Author + information
- Received October 8, 2018
- Revision received December 3, 2018
- Accepted December 6, 2018
- Published online February 13, 2019.
- Tushar Kotecha, MBChBa,b,∗,
- Ana Martinez-Naharro, MDb,c,∗,
- Michele Boldrini, MDc,
- Daniel Knight, MDa,b,
- Philip Hawkins, PhDb,c,
- Sundeep Kalra, PhDb,
- Deven Patel, MDb,
- Gerry Coghlan, MDb,
- James Moon, MDa,d,
- Sven Plein, PhDe,
- Tim Lockie, PhDb,
- Roby Rakhit, MDa,b,
- Niket Patel, MDa,b,
- Hui Xue, PhDf,
- Peter Kellman, PhDf and
- Marianna Fontana, PhDb,c,∗ ()
- aInstitute of Cardiovascular Science, University College London, United Kingdom
- bRoyal Free Hospital, London, United Kingdom
- cDivision of Medicine, University College London, United Kingdom
- dBarts Heart Centre, London, United Kingdom
- eInstitute of Cardiovascular and Metabolic Medicine, University of Leeds, United Kingdom
- fNational Heart, Lung, and Blood Institute, National Institute of Health, Bethesda, Maryland
- ↵∗Address for correspondence:
Dr. Marianna Fontana, Department of Cardiovascular Magnetic Resonance, Royal Free Hospital, Rowland Hill Street, London NW3 2PF, United Kingdom.
Objectives The study sought to assess the performance of cardiovascular magnetic resonance (CMR) myocardial perfusion mapping against invasive coronary physiology reference standards for detecting coronary artery disease (CAD, defined by fractional flow reserve [FFR] ≤0.80), microvascular dysfunction (MVD) (defined by index of microcirculatory resistance [IMR] ≥25) and the ability to differentiate between the two.
Background Differentiation of epicardial (CAD) and MVD in patients with stable angina remains challenging. Automated in-line CMR perfusion mapping enables quantification of myocardial blood flow (MBF) to be performed rapidly within a clinical workflow.
Methods Fifty patients with stable angina and 15 healthy volunteers underwent adenosine stress CMR at 1.5T with quantification of MBF and myocardial perfusion reserve (MPR). FFR and IMR were measured in 101 coronary arteries during subsequent angiography.
Results Twenty-seven patients had obstructive CAD and 23 had nonobstructed arteries (7 normal IMR, 16 abnormal IMR). FFR positive (epicardial stenosis) areas had significantly lower stress MBF (1.47 ± 0.48 ml/g/min) and MPR (1.75 ± 0.60) than FFR-negative IMR-positive (MVD) areas (stress MBF: 2.10 ± 0.35 ml/g/min; MPR: 2.41 ± 0.79) and normal areas (stress MBF: 2.47 ± 0.50 ml/g/min; MPR: 2.94 ± 0.81). Stress MBF ≤1.94 ml/g/min accurately detected obstructive CAD on a regional basis (area under the curve: 0.90; p < 0.001). In patients without regional perfusion defects, global stress MBF <1.82 ml/g/min accurately discriminated between obstructive 3-vessel disease and MVD (area under the curve: 0.94; p < 0.001).
Conclusions This novel automated pixel-wise perfusion mapping technique can be used to detect physiologically significant CAD defined by FFR, MVD defined by IMR, and to differentiate MVD from multivessel coronary disease. A CMR-based diagnostic algorithm using perfusion mapping for detection of epicardial disease and MVD warrants further clinical validation.
- cardiovascular magnetic resonance
- coronary artery disease
- index of microcirculatory resistance
- microvascular dysfunction
- myocardial blood flow
Stable angina is a common clinical presentation that is frequently the result of obstructive coronary artery disease (CAD). Invasive coronary angiography (ICA) allows for diagnosis of epicardial CAD and treatment with percutaneous coronary intervention; however, more than one-half of patients referred for ICA for investigation of chest pain have normal or angiographically nonobstructed coronary arteries (1). Many of these patients are told their test is “normal” or given a diagnosis of microvascular dysfunction (MVD), with reassurance and no specific treatment, despite angina in the absence of obstructive CAD being associated with increased cardiovascular risk (2), as well as higher rates of hospital admissions and repeat coronary angiography (3).
