Past Event: Oden Institute Seminar

Challenges and Opportunities for Extracting Cardiovascular Risk Biomarkers

Ioannis Kakadiaris, Professor, Computer Science, Electrical & Computer Engineering, and Biomedical Engineering, University of Houston

Abstract

In this talk, I will present an analysis of the challenges and opportunities in the area of cardiovascular risk prediction. Specifically, I will present our research in the area of biomedical image computing with emphasis on the mining of information from cardiovascular imaging data for the detection of persons with a high likelihood of developing a heart attack in the near future (vulnerable patients). Emphasis will be given to methods for detection and segmentation of anatomical structures, and shape- and motion-estimation of dynamic organs. The left ventricle in non-invasive cardiac MRI (Magnetic Resonance Imaging) data is extracted using a new multi-class, multi-feature fuzzy connectedness method and deformable models will be/are used for shape and volume estimation. In non-invasive cardiac CT (Computed Tomography) data, the thoracic fat is detected using a relaxed version of the multi-class, multi-feature fuzzy connectedness method. Additionally, the calcified lesions in the coronary arteries are identified and quantified using a hierarchical supervised learning framework from the CT data. In non-invasive contrast-enhanced CT, the coronary arteries are detected using our tubular shape detection method for motion estimation and, possibly, for non-calcified lesion detection. In invasive IVUS imaging, our team has developed a unique IVUS acquisition protocol and novel signal/image analysis methods for the detection (for the first time in‐vivo) of ‘vasa vasorum’ (VV). The VV are micro-vessels that are commonly present to feed the walls of larger vessels. However, recent clinical evidence has uncovered their tendency to proliferate around areas of inflammation, including the inflammation associated with vulnerable plaques. In summary, our research is focused on developing innovative computational tools to mine quantitative parameters from imaging data for early detection of asymptomatic cardiovascular patients. The expected impact of our work stems from the fact that sudden heart attack remains the number one cause of death in the US, and unpredicted heart attacks account for the majority of the $280 billion burden of cardiovascular diseases. Bio Prof. Ioannis A. Kakadiaris is a Hugh Roy and Lillie Cranz Cullen University Professor of Computer Science, Electrical & Computer Engineering, and Biomedical Engineering at the University of Houston, Houston, TX, USA. He also holds an adjunct position at the School of Health Information Sciences at the University of Texas, Health Sciences Center. He joined UH in August 1997 after a postdoctoral fellowship at the University of Pennsylvania. Ioannis earned his B.Sc. in physics at the University of Athens in Greece, his M.Sc. in computer science from Northeastern University and his Ph. D. at the University of Pennsylvania. He is the founder and director of the Computational Biomedicine Lab. His research interests include cardiovascular informatics, biomedical image analysis, biometrics, computer vision, and pattern recognition. Dr. Kakadiaris is the recipient of a number of awards, including the NSF Early Career Development Award, Schlumberger Technical Foundation Award, UH Computer Science Research Excellence Award, UH Enron Teaching Excellence Award, and the James Muller Vulnerable Plaque Young Investigator Price. His research has been featured on Discovery Channel, National Public Radio, KPRC NBC News, KTRH ABC News, and KHOU CBS News