CT-based Diagnosis of Diffuse Coronary Artery Disease on the Basis of Scaling Power Laws

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Diffuse coronary artery disease (CAD) without severe segmental stenosis is a substrate for plaque rupture (1–3). Hence, diffuse CAD is associated with unstable coronary syndromes or myocardial infarctions, which have significant clinical implications (4–6). In contrast to severe segmental stenosis, diffuse CAD is difficult to diagnose angiographically given the absence of a “normal” reference vessel (7). Although intravascular ultrasonography (US) has been used to visualize plaque burden in the vessel wall for the diagnosis of diffuse CAD (8,9), it is an interventional tool that requires an invasive procedure. Hence, there is a need for a noninvasive method with which to quantify diffuse CAD.

There have been previous attempts at applying global morphologic features of the coronary artery tree in the assessment of diffuse CAD (7,10,11). Several experimental reports have also documented a direct relationship between coronary artery lumen size and heart weight or distal myocardial bed size (12–18) and between myocardial mass and the cumulative length of the arterial branches that perfuse the region (18,19). On the basis of the principle of minimum energy, we have recently deduced scaling power laws between length and volume and between length and cross-sectional area in an entire tree structure of various organs in different species (20,21). In particular, these scaling power laws have a self-similar nature (20–22), which implies that they can be clinically applied to a partial tree (eg, an epicardial coronary artery tree obtained with angiography, computed tomography [CT], or magnetic resonance [MR] imaging). Hence, we hypothesized that the length-volume scaling power law (ie, scaling relation and power law distribution for the sum of intravascular lengths and volumes in a tree) provides the signature of “normal” vasculature and deviations from which can be used to quantify the extent of diffuse CAD. The purpose of this study was to provide proof of concept for a diagnostic method to assess diffuse CAD on the basis of coronary CT angiography.