Quantitative Perfusion
Muhummad Sohaib Nazir, PhD
NIHR Clinical Lecturer in Cardiology
King's College London, United Kingdom
Muhummad Sohaib Nazir, PhD
NIHR Clinical Lecturer in Cardiology
King's College London, United Kingdom
Avan Suinesiputra
Post doctoral researcher
King's College London, United Kingdom
Matthew Ryan, PhD
Clinical Lecturer
King's College London, United Kingdom
Sarah McElroy, PhD
Clinical scientist
Siemens Healthcare Limited
London, England, United Kingdom
Aurélien Bustin, PhD
Junior Professor
IHU LIRYC
Bordeaux, Aquitaine, France
Xenios Milidonis, PhD
Post doctoral researcher
King's College London
London, England, United Kingdom
Momina Yazdani, BMSc
Clinical Research Fellow
King's College London, United Kingdom
Reza Hajjhosseiny, MD
Clinical Research Fellow
King's College London, United Kingdom
Elsa-marie Totoo, MSc
PhD student
King's College London, United Kingdom
Daniel Stäb, PhD
Senior Scientist
Siemens Healthcare Pty Ltd, Melbourne, Australia
Melbourne, Victoria, Australia
Peter Speier, PhD
Principal Key Expert
Magnetic Resonance, Siemens Healthcare GmbH, Erlangen, Germany
Erlangen, Bayern, Germany
Karl P. Kunze, PhD
Senior Cardiac MR Scientist
Siemens Healthineers
London, England, United Kingdom
Radhouene Neji, PhD
Siemens Research Scientist
King's College London, United Kingdom
Divaka Perera, MD, PhD
Professor of Cardiology
King's College London, England, United Kingdom
Sven Plein, MD, PhD
Professor
University of Leeds
Leeds, England, United Kingdom
Sébastien Roujol, PhD
Reader in Medical Imaging
King's College London
London, England, United Kingdom
René M. Botnar, PhD
Professor
King's College London
London, England, United Kingdom
Claudia Prieto, PhD
Professor
King's College London
London, United Kingdom
Amedeo Chiribiri, MD PhD FHEA FSCMR
Professor of Cardiovascular Imaging; Consultant Cardiologist
King's College London
London, England, United Kingdom
Quantitative stress myocardial blood flow (MBF) has a good diagnostic accuracy for coronary artery disease (CAD) but does not derive anatomical data on atherosclerotic plaque. High-resolution coronary magnetic resonance angiography (CMRA) may provide incremental diagnostic value for patients with CAD. The objective of this study was to compare the diagnostic accuracy of stress perfusion alone versus an ‘all-in-one cardiac MR (CMR) approach’ with stress perfusion + CMRA against invasive coronary angiography (ICA) and fractional flow reserve (FFR).
Methods:
Single center, prospective cohort study of patients with suspected CAD who underwent an additional CMR scan. Imaging was performed on a 1.5T MRI scanner (MAGNETOM Aera, Siemens Healthcare, Erlangen, Germany). Prior to high-resolution CMRA, patients were administered with sublingual glyceryl trinitrate and intravenous beta blockers and CMRA was acquired using an image navigated submillimeter resolution prototype with 100% respiratory scan efficiency. (1) Stress perfusion was performed using intravenous adenosine administered at 140mcg/kg/min prior to single bolus 0.075mmol/kg gadobutrol (Gadovist, Bayer, Germany) contrast. Stress perfusion was acquired using a prototype simultaneous multi slice sequence as previously described and quantified using Fermi-constrained deconvolution. (2)
Significant CAD was defined by a stenosis ≥70% or fractional flow reserve ≤0.80. Using the ‘all-in-one CMR approach’, stress MBF was reclassified according to the presence or absence of CAD on CMRA on a patient and territory level. Diagnostic accuracy was determined by receiver operator characteristic curves and compared using DeLong’s test.
Results:
Patients (n=34, mean age 62±10, 15 female), with CAD prevalence of 35%, underwent CMR within median time interval of 7 days (IQR 1-20) to ICA.
On a patient-level, there was high diagnostic accuracy for CAD detection with stress MBF alone (area under the curve (AUC) 0.82 [95% CI 0.68-0.96]) and combined stress MBF and CMRA (AUC was 0.85 [95% CI 0.72-0.97]), although not statistically different (p=0.71). (Figure 1).
On a territory-level, the diagnostic accuracy for combined stress MBF and CMRA (AUC 0.87 [95% CI 0.78-0.96]) was higher compared to stress MBF alone (AUC 0.71 [95% CI: 0.58-0.85]), p=0.037. (Figure 2).
Conclusion:
Using this comprehensive all-in-one CMR approach, anatomy (CMRA), ischemia (stress MBF) and viability (late gadolinium enhancement) can be obtained from a single exam and improves diagnostic accuracy on a territory-level compared to stress MBF alone. This technique may provide a single comprehensive scan to aid clinical decision making and management in CAD patients through initiation of preventative medical therapy and patient-specific strategies for potential revascularization.