Combined anatomy and physiology on coronary computed tomography angiography: a step or two in the right direction

Reasons and implications of agreements and disagreements between coronary flow reserve, fractional flow reserve, and myocardial perfusion imaging

Information on coronary physiology and myocardial blood flow (MBF) in patients with suspected angina is increasingly important to inform treatment decisions. A number of different techniques including myocardial perfusion imaging (MPI), noninvasive estimation of MBF, and coronary flow reserve (CFR), as well as invasive methods for CFR and fractional flow reserve (FFR) are now readily available. However, despite their incorporation into contemporary guidelines, these techniques are still poorly understood and their interpretation to guide revascularization decisions is often inconsistent. In particular, these inconsistencies arise when there are discrepancies between the various techniques. The purpose of this article is therefore to review the basic principles, techniques, and clinical value of MPI, FFR, and CFR-with particular focus on interpreting their agreements and disagreements.

State-of-the-Art Updates on Cardiac Computed Tomographic Angiography for Assessing Coronary Artery Disease

OPINION STATEMENT

Cardiac computed tomographic angiography (CCTA) is a noninvasive imaging modality that is increasingly useful for the evaluation of coronary artery disease (CAD). Over the past decade, CCTA has consistently demonstrated an excellent sensitivity for the detection and exclusion of coronary atherosclerosis in patients with stable or acute chest pain symptoms. Large prospective registries have repeatedly demonstrated the prognostic significance of the presence, extent, or absence of CAD by CCTA. In response to initial concerns, technical advances have permitted a dramatic reduction in patient radiation exposure with preserved image quality. For many patients, the radiation dose of CCTA is less than half of that with conventional myocardial perfusion imaging while providing significantly more anatomic information. Furthermore, CCTA's excellent spatial resolution is increasingly being used for noninvasive assessment of coronary plaque, including the detection of higher-risk vulnerable plaque and association between plaque characteristics and ischemia. Finally, new promising techniques that incorporate physiology with anatomy, such as CT-based fractional flow reserve (FFR-CT) and CT perfusion (CTP), are allowing for the noninvasive hemodynamic assessment of coronary stenoses and improvements in the specificity of CCTA findings. Such advances augur a coming transition when CCTA will be a first-line test for the detection, exclusion, and even management of CAD in many patients.

Non-invasive prediction of hemodynamically significant coronary artery stenoses by contrast density difference in coronary CT angiography

OBJECTIVES

Coronary computed tomography angiography (CTA) allows the detection of obstructive coronary artery disease. However, its ability to predict the hemodynamic significance of stenoses is limited. We assessed differences in plaque characteristics and contrast density difference between hemodynamically significant and non-significant stenoses, as defined by invasive fractional flow reserve (FFR).

METHODS

Lesion characteristics of 59 consecutive patients (72 lesions) in whom invasive FFR was performed in at least one coronary artery with moderate to high-grade stenoses in coronary CTA were evaluated by two experienced readers. Coronary CTA data sets were acquired on a second-generation dual-source CT scanner using retrospectively ECG-gated spiral acquisition or prospectively ECG-triggered axial acquisition mode. Plaque volume and composition (non-calcified, calcified), remodeling index as well as contrast density difference (defined as the percentage decline in luminal CT attenuation/cross-sectional area over the lesion) were assessed using a semi-automatic software tool (Autoplaq). Additionally, the transluminal attenuation gradient (defined as the linear regression coefficient between intraluminal CT attenuation and length from the ostium) was determined. Differences in lesion characteristics between hemodynamically significant (invasively measured FFR ≤0.80) and non-significant lesions (FFR >0.80) were determined.

RESULTS

Mean patient age was 64±11 years with 44 males (75%). 21 out of 72 coronary artery lesions (29%) were hemodynamically significant according to invasive FFR. Mean invasive FFR was 0.66±0.12 vs. 0.91±0.05 for hemodynamically significant versus non-significant lesions. Hemodynamically significant lesions showed a significantly greater percentage of non-calcified plaque compared to non-hemodynamically relevant lesions (51.3±15.3% vs. 43.6±16.5%, p=0.021). Contrast density difference was significantly increased in hemodynamically relevant lesions (26.0±20.2% vs. 16.6±10.9% for non-significant lesions; p=0.013). At a threshold of ≥24%, the contrast density difference predicted hemodynamically significant lesions with a specificity of 75%, sensitivity of 33%, PPV of 35% and NPV of 73%. The transluminal attenuation gradient showed no significant difference between hemodynamically significant and non-significant lesions (-1.4±1.4HU/mm vs. -1.1±1.3HU/mm, p=n.s.).