Visual analysis of ICA is able to demonstrate coronary artery patency but fails to provide information about the physiological significance of a stenosis (4). Fractional flow reserve (FFR) is a pressure-derived measure that can be used to assess the severity of epicardial stenosis, and FFR-guided revascularization has been shown to be superior to angiography-guided intervention in stable CAD (5). It has emerged as the invasive reference standard for physiological assessment of stenosis severity in view of its ease of use, reproducibility, and lack of variability with changes in heart rate and blood pressure (6). Index of microcirculatory resistance (IMR) is an additional invasive physiological measure that has been proposed as a useful tool to diagnose coronary MVD, independent of epicardial stenosis (7). It has been shown that >25% of vessels with FFR >0.80 have elevated IMR, consistent with a diagnosis of MVD (8).
Stress perfusion cardiovascular magnetic resonance (CMR) has emerged as an accurate noninvasive tool for the detection of clinically significant CAD, and its use has been incorporated into guidelines (9). In clinical practice, stress perfusion CMR is usually evaluated qualitatively by visual analysis of first-pass gadolinium images (10,11). This approach may be inaccurate when myocardial blood flow (MBF) is globally reduced (i.e., in 3-vessel disease), but also does not allow reliable quantification of severity of disease or the assessment of microvascular function.
A new respiratory motion-corrected myocardial perfusion method with automated in-line perfusion mapping has been developed, allowing free breathing acquisition and pixel-wise quantification of MBF (12). This dual-sequence protocol provides the ability to rapidly and quantitatively assess MBF using perfusion maps generated and displayed in-line on the scanner within minutes. This would be clinically desirable to objectively assess the severity of CAD, to potentially diagnose MVD, and to risk-stratify patients. The sequence has recently been shown to have good repeatability in healthy subjects (13). Although it has been shown that there is good correlation between MBF quantified by stress CMR and positron emission tomography (PET) (14), this new sequence requires further clinical validation and assessment.
The aims of this study were to: 1) validate in-line myocardial perfusion mapping against invasively measured FFR for the diagnosis of physiologically obstructive CAD; 2) assess myocardial perfusion mapping as a tool to diagnose coronary MVD using IMR as the invasive reference standard; and 3) assess the ability of myocardial perfusion mapping to differentiate between MVD and obstructive 3-vessel CAD.
Patients aged between 18 and 80 years with symptoms consistent with stable angina were recruited at the Royal Free Hospital, London, United Kingdom, between May 2017 and May 2018. Participants underwent stress perfusion CMR with myocardial perfusion mapping before ICA and then measurement of FFR and IMR in all major epicardial vessels during the invasive procedure. All patients were referred for ICA for clinical reasons and underwent stress perfusion CMR before the invasive procedure as part of the research protocol (unless CMR was performed in the preceding 30 days for clinical reasons). Fifteen healthy volunteers (control subjects) with no symptoms and no history of cardiovascular disease, hypertension, were also recruited and underwent stress perfusion CMR only. Ethical approval was obtained from the London Hampstead Research Ethics Committee for recruitment of patients (REC reference: 17/LO/0500) and South-Central Research Ethics Committee for recruitment of control subjects (REC reference: 17/SC/0077). All patients recruited provided written informed consent.
Previous coronary artery bypass surgery, myocardial infarction (with transmural late gadolinium enhancement), unstable symptoms (including crescendo angina, angina at rest, or acute coronary syndrome), standard contraindications to CMR or adenosine, or estimated glomerular filtration rate <30 ml/min/1.73 m2 were excluded.
Stress perfusion CMR image acquisition and analysis
All patients underwent stress perfusion CMR at 1.5-T (Magnetom Aera, Siemens Healthcare, Erlangen, Germany). Scans were performed in accordance with local protocol and patients were asked to refrain from caffeine for at least 12 hours before the scan. Basal, mid-ventricular, and apical short-axis perfusion images were acquired both at rest and during hyperemia. Hyperemia was induced using adenosine infused via a peripheral cannula at a rate of 140 μg/kg/min for 4 min with a further 2 min at 175 μg/kg/min if there was evidence of insufficient stress (such as no heart rate response and no symptoms). Image acquisition was performed over 60 heartbeats with a bolus of 0.05 mmol/kg gadoterate meglumine (Dotarem, Guerbet SA, Paris, France) administered at 4 ml/s followed by a 20-ml saline flush during acquisition of the perfusion sequence. The perfusion sequence protocol has been described previously; further details are provided in the Supplemental Appendix (12).
