4 Quantitative myocardial perfusion with simultaneous-multi-slice stress CMR: validation against invasive anatomical and physiological coronary angiography

Introduction

Stress perfusion cardiovascular magnetic resonance (CMR) has high diagnostic accuracy for coronary artery disease (CAD). However, routine stress perfusion CMR lacks complete spatial coverage of the heart, which may underestimate ischaemia. We recently developed methods to achieve high-resolution near whole-heart coverage myocardial perfusion with absolute myocardial blood flow (MBF) quantification. The objective of this study was to determine the diagnostic accuracy of stress MBF derived from high-resolution, simultaneous multi slice (SMS) myocardial perfusion CMR for detection of significant CAD defined by invasive coronary angiography (ICA) and fractional flow reserve (FFR).

Materials and Methods

Thirty-eight patients (14 female, mean age 61±11 years) with suspected CAD underwent SMS accelerated adenosine stress first-pass contrast enhanced CMR perfusion imaging at 1.5T MRI. Absolute stress MBF was derived. Visual analysis of the dynamic perfusion series was undertaken by two expert CMR readers. Diagnostic accuracy was determined on presence of significant CAD as defined by ICA and FFR ≤ 0.80 using Area under the Curve (AUC) from receiver operator characteristic curves.

Results

At the patient level, there was significantly lower MBF in patients with CAD compared to those without CAD (2.03 [1.82 – 2.37] vs 2.68 [2.31 – 2.93] ml/g/min, p<0.001). There was a high diagnostic accuracy for the detection of CAD for quantitative stress MBF with AUC 0.84 (95% confidence interval [CI] 0.68–0.94, p<0.001), and for visual analysis with AUC 0.89 (95% CI: 0.75 – 0.97), p<0.0001). These were not significantly different (p=0.52). The optimal threshold for MBF detection of CAD was ≤2.50 ml/g/min, with sensitivity of 100% (95% CI: 77%-100%) and specificity of 71% (95% CI: 49%-87%).

Conclusion

High-resolution quantitative near whole-heart myocardial perfusion imaging shows good diagnostic accuracy for detection of significant CAD, with comparable accuracy compared to expert visual analysis. This technique could be considered for automated widespread clinical use without the need for expert analysis of visual dynamic perfusion images.

Acknowledgements

The authors acknowledge financial support from the Department of Health through the National Institute for Health Research (NIHR) comprehensive Biomedical Research Centre award to Guy’s & St Thomas’ NHS Foundation Trust in partnership with King’s College London and King’s College Hospital NHS Foundation Trust and by the NIHR MedTech Co-operative for Cardiovascular Disease at Guy’s and St Thomas’ NHS Foundation Trust. This paper presents independent research funded by the National Institute for Health Research (NIHR) under its Research for Patient Benefit (RfPB) Programme (Grant Reference Number PB-PG-0416–20008). The work was also supported by the EPSRC (EP/P001009/1, EP/R010935/1, and EP/L015226/1) and the Wellcome/EPSRC Centre for Medical Engineering [WT 203148/Z/16/Z]. MSN was funded by a NIHR Clinical Lectureship [CL-2019–17–001]. SP is funded by a BHF Chair [CH/16/2/32089]. The views expressed are those of the authors and not necessarily those of the BHF, the DoH, the EPSRC, the NHS, the NIHR, or the Wellcome Trust.