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.
KeywordsHeart Attack Vulnerable Plaque Image Analysis Method Probabilistic Atlas Frame Gating
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.
- 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.A.N. Bandekar. A Unified Knowledge-based Segmentation Framework for Medical Images. PhD thesis, University of Houston, Dec. 2006.Google Scholar
- 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
- 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.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.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.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
- 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
- 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.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