Perfusion maps were analyzed using offline using Osirix MD 9.0 (Bernex, Switzerland). The endocardial and epicardial borders were manually delineated for each basal, mid-ventricular, and apical perfusion map, excluding obvious image artefacts and coronary arteries. Average MBF in milliliters per gram per minute was assessed per coronary artery territory according the 17-segment model modified for coronary dominance and excluding the apical segment (15). Myocardial perfusion reserve (MPR) was also calculated, defined as the ratio between MBF at stress over rest. Global MBF in milliliters per gram per minute was calculated by averaging MBF across the 3 slices; global MPR was calculated as the ratio between global stress MBF and global rest MBF. First-pass perfusion images were also analyzed visually by 2 experienced observers blinded to the findings of the coronary angiograms, coronary physiology, and perfusion maps.
Invasive coronary physiology measures and analysis
Where possible, indices of coronary physiology were obtained in all major epicardial vessels. Measurements were obtained using a coronary PressureWire (St Jude Medical, St Paul, Minnesota) connected to a RadiAnalyzer (St Jude Medical). FFR ≤0.80 and IMR ≥25 were defined as abnormal (5,7). Small, nondominant vessels were not interrogated. Where a vessel had a critical stenosis (>95% stenosis diameter) and it was assessed by the operator to be unsafe to pass a pressure wire, it was assumed that the vessel had an FFR <0.80. For regional per-territory analysis, each vessel was classified as follows: obstructive CAD, FFR ≤0.80; MVD, FFR >0.80; IMR ≥25; normal, FFR >0.80 and IMR <25. For global analysis, patients were classified into the following groups: single-vessel disease, FFR ≤0.80 in 1 epicardial artery; double vessel disease, FFR ≤0.80 in 2 epicardial arteries; 3-vessel disease, FFR ≤0.80 in all 3 epicardial arteries; MVD, FFR >0.80 in all 3 epicardial arteries and IMR ≥25 in at least 1 artery; and normal coronary physiology (NCP), FFR >0.80 and IMR <25 in all epicardial vessels. Further details of the coronary physiology protocol are described in the online material.
All continuous variables were tested for normal distribution (Shapiro-Wilk test). Normally distributed metrics are summarized by the mean ± SD. For normally distributed variables, the unpaired Student’s t-test was used to compare the means between 2 groups and 1-way analysis of variance with post hoc Bonferroni correction to compare the means of multiple groups. Data that were not normally distributed are summarized by the median (interquartile range). Correlations between continuous variables were evaluated by the Spearman correlation coefficient (rho). Receiver-operating characteristic (ROC) curves were compared using the Delong method. The Youden index was used to identify optimal stress MBF and MPR cutoffs on a per-territory basis using all coronary territories combined and also by individual coronary territory. A p value <0.05 was considered statistically significant. No adjustments were made for MBF or physiology measurements within individuals. ROC analyses were performed using MedCalc 184.108.40.206 (Ostend, Belgium). All other statistical analysis was performed using SPSS Statistics, version 24 (IBM, Somers, New York).
Fifty-four patients and 15 control subjects were prospectively enrolled (Supplemental Figure 1). Two participants were excluded following stress perfusion CMR, 1 because of evidence of previous transmural myocardial infarction on late gadolinium imaging and the other because of inadequate stress due to recent caffeine intake. Two participants did not undergo coronary physiology assessment, both because of operational issues. In total, 50 patients (mean age: 63 ± 8 years, 40 [80%] men) underwent both stress perfusion CMR and coronary physiology assessment. In total, FFR and IMR were determined in 101 vessels and FFR only in 13 vessels. Twenty-three vessels had critical stenoses (assessed by the operator to be unsafe to pass a pressure wire) and were presumed to have FFR ≤0.80.
