Diagnostic Performance of Fully Automated Pixel-Wise Quantitative Myocardial Perfusion Imaging by Cardiovascular Magnetic Resonance

Abstract

Objectives: The authors developed a fully automated framework to quantify myocardial blood flow (MBF) from contrast-enhanced cardiac magnetic resonance (CMR) perfusion imaging and evaluated its diagnostic performance in patients.

Background: Fully quantitative CMR perfusion pixel maps were previously validated with microsphere MBF measurements and showed potential in clinical applications, but the methods required laborious manual processes and were excessively time-consuming.

Methods: CMR perfusion imaging was performed on 80 patients with known or suspected coronary artery disease (CAD) and 17 healthy volunteers. Significant CAD was defined by quantitative coronary angiography (QCA) as ≥70% stenosis. Nonsignificant CAD was defined by: 1) QCA as <70% stenosis; or 2) coronary computed tomography angiography as <30% stenosis and a calcium score of 0 in all vessels. Automatically generated MBF maps were compared with manual quantification on healthy volunteers. Diagnostic performance of the automated MBF pixel maps was analyzed on patients using absolute MBF, myocardial perfusion reserve (MPR), and relative measurements of MBF and MPR.

Results: The correlation between automated and manual quantification was excellent (r = 0.96). Stress MBF and MPR in the ischemic zone were lower than those in the remote myocardium in patients with significant CAD (both p < 0.001). Stress MBF and MPR in the remote zone of the patients were lower than those in the normal volunteers (both p < 0.001). All quantitative metrics had good area under the curve (0.864 to 0.926), sensitivity (82.9% to 91.4%), and specificity (75.6% to 91.1%) on per-patient analysis. On a per-vessel analysis of the quantitative metrics, area under the curve (0.837 to 0.864), sensitivity (75.0% to 82.7%), and specificity (71.8% to 80.9%) were good.

Conclusions: Fully quantitative CMR MBF pixel maps can be generated automatically, and the results agree well with manual quantification. These methods can discriminate regional perfusion variations and have high diagnostic performance for detecting significant CAD. (Technical Development of Cardiovascular Magnetic Resonance Imaging; NCT00027170).

Keywords: computer-aided diagnosis; image processing; magnetic resonance imaging; myocardial blood flow; myocardial perfusion; quantification.

Published by Elsevier Inc.

Figures

FIGURE 1

Flowchart of the Automated Pixel-Wise MBF Quantification Pipeline The automated processing pipeline for first-pass cardiac magnetic resonance myocardial blood flow (MBF) map quantification. LV = left ventricular.

FIGURE 2

Automated MBF Pixel Maps in a Normal Volunteer Automated stress and rest myocardial blood flow (MBF) pixel maps in a normal volunteer show coherent hyperemic MBF (orange) and rest MBF (green) on all 3 slices (Online Video 1).

FIGURE 3

Automated MBF Pixel Maps in Patient With Single-Vessel Disease Automated MBF pixel maps in a patient with 89% right coronary artery stenosis by QCA show an inferior perfusion defect (red arrows) on the stress perfusion image and MBF map (Online Video 2). The possible perfusion defect in the basal anteroseptal wall did not reach abnormal thresholds. It was associated with a severe narrowing of a septal perforator artery that was too small for QCA. MBF = myocardial blood flow; QCA = quantitative coronary angiography.

FIGURE 4

Automated MBF Pixel Maps in Patient With Multivessel Disease Automated MBF pixel maps in a patient with an 87% circumflex stenosis, an 84% left anterior descending stenosis, and a 65% right coronary artery stenosis by QCA show corresponding perfusion defects in the stress maps in all 3 coronary artery territories. There is some epicardial hyperemic perfusion in the basal anteroseptal and anterolateral segments (Online Video 3). MBF = myocardial blood flow; QCA = quantitative coronary angiography.

FIGURE 5

Comparisons of MBF Between Automated and Manual Quantification Correlations and Bland-Altman plots comparing automatically and manually quantified MBF in healthy volunteers. The dashed lines represent the bias (MBFautomated − MBFmanual) and limits of agreement (mean ± 1.96 SD). MBF = myocardial blood flow.

FIGURE 6

Comparisons of MBF Between Patients and Healthy Volunteers In patients with significant coronary artery disease (CAD+), myocardial blood flow (MBF) and myocardial perfusion reserve (MPR) in the ischemic sectors are significantly lower than in the remote sectors (all p

FIGURE 7

Diagnostic Accuracy Comparisons Receiver-operating characteristic curves show the diagnostic performance of fully automated CMR perfusion quantification by myocardial blood flow (MBF), myocardial perfusion reserve (MPR), relative MBF (rMBF), and relative MPR (rMPR) on a per-patient and per-vessel analysis.