Imaging-based Phenotype for Cardiovascular Risk Assessment

Cardiovascular (CV) disease is a major healthcare challenge in the United States and the world. Early and accurate risk assessment is critically important because it will provide great opportunities to prevent the devastating disease and thus significantly reduce health-care burden. Over the past decades, a number of CV risk factors have been identified from longitudinal population studies. More recent studies have shown that imaging-based phenotypes, such as calcification and adipose can significantly increase the power of CV risk assessment. However, the current risk factors are still not satisfactory for early and accurate prediction. This project aims to further advance this area of research by developing methods for computing novel imaging-based phenotypes from non-contrast Computed Tomography (CT) images and new models for analyzing CV risk factors based on new phenotypes. One of the clinical hypotheses is that detection of artery deformation and fat content around artery can provide important factors in subclinical assessment of cardiac risk for asymptomatic subjects. The significant challenges for cardiovascular risk assessment using non-contrast CT images are the poor quality of the low-dose, non-contrast CT images and amorphousness of the coronary structures. This project addresses these challenges in three aspects.

Grantor dc:publisher

Wake Forest University

Year dc:date.issued

2016

Author dc:creator

• Zheng, Mingna

Language dc:language.iso

en

Handle dc:identifier.uri

http://hdl.handle.net/10339/59299

OAI identifier oai:identifier

oai:wakespace.lib.wfu.edu:10339/59299