Twenty-seven patients had obstructive CAD, defined as FFR ≤0.80 in at least 1 major epicardial vessel. FFR was abnormal in 48 vessels (14 single-vessel disease, 5 2-vessel disease, and 8 3-vessel disease). FFR was positive in the left anterior descending artery in 25 cases, circumflex artery in 11 cases, and right coronary artery in 12 cases. Twenty-three patients had nonobstructive CAD, defined as FFR >0.80 in all 3 major epicardial vessels. Of these, 16 had evidence of MVD, defined as IMR ≥25 in at least 1 epicardial vessel. Seven patients had FFR >0.80 and IMR <25 in all epicardial vessels; these were therefore defined as NCP. No patients included in the analysis had evidence of transmural late gadolinium enhancement. Six patients (12%) had evidence of subendocardial (<50% wall thickness) late gadolinium enhancement (average: 2.3 ± 1.2 segments). Baseline characteristics are summarized in Table 1.
Regional stress MBF and MPR for detection of obstructive CAD
Mean stress MBF and MPR were reduced in myocardial territories supplied by vessels with FFR ≤0.80 (mean stress MBF: 1.47 ± 0.48 ml/g/min, FFR ≤0.80 vs. 2.30 ± 0.49 ml/g/min FFR >0.80, p < 0.001; MPR: 1.75 ± 0.60 FFR ≤0.80 vs. 2.78 ± 0.84 FFR >0.80, p < 0.001) (Figure 1). Examples of stress perfusion maps and corresponding coronary angiogram images of a patient with severe obstructive single-vessel disease are displayed in Figure 2. Including only vessels in which FFR was measured, there were weak correlations between stress MBF and MPR with FFR (stress MBF and FFR: rho = 0.400, p < 0.001; MPR and FFR: rho = 0.426, p < 0.001). In vessels where FFR ≤0.80 there was a linear relationship between stress MBF and FFR (FFR = 0.35 + (0.002 × stress MBF), R2 = 0.502, p < 0.001) (Figure 3).
ROC analysis was performed to assess the performance of regional stress MBF and MPR to predict FFR ≤0.80 on a per-territory basis using all coronary arteries. Stress MBF had an area under the curve (AUC) of 0.90 (95% confidence interval [CI]: 0.85 to 0.96, p < 0.001) and MPR had an AUC of 0.82 (95% CI: 0.75 to 0.90, p < 0.001), with stress MBF performing significantly better (p = 0.0364). When combining all coronary territories, the optimal cutoff value for stress MBF was 1.94 ml/g/min, with 85% sensitivity, 81% specificity, positive predictive value (PPV) 70.1%, and negative predictive value (NPV) 91%. The optimal MPR cutoff value was 1.96, with 75% sensitivity, 80% specificity, PPV 67%, and NPV 86% (Figure 4). For visual analysis of first-pass perfusion images, sensitivity was 81% and specificity 80% (global agreement 86%, kappa 0.72).
After removal of vessels with abnormal IMR (MVD areas), the diagnostic accuracy of stress MBF and MPR improved significantly (stress MBF: AUC 0.95 [95% CI: 0.88 to 0.98], optimal cutoff 2.01 ml/g/min, sensitivity 90%, specificity 89%, PPV 88%, NPV 90%, p < 0.001; MPR: AUC 0.87 (95% CI: 0.78 to 0.93), optimal cutoff 2.15, sensitivity 79%, specificity 87% PPV 84%, NPV 82%, p < 0.001), with stress MBF remaining superior to MPR (p = 0.0322) (Figure 4). Analysis was also performed separately for each coronary territory with, stress MBF AUC 0.90 for the left anterior descending artery, 0.91 for circumflex artery, and 0.98 for right coronary artery (Table 2). When these thresholds and the combined threshold (1.94 ml/g/min) were retested in the same cohort, the accuracy of using separate thresholds for each territory to detect obstructive disease was 85%; using the combined threshold, this was 82% (Supplemental Figure 2).
