Assessment of coronary artery stenosis severity and location: quantitative analysis of transmural perfusion gradients by high-resolution MRI versus FFR

Abstract

Objectives: This study sought to test the hypothesis that transmural perfusion gradients (TPG) on adenosine stress myocardial perfusion cardiac magnetic resonance (CMR) predict hemodynamically significant coronary artery disease (CAD) as defined by fractional flow reserve (FFR).

Background: Myocardial ischemia affects the subendocardial layers of the left ventricular myocardium earlier and more severely than the outer layers, and the identification of TPG should be sensitive and specific for the diagnosis of CAD. Previous studies have shown that high spatial resolution myocardial perfusion CMR allows quantitation of TPG between the subendocardium and the subepicardium.

Methods: Sixty-seven patients (53 men, age 61 ± 9 years) underwent coronary angiography and high-resolution (1.2 × 1.2-mm in-plane) adenosine stress perfusion CMR at 3.0-T. TPG was calculated for 3 coronary territories. Visual analysis was performed to identify myocardial ischemia. FFR was measured in all vessels with ≥50% severity stenosis. FFR <0.8 was considered hemodynamically significant. In a training group of 30 patients, the optimal threshold of TPG to detect significant CAD was determined (Group 1). This threshold was then tested prospectively in the remaining 37 patients (Group 2).

Results: In Group 1, a 20% TPG provided the best diagnostic threshold on both per-segment and per-patient analysis. Applied to Group 2, this threshold yielded a sensitivity of 0.78, specificity of 0.94, and area under the curve of 0.86 for the detection of CAD in a per-segment analysis and of 0.89, 0.83, and 0.86 in a per-patient analysis, respectively. TPG had a similar diagnostic accuracy to visual assessment. Linear regression analysis showed a relationship between TPG and FFR values, with r = 0.63 (p < 0.001).

Conclusions: The quantitative analysis of transmural perfusion gradients on high-resolution myocardial perfusion CMR accurately predicts hemodynamically significant CAD as defined by FFR. A TPG diagnostic threshold of 20% is as accurate as visual assessment.

Figure 1. Schematic Representation of TPG Analysis

(A) After accurate motion correction and image registration, high-resolution perfusion series are segmented by drawing the endocardial (endo) and epicardial (epi) contour. By means of bilinear interpolation, data are resampled in 10 transmural layers and 60 radial segments per slice. (B) The algorithm calculates the intensity of the gradient G in each angular (α) and temporal position (t) by the spatial averaging of the SI of the inner (l_endo) and outer third (I_epi) of the LV wall, normalized by the average transmural SI (I_trasm) (B). (C) The results are displayed on the gradientogram plot. The intensity of the gradient is represented by the gray level in each radial segment (y-axis) and for each temporal dynamic (x-axis). Increasing endocardial to epicardial gradients are represented with a darkening gray level, so that an endocardial perfusion defect generates a dark area in the gradientogram. (D) The gradientogram can be segmented at different percentage thresholds of the gradient’s amplitude. In this case, an intensity threshold of 20% identifies a significant transmural perfusion gradient (green area). LV = left ventricular; SI = signal intensity; TPG = transmural perfusion gradient.

Figure 2. Example of TPG Analysis

Upper series, left to right: apical, mid-ventricular, and basal perfusion images at peak enhancement during first pass of gadolinium. The asterisks indicate a subendocardial perfusion defect in the inferior segments (no ischemia was seen on the apical slice in this case). Data are sampled in the radial direction starting from the 0° position, clockwise. Lower series, left to right: gradientogram plots segmented on a 15% threshold showing green areas of inducible TPG corresponding to areas of subendocardial ischemia in the corresponding CMR images. The angular position is represented on the y-axis. The time axis represents the evolution of the transmural perfusion gradient from the SI upslope in the left ventricle (T-onset) to the 15 following dynamic images (T-onset+15s). CMR = cardiac magnetic resonance; other abbreviations as in Figure 1.

Figure 3. ROC Analysis for TPG Analysis on Per-Vessel and Per-Patient Analysis

When the best TPG threshold (20%) was applied to Group 2, it yielded a sensitivity of 0.78, specificity of 0.94, and area under the curve of 0.86 for a per-vessel analysis, and sensitivity 0.89, specificity 0.83, and area under the curve 0.86 for a per-patient analysis. ROC = receiver-operating characteristic; TPG = transmural perfusion gradient.

Figure 4. Scatterplot Showing the Distribution of TPG Values According to the Invasive Diagnosis

A progressive rise of TPG values was observed for increasing degrees of severity of coronary artery stenosis. Segments supplied by vessels with FFR <0.8 had higher TPG values compared with segments supplied by vessels with FFR ≥0.8 and vessels with no lesions or angiographic lesions <50%. The highest TPG values were measured in vessels with chronic total occlusion (CTO). FFR = fractional flow reserve; TPG = transmural perfusion gradient.

Figure 5. Scatterplot Showing the Distribution of TPG Values According to FFR

Linear regression analysis showed a relationship between TPG and FFR values. Several physiological factors such as the amount of collateral flow, microvascular reactivity, myocardial contractile function and the presence of subendocardial scar are in principle capable to modulate the relationship between TPG and FFR and can explain the observed degree of correlation, with r = 0.63 (0.47 to 0.75; p < 0.0001). Abbreviations as in Figure 4.