Quantifying Plaque Burden and Morphology Using Coronary Computed Tomography Angiography to Predict Coronary Physiology

Now this is not the end. It is not even the beginning of the end. But it is, perhaps, the end of the beginning. u003e u003e —Winston ChurchillCoronary computed tomography angiography (CTA) is a powerful noninvasive technique that can be used to visualize the presence, extent, and severity of both noncalcified and calcified plaque. When compared with other noninvasive imaging approaches, coronary CTA has the highest sensitivity to detect anatomic stenosis and, consequently, the highest negative predictive value to exclude obstructive coronary artery disease (CAD).1 However, once a coronary stenosis is identified, a limitation of CTA—which is equally problematic for invasive coronary angiography—is that the physiological consequences associated with those stenoses cannot be determined with any degree of accuracy.2,3 On the other hand, the presence and severity of ischemia are essential for understanding the pathophysiology of patient’s symptoms and determining the potential role of coronary revascularization. Consequently, multiple techniques have been introduced to harness physiological data from CTA and infer whether a stenosis is hemodynamically significant. Some investigators have applied computational fluid dynamic modeling to estimate coronary pressure gradients and calculation of the fractional flow reserve across stenosis.4 Others have relied on the use of iodinated contrast at rest and during pharmacological stress to assess myocardial perfusion (computed tomography [CT] perfusion)5 in a similar manner to radionuclide scintigraphy and magnetic resonance imaging–based myocardial perfusion techniques. Recent clinical trials have demonstrated that in carefully selected patients, these techniques can provide useful information to understand the physiological significance of stenoses.6–9 So, it is clear that the paradigm of isolated stenosis quantification with angiography (noninvasive or invasive) has provided insufficient and, sometimes, misleading information for diagnosis and management of CAD. However, there is limited data addressing the question of whether a more complete quantification of …