Diagnosis of regional coronary MVD
In total, 34 vessels (38%) had abnormal IMR in the presence of FFR >0.80 (unobstructed vessels), 28 vessels in patients with nonobstructive CAD and 6 unobstructed vessels in patients 1- or 2-vessel obstructive disease. In unobstructed vessels, stress MBF and MPR were reduced in myocardial territories supplied by arteries with IMR ≥25 (mean stress MBF: 2.10 ± 0.35 ml/g/min IMR ≥25 vs. 2.47 ± 0.50 ml/g/min IMR <25, p < 0.001; MPR: 2.41 ± 0.79 IMR ≥25 vs. 2.94 ± 0.81 IMR <25, p = 0.004). Stress MBF correlated better with IMR compared with MPR with IMR (stress MBF and IMR: rho = –0.368, p = 0.001, MPR and IMR: rho = –0.244, p = 0.025), with regression analysis showing a nonlinear relationship (Supplemental Figure 3). ROC analysis showed an optimal cutoff value for regional stress MBF of ≤2.19 ml/g/min to predict abnormal IMR in that territory with sensitivity 71%, specificity 70%, PPV 62%, and NPV 78% (AUC 0.73 [95% CI: 0.63 to 0.84], p < 0.001). The optimal regional MPR cutoff to predict abnormal IMR was ≤2.06 (sensitivity 44%, specificity 92%, PPV 47%, and NPV 66%; AUC 0.68 [95% CI: 0.56 to 0.80], p = 0.004). There was no difference in performance between stress MBF and MPR (p = 0.4078).
Myocardial perfusion mapping to differentiate epicardial disease from coronary MVD and normal
On global analysis of patients with no apparent regional perfusion defects, obstructive 3-vessel disease showed the greatest reduction in global stress MBF and MPR with MVD showing moderate reduction compared with patients with NCP (FFR >0.80 and IMR <25 in all 3 vessels) and control subjects (global stress MBF: 1.40 ± 0.57 ml/g/min 3-vessel disease vs. 2.03 ± 0.30 ml/g/min MVD vs. 2.74 ± 0.64 ml/g/min patients with NCP vs. 3.17 ± 0.65 ml/g/min control subjects, p < 0.001 between groups and p < 0.05 for all individual comparisons except patients with NCP vs. control subjects [Figures 5 and 6⇓⇓]; MPR: 1.71 ± 0.70 3-vessel disease vs. 2.37 ± 0.73 MVD vs. 2.59 ± 0.59 patients with NCP vs. 4.11 ± 0.62 control subjects, p < 0.001 between groups and p < 0.01 for control subjects vs. all others, nonsignificant for all other individual comparisons). There was no significant difference in rest perfusion between the groups (global rest perfusion: 0.85 ± 0.32 ml/g/min 3-vessel disease vs. 0.92 ± 0.28 ml/g/min MVD vs. 1.09 ± 0.31 ml/g/min patients with NCP vs. 0.78 ± 0.18 ml/g/min control subjects, nonsignificant for all comparisons).
ROC analysis showed that global stress MBF >2.25 ml/g/min was able to differentiate normal from abnormal (obstructive CAD or MVD) with 95% sensitivity and 88% specificity (PPV 88%, NPV 96%, AUC 0.96 [95% CI: 0.85-0.99]; p < 0.001). A global stress MBF ≤1.82 ml/g/min was able to differentiate 3-vessel disease from MVD and normal with sensitivity 88% and specificity 89% (PPV 64%, NPV 97%, AUC 0.94 [95% CI: 0.82-0.99]; p < 0.001). When these cutoffs were retested within the same cohort, accuracy was 80.4% (Supplemental Figure 4). When visual analysis of first-pass perfusion images was combined with global stress MBF, the accuracy improved to 84.8% with 100% sensitivity and 92% specificity for the detection of 3-vessel disease (Figure 7).
