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CLINICAL RESEARCH: CARDIAC IMAGING

Cardiovascular Magnetic Resonance Perfusion Imaging at 3-Tesla for the Detection of Coronary Artery Disease

A Comparison With 1.5-Tesla

Adrian S.H. Cheng, MBBS, MRCP^_*,, Tammy J. Pegg, MBChB, MRCP^_*, Theodoros D. Karamitsos, MD^_*,, Nick Searle, DCR(R), Michael Jerosch-Herold, PhD^||, Robin P. Choudhury, DM, MRCP, Adrian P. Banning, MD, FRCP, FESC, Stefan Neubauer, MD, FRCP^_*,, Matthew D. Robson, PhD^_*, and Joseph B. Selvanayagam, DPhil, FRACP, FESC^_*,,_*

^_* University of Oxford Centre for Clinical Magnetic Resonance Research, Oxford, United Kingdom
Department of Cardiovascular Medicine, University of Oxford, Oxford, United Kingdom
Department of Radiology
Department of Cardiology, John Radcliffe Hospital, Oxford, United Kingdom; and the ||Advanced Imaging Research Center, Oregon Health & Science University, Portland, Oregon

Manuscript received September 21, 2006; revised manuscript received March 2, 2007, accepted March 6, 2007.

^_* Reprint requests and correspondence: Dr. Joseph B. Selvanayagam, University of Oxford, Cardiovascular Medicine, Headley Way, Headington, Oxford, United Kingdom (Email: joseph.selvanayagam{at}cardiov.ox.ac.uk).

Objectives: This study was designed to establish the diagnostic accuracy of cardiovascular magnetic resonance (CMR) perfusion imaging at 3-Tesla (T) in suspected coronary artery disease (CAD).

Background: Myocardial perfusion imaging is considered one of the most compelling applications for CMR at 3-T. The 3-T systems provide increased signal-to-noise ratio and contrast enhancement (compared with 1.5-T), which can potentially improve spatial resolution and image quality.

Methods: Sixty-one patients (age 64 ± 8 years) referred for elective diagnostic coronary angiography (CA) for investigation of exertional chest pain were studied (before angiogram) with first-pass perfusion CMR at both 1.5- and 3-T and at stress (140 µg/kg/min intravenous adenosine, Adenoscan, Sanofi-Synthelabo, Guildford, United Kingdom) and rest. Four short-axis images were acquired during every heartbeat using a saturation recovery fast-gradient echo sequence and 0.04 mmol/kg Gd-DTPA bolus injection. Quantitative CA served as the reference standard. Perfusion deficits were interpreted visually by 2 blinded observers. We defined CAD angiographically as the presence of 1 stenosis of 50% diameter in any of the main epicardial coronary arteries or their branches with a diameter of 2 mm.

Results: The prevalence of CAD was 66%. All perfusion images were found to be visually interpretable for diagnosis. We found that 3-T CMR perfusion imaging provided a higher diagnostic accuracy (90% vs. 82%), sensitivity (98% vs. 90%), specificity (76% vs. 67%), positive predictive value (89% vs. 84%), and negative predictive value (94% vs. 78%) for detection of significant coronary stenoses compared with 1.5-T. The diagnostic performance of 3-T perfusion imaging was significantly greater than that of 1.5-T in identifying both single-vessel disease (area under receiver-operator characteristic [ROC] curve: 0.89 ± 0.05 vs. 0.70 ± 0.08; p < 0.05) and multivessel disease (area under ROC curve: 0.95 ± 0.03 vs. 0.82 ± 0.06; p < 0.05). There was no difference between field strengths for the overall detection of coronary disease (area under ROC curve: 0.87 ± 0.05 vs. 0.78 ± 0.06; p = 0.23).

Conclusions: Our study showed that 3-T CMR perfusion imaging is superior to 1.5-T for prediction of significant single- and multi-vessel coronary disease, and 3-T may become the preferred CMR field strength for myocardial perfusion assessment in clinical practice.

Abbreviations and Acronyms   CAD = coronary artery disease   CMR = cardiovascular magnetic resonance   CNR = contrast-to-noise ratio   IV = intravenous   LAD = left anterior descending   LV = left ventricle   MI = myocardial infarction   PET = positron emission tomography   RCA = right coronary artery   ROC = receiver operating characteristic   ROI = region of interest   SI = signal intensity   SNR = signal-to-noise ratio   SPECT = single-photon emission computed tomography   T = Tesla

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