Identifying Coronary Artery Calcification on Non-gated Computed Tomography Scans

Summary

Here, we present a protocol to reliably and systematically identify coronary artery calcification (CAC) on non-gated computed tomography (CT) scans of the chest or abdomen. CAC provides an objective measure of coronary artery disease for both research and clinical purposes.

Abstract

Coronary artery calcification (CAC) provides an objective measure of coronary artery disease and can readily be identified on non-gated computed tomography (CT) scans with a high correlation with gated cardiac CT scans. This standardized protocol takes a step-wise approach to not only optimizing an image for the identification of calcification but also to distinguishing CAC from other common causes of calcification in the cardiac silhouette. Recognition of CAC on non-gated CT scans helps to identify a very powerful prognostic factor that can influence therapeutic interventions or downstream diagnostic testing without requiring a gated cardiac scan. These non-gated CT scans are often acquired as part of the routine care of the patient, and this data is readily available without another dose of ionizing radiation. This protocol allows for the precise and accurate extraction of this data for the purposes of retrospective data analysis in clinical research studies, but also in the clinical evaluation and management of patients.

Introduction

Coronary artery disease is a predictor of major adverse cardiovascular events. CAC on CT scans provides objective evidence of coronary artery disease and may identify previously undiagnosed patients. In addition, CAC has a significant prognostic value. Specifically, the absence of CAC on gated cardiac CT scans identifies a patient population that has a low risk for subsequent cardiovascular events in many different subsets of patients, including patients presenting with cardiac symptoms, as well as asymptomatic patients^1,2. With ~70 million CT scans performed in the United States and the usage rising, and appr....

Protocol

This protocol follows guidelines set forth by the Institutional Review Board and human subject research protocol of the University of Kentucky.

1. Opening the Image Viewer

1. Open the image viewer used at the institution where the research is being conducted. Double-click on the desktop icon to open the viewer.
2. Log in using an institutional username and password.

2. Identifying the Appropriate Patient

1. Click on the Study List icon in the toolbar.
2. Under the Search Criteria drop-down list, choose the option labeled With Patient....

Results

Coronary anatomy is relatively predictable in most patients as described above. The typical locations to evaluate these vessels are also easily identified in most patients (Figure 1). Using the described methodology, the presence or absence of CAC could be reliably identified in 84% of the patients in a single cohort (267 of 317 possible patients)¹⁵. The vast majority of patients excluded did not have a CT scan in the designated time f.......

Discussion

The identification of CAC is an extremely powerful prognostic tool with an ever-growing body of literature supporting its use in many different clinical scenarios. The majority of the literature is focused on gated cardiac CT scans for the identification of CAC, but there is robust evidence of both the correlation of CAC on non-gated CT scans, as well as the prognostic ability of this finding. Given the CT scan utilization in the United States, as well as the ever-growing concerns about radiation exposure, the ability to.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Microsoft Windows Server 2012 R2 Standard	PowerEdge R730	8F8KFB2	Server specifications for post-processing software: Intel(R) Xeon(R) CPU E5-2609 v3 @ 1.90GHz Intel(R) Xeon®CPU E5-2609 v3 @ 1.90GHz
Intuition	Terarecon	4.4.12.xxx	Post-processing software
McKesson Radiology Viewing Station	McKesson	Station Lite Version 1.0.0.182	IP version 8.0.31.0
Computer Desktop and Monitor: Optiplex 9030 AIO	Dell	Optiplex 9030 AIO	Processor: Intel  Core i5-4590S CPU @ 3.00 GHz, 3001Mhz, 4 Cores, 4 Logical Processors