Of the 25 patients with nonobstructive CAD (i.e., FFR >0.80 in all vessels), 5 had abnormal IMR in 1 vessel, 8 abnormal IMR in 2 vessels, and 3 abnormal IMR in 3 vessels. Global stress MBF was significantly higher in those with NCP (i.e., FFR >0.80 and IMR <25 in all 3 vessels) compared with those with at least 1 IMR-positive territory. There was a trend toward lower global stress MBF with more IMR positive vessels (global stress MBF: NCP 2.80 ± 0.67 ml/g/min, 1-vessel abnormal IMR 2.06 ± 0.36 ml/g/min, 2-vessel abnormal IMR 2.13 ± 0.23 ml/g/min, 3-vessel abnormal IMR 1.77 ± 0.27 ml/g/min, p < 0.01 between groups and p < 0.05 between NCP and 1-vessel and NCP and 3-vessel) (Supplemental Figure 5).
The present study shows that this novel automated in-line myocardial perfusion mapping technique can be used to detect epicardial CAD defined by FFR ≤0.80, MVD defined by IMR ≥25, and differentiate MVD from 3-vessel disease with good accuracy. Performance of this method for detection of physiologically significant CAD is comparable to previously published quantitative methods (16–18) and a meta-analysis of quantitative stress perfusion CMR studies (which showed per-territory sensitivity 82%, specificity 83% and AUC 0.84 for the detection of obstructive CAD) (19) with the added advantage of being able to detect coronary MVD and providing perfusion maps in-line on the scanner ready for quantitative analysis of MBF. The only other study to assess pixel-wise quantitative myocardial perfusion mapping reported per-vessel sensitivity of 75% to 83% and specificity of 72% to 81% to detect obstructive CAD (20) using quantitative coronary angiography as the reference standard rather than FFR.
We propose a potential diagnostic algorithm for the integration of automated in-line myocardial perfusion mapping into the clinical CMR workflow (Figure 8). This could provide a simple noninvasive approach for evaluating epicardial CAD and MVD in patients with angina and also differentiate 3-vessel disease from MVD. These results will need further validation in a larger multicenter study.
Detection of obstructive CAD
Myocardial regions with obstructive CAD (FFR ≤0.80) had mean stress MBF 1.47 ml/g/min and MPR 1.75. This is comparable to previous studies of quantitative perfusion CMR that report stress MBF values of 1.4 to 2.32 ml/g/min and MPR 1.20 to 1.82 in regions subtended by arteries with obstructive CAD (16,17,21,22).
Our results show lower specificity compared with previous studies using manually derived quantitative stress perfusion CMR for detection of functionally significant epicardial disease (17,18). This may be in part due to the high prevalence of MVD in our population. Within our cohort, 38% of vessels without obstructive disease had abnormal IMR and 70% of patients with nonobstructive CAD had at least 1 vessel with evidence of MVD, higher than previously reported (23,24). MVD may explain in part the poor correlation between stress MBF and FFR within FFR-negative vessels, and the improvement in diagnostic accuracy of stress MBF and MPR for the detection of FFR positive vessels once areas with abnormal IMR are removed. Given the linear relationship between stress MBF and FFR in areas with obstructive disease (FFR ≤0.80), myocardial perfusion maps could also give an indication of severity of disease and may be useful to risk-stratify patients, although future studies are required to confirm this hypothesis.
Detection of coronary MVD
In vessels with FFR >0.80, stress MBF and MPR were reduced in MVD areas. The only previous quantitative CMR study to investigate detection of MVD using IMR as the reference standard showed stress MBF values comparable to obstructive CAD (1.5 ml/g/min MVD and 1.4 ml/g/min obstructive disease) (21). Our data suggest that obstructive disease causes a greater reduction in stress MBF than MVD, which results in intermediate reduction. This is consistent with previous PET data (25). Mean global stress MBF (2.03 ml/g/min) of MVD patients in this study is comparable with PET studies of patients with clinical features of MVD (2.15 ml/g/min (26) and 2.52 ml/g/min (27)).
Differentiation of epicardial disease from coronary MVD and normal
The accurate detection of severe 3-vessel disease is critical to enable correct patient management but has been a limitation of myocardial perfusion techniques in the past because of an underestimation of ischemic burden. The proposed approach to CMR stress perfusion quantification overcomes this limitation and rapidly provides images in-line to the scanner that are easy to interpret both visually and fully quantitatively. An additional clinical challenge when there is global reduction in the stress MBF and MPR is the differentiation between 3-vessel disease and coronary MVD. Our data show that functionally significant epicardial disease displays a greater reduction in both stress MBF compared with areas with MVD and is able to differentiate 3-vessel epicardial disease from MVD and normal with excellent diagnostic accuracy.
