Semi-Automatic Graphical Tool for Measuring Coronary Artery Spatially Weighted Calcium Score from Gated Cardiac Computed Tomography Images

The mission of the Biomedical Engineering and Imaging Institute is to create innovative biomedical engineering and imaging technologies that can be utilized in medical science and clinical practice. The goal is to identify and develop techniques for early and accurate diagnosis of diseases, which can then be useful in optimizing more effective treatments. Our cardiovascular imaging group concentrates on image acquisition and analysis techniques for MRI, PET, and CT imaging.

CT imaging can be used to measure calcification and determine the severity of coronary artery disease without invasive procedures. The Agatston score is the most commonly used method for calcium scoring, but it relies on signal thresholds, which can cause early stages of the disease to be missed due to small lowly attenuating deposits not being detectable above image noise. Dr.Jason Liang and his colleagues at the University of Washington developed the idea of the spatially weighted calcium score.

However, at present, there are no reliable and consistent tools to compute the score. We have created a program that offers researchers the ability to calculate it accurately and repeatedly. The spatially weighted calcium score is designed to be sensitive to detect micro-calcification, which occurs before advanced coronary calcification can be detected by traditional calcium scoring CT scans.

That sensitivity is significant in identifying subclinical coronary artery disease and by providing quantitative data, which can be useful when assessing novel risk factors like metal exposure from e-cigarettes, evaluating modifiable risks in younger populations, and exploring the impact of interventions. The immediate application of this work is to examine the relationship between coronary artery calcification and exposure to toxic metals from use of e-cigarettes. This project is funded by NIH and led by Professor Ana Navas-Acien at Columbia University.