Two-dimensional (2D) perfusion-CMR has been shown to have greater diagnostic accuracy than single-photon emission computed tomography but remains limited by a lack of complete myocardial coverage. Three-dimensional (3D) whole-heart myocardial perfusion CMR addresses this limitation and has recently been shown to be clinically feasible. However, the feasibility and potential clinical utility of quantitative 3D perfusion measurements, as already shown with 2D-perfusion-CMR and positron emission tomography, has yet to be evaluated. The purpose of this study was to establish the feasibility of quantitative 3D-perfusion-CMR to detect coronary artery disease (CAD). Additionally, as 3D-perfusion-CMR offers the opportunity to select the phase of acquisition, a secondary objective was to determine differences between systolic and diastolic estimates of myocardial blood flow (MBF).
35 patients underwent 3D-perfusion-CMR (Philips 3T Achieva TX) with data acquired at both end-systole and mid-diastole (Fig 1). Systolic and diastolic perfusion images were analyzed in separate reporting sessions in random order. Image quality (0=non-diagnostic, 1=poor, 2=adequate and 3=excellent) and the occurrence of artifact related to respiratory-motion, k-t reconstruction or dark-rim artifact (0=none, 1=mild, 2=moderate and 3=severe) were scored. MBF and myocardial perfusion reserve (MPR) were estimated on a per patient and per territory basis by Fermi function deconvolution. CAD was defined as luminal stenosis ≥70% on quantitative coronary angiography.
Figure 1. This example shows 3D-perfusion-CMR in a 75-year-old man presenting with angina. Stress-induced perfusion defects are seen infero-laterally from base to apex and antero-laterally from mid-ventricle to apex in both diastole and systole. However, perfusion defects are difficult to discern from dark-rim artifact in diastole and are more clearly delineated with systolic acquisition. Late-gadolinium enhancement imaging did not reveal any previous myocardial infarction. X-ray coronary angiography revealed 80% stenosis of a large diagonal branch and significant proximal disease in a large dominant left circumflex artery.
38 coronary territories had significant CAD. MPR had a high diagnostic accuracy for the detection of CAD, in both systole and diastole (area under curve: 0.92 vs. 0.94; p=0.41) (Fig 2). At rest, systolic and diastolic MBF estimates were similar - in both normal and diseased territories (no CAD: 1.24 ± 0.15 vs. 1.25 ± 0.15ml/g/min, p=0.27; CAD: 1.24 ± 0.15 vs. 1.26 ± 0.14ml/g/min, p=0.20). At stress, diastolic MBF estimates were significantly greater than systolic estimates (no CAD: 3.21 ± 0.50 vs. 2.75 ± 0.42ml/g/min, p<0.0001; CAD: 2.13 ± 0.45 vs. 1.98 ± 0.41ml/g/min, p<0.0001). The diastolic/systolic stress MBF ratio was significantly reduced in territories with CAD (CAD: 1.08 ± 0.06 vs. no CAD: 1.17 ± 0.11; p<0.0001). Systolic acquisition had a higher overall image quality score (median: 3 vs. 2, p=0.002) and was less prone to artifact than diastolic acquisition (median artifact score: 0 vs. 1; p<0.0001). In particular, there was a greater frequency of dark-rim artifact in diastole compared to systole (19 vs. 9 patients).
Figure 2. These charts show the individual myocardial perfusion reserve (MPR) values from normal and significantly diseased perfusion territories with both systolic and diastolic acquisition (x=mean value, solid line = median value). The optimal cut-off MPR values determined by receiver-operating characteristic (ROC) analysis are also plotted (dashed lines, 1.75 for systole and 2.02 for diastole). ROC analysis found MPR had a high diagnostic accuracy for the detection of CAD, in both systole and diastole (area under curve: 0.92 vs. 0.94 respectively; p=0.41).
We have shown that quantitative 3D-perfusion-CMR is feasible and can be used to detect CAD with high diagnostic accuracy. Image quality and less artifact, make systole the preferred phase for acquisition in 3D-perfusion-CMR. Finally, there were significant differences in systolic and diastolic MBF estimates and therefore the phase of acquisition should always be stated in future quantitative studies.
JPG and SP receive an educational research grant from Philips Healthcare. SP is funded by a BHF fellowship (FS/1062/28409).