, pages 433–436, Honolulu, Hawaii, 2007.Google Scholar
  10. 10.
    S. O’Malley, M. Naghavi, and I. Kakadiaris. Image-based frame gating for stationary-catheter IVUS sequences. In Proc. Int. Workshop on Computer Vision for Intravascular and Intracardiac Imaging, pages 14–21, Copenhagen, Denmark, 2006.Google Scholar
  11. 11.
    S. O’Malley, M. Naghavi, and I. Kakadiaris. One-class acoustic char-acterization applied to blood detection in ivus. In Proc. Medical Image Computing and Computer-Assisted Intervention, Brisbane, Australia, 2007.Google Scholar
  12. 12.
    S. O’Malley, M. Vavuranakis, M. Naghavi, and I. Kakadiaris. Intravascular ultrasound-based imaging of vasa vasorum for the detection of vulnerable atherosclerotic plaque. In Proc. Int. Conf. on Medical Image Computing and Computer Assisted Intervention, volume 1, pages 343–351, Palm Springs, CA, USA, 2005.Google Scholar
  13. 13.
    A. Pednekar and I.A. Kakadiaris. Image segmentation based on fuzzy connectedness using dynamic weights. IEEE Trans. Image Processing, 15(6):1555–1562, 2006.CrossRefGoogle Scholar
  14. 14.
    A. Pednekar, U. Kurkure, I. A. Kakadiaris, R. Muthupillai, and S. Flamm. Left ventricular segmentation in MR using hierarchical multi-class multi-feature fuzzy connectedness. In Proc. Medical Image Computing and Computer Assisted Intervention, Rennes, Saint-Malo, France, 2004. Springer.Google Scholar
  15. 15.
    D. Rueckert et al. Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans. on Medical Imaging, 18:712–721, 1999.CrossRefGoogle Scholar
  16. 16.
    R. Taguchi et al. Pericardial fat accumulation in men as a risk factor for coronary artery disease. Atherosclerosis, 157(1):203–9, 2001.CrossRefMathSciNetGoogle Scholar
  17. 17.
    M. Vavuranakis, I. Kakadiaris, S. O’Malley, T. Papaioannou, E. Sanidas, S. Carlier, M. Naghavi, and C. Stefanadis. Contrast enhanced intravascular ultrasound for the detection of vulnerable plaques: a combined morphology and activity-based assessment of plaque vulnerability. Expert Review of Cardiovascular Therapy, 5:917-915, 2007.CrossRefGoogle Scholar
  18. 18.
    M. Vavuranakis, I.A. Kakadiaris, S. M. O’Malley, T. G. Papaioannou, E. A. Sanidas, M. Naghavi, S. Carlier, D. Tousoulis, and C. Stefanadis. A new method for assessment of plaque vulnerability based on vasa vasorum imaging, by using contrast-enhanced intravascular ultrasound and differential image analysis. Int. Journal of Cardiology, 2008 (In Press).Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • I. A. Kakadiaris
    • 1
    Email author
  • U. Kurkure
    • 1
  • A. Bandekar
    • 1
  • S. O’Malley
    • 1
  • M. Naghavi
    • 2
  1. 1.CBLUniversity of HoustonHoustonTX
  2. 2.AEHAHoustonTX

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