CONCLUSIONS

Quantitative contrast density difference across coronary lesions in coronary CTA data sets may be applied as a non-invasive tool to identify hemodynamically significant stenoses.

Automated Quantitative Plaque Burden from Coronary CT Angiography Noninvasively Predicts Hemodynamic Significance by using Fractional Flow Reserve in Intermediate Coronary Lesions

PURPOSE

To evaluate the utility of multiple automated plaque measurements from coronary computed tomographic (CT) angiography in determining hemodynamic significance by using invasive fractional flow reserve (FFR) in patients with intermediate coronary stenosis.

MATERIALS AND METHODS

The study was approved by the institutional review board. All patients provided written informed consent. Fifty-six intermediate lesions (with 30%-69% diameter stenosis) in 56 consecutive patients (mean age, 62 years; range, 46-88 years), who subsequently underwent invasive coronary angiography with assessment of FFR (values ≤0.80 were considered hemodynamically significant) were analyzed at coronary CT angiography. Coronary CT angiography images were quantitatively analyzed with automated software to obtain the following measurements: volume and burden (plaque volume × 100 per vessel volume) of total, calcified, and noncalcified plaque; low-attenuation (<30 HU) noncalcified plaque; diameter stenosis; remodeling index; contrast attenuation difference (maximum percent difference in attenuation per unit area with respect to the proximal reference cross section); and lesion length. Logistic regression adjusted for potential confounding factors, receiver operating characteristics, and integrated discrimination improvement were used for statistical analysis.

RESULTS

FFR was 0.80 or less in 21 (38%) of the 56 lesions. Compared with nonischemic lesions, ischemic lesions had greater diameter stenosis (65% vs 52%, P = .02) and total (49% vs 37%, P = .0003), noncalcified (44% vs 33%, P = .0004), and low-attenuation noncalcified (9% vs 4%, P < .0001) plaque burden. Calcified plaque and remodeling index were not significantly different. In multivariable analysis, only total, noncalcified, and low-attenuation noncalcified plaque burden were significant predictors of ischemia (P < .015). For predicting ischemia, the area under the receiver operating characteristics curve was 0.83 for total plaque burden versus 0.68 for stenosis (P = .04).

CONCLUSION

Compared with stenosis grading, automatic quantification of total, noncalcified, and low-attenuation noncalcified plaque burden substantially improves determination of lesion-specific hemodynamic significance by FFR in patients with intermediate coronary lesions.

Enhancing physiologic simulations using supervised learning on coarse mesh solutions

Computational modelling of physical and biochemical processes has emerged as a means of evaluating medical devices, offering new insights that explain current performance, inform future designs and even enable personalized use. Yet resource limitations force one to compromise with reduced order computational models and idealized assumptions that yield either qualitative descriptions or approximate, quantitative solutions to problems of interest. Considering endovascular drug delivery as an exemplary scenario, we used a supervised machine learning framework to process data generated from low fidelity coarse meshes and predict high fidelity solutions on refined mesh configurations. We considered two models simulating drug delivery to the arterial wall: (i) two-dimensional drug-coated balloons and (ii) three-dimensional drug-eluting stents. Simulations were performed on computational mesh configurations of increasing density. Supervised learners based on Gaussian process modelling were constructed from combinations of coarse mesh setting solutions of drug concentrations and nearest neighbourhood distance information as inputs, and higher fidelity mesh solutions as outputs. These learners were then used as computationally inexpensive surrogates to extend predictions using low fidelity information to higher levels of mesh refinement. The cross-validated, supervised learner-based predictions improved fidelity as compared with computational simulations performed at coarse level meshes--a result consistent across all outputs and computational models considered. Supervised learning on coarse mesh solutions can augment traditional physics-based modelling of complex physiologic phenomena. By obtaining efficient solutions at a fraction of the computational cost, this framework has the potential to transform how modelling approaches can be applied in the evaluation of medical technologies and their real-time administration in an increasingly personalized fashion.