Potential clinical implications of in-line myocardial perfusion mapping
Obstructive CAD affecting 1 or 2 vessels leads to regional perfusion defects that can be diagnosed visually by CMR with acceptable diagnostic accuracy; therefore, techniques using quantitative perfusion (previously needing time-consuming postprocessing and dedicated software) have never entered the routine clinical workflow. There remain clinical diagnostic challenges that need to be addressed, however. First, it has been demonstrated that qualitative visual analysis is not enough for the detection of significant multivessel epicardial disease (28), especially in patients with 3-vessel disease in which visual analysis may underestimate the extent of ischemia. Although this has traditionally been believed to be a problem associated with PET, it has also been shown that stress perfusion CMR only diagnoses perfusion defects in all 3 territories in up to two-thirds of patients with known obstructive 3-vessel disease (29,30). Second, we still lack a simple, widely available diagnostic tool for detection of MVD. MVD does not usually result in visible regional or global perfusion defects, and in this setting quantitative perfusion becomes invaluable. Third, once we are able to detect global reduction in stress MBF and MPR, it is essential to have a diagnostic tool that is able to accurately differentiate 3-vessel disease from MVD because patient management is drastically different. Myocardial perfusion mapping offers clinicians a diagnostic tool that may be able to address these challenges by being able to detect epicardial CAD and MVD and differentiate MVD from multivessel disease. Because this technique enables measurement of MBF at a pixel level, fully automated detection algorithms could be developed and implemented in the future, enabling categorization of studies into MVD, 3-vessel disease, or normal physiology.
The cohort studied were at high risk of CAD and predominantly men. Accepting this, the diagnostic accuracy of CMR-derived stress MBF and MPR is good, but the performance of this technique in lower risk populations requires further investigation. The proposed diagnostic algorithm provides a framework for differentiating epicardial disease from MVD and normal; however, the sample size was small, there was no independent validation sample, and no adjustments were made for MBF measurements within individuals. This algorithm therefore requires further validation in a larger cohort of patients to confirm the accuracy of the suggested cutoff values. In its current form, the technique is not yet fully automated because it requires the user to manually trace endo- and epicardial borders and to visually differentiate regional from global perfusion defects. Finally, because of the sample size, effects of confounders such as age, sex, smoking status, and presence of diabetes were not investigated. The effect of these factors on MBF warrants further investigation.
This novel automated in-line myocardial perfusion mapping technique can be used to detect epicardial CAD, MVD, and to differentiate MVD from multivessel disease. We propose a CMR-based algorithm for diagnosis of epicardial disease and MVD that could be readily implemented into clinical workflow following further clinical validation.
COMPETENCY IN MEDICAL KNOWLEDGE: Stress MBF ≤1.94 ml/g/min measured using CMR perfusion mapping is able to detect physiologically significant epicardial coronary disease (defined by FFR ≤0.80). In the absence of regional perfusion defects, global analysis of stress MBF is able to differentiate obstructive 3-vessel disease, coronary microvascular dysfunction, and normal.
TRANSLATIONAL OUTLOOK: Further research is required to fully validate a proposed CMR-based diagnostic algorithm to detect obstructive coronary disease and coronary microvascular dysfunction using myocardial perfusion mapping.
↵∗ Drs. Kotecha and Martinez-Naharro contributed equally to this work.
This study was supported by the National Amyloidosis Centre, University College London, and the National Institute for Health Research University College London Hospitals Biomedical Research Centre. All authors have reported that they have no relationships relevant to the contents of this paper to disclose.
- Abbreviations and Acronyms
- area under the curve
- coronary artery disease
- confidence interval
- cardiovascular magnetic resonance
- fractional flow reserve
- invasive coronary angiography
- index of microcirculatory resistance
- myocardial blood flow
- myocardial perfusion reserve
- microvascular dysfunction
- normal coronary physiology
- negative predictive value
- positron emission tomography
- positive predictive value
- receiver-operating characteristic
- Received October 8, 2018.
- Revision received December 3, 2018.
- Accepted December 6, 2018.
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