High spatial resolution myocardial perfusion cardiac magnetic resonance for the detection of coronary artery disease

Eur Heart J. 2008 Sep;29(17):2148-55. doi: 10.1093/eurheartj/ehn297. Epub 2008 Jul 18.

Abstract

Aims: To evaluate the feasibility and diagnostic performance of high spatial resolution myocardial perfusion cardiac magnetic resonance (perfusion-CMR).

Methods and results: Fifty-four patients underwent adenosine stress perfusion-CMR. An in-plane spatial resolution of 1.4 × 1.4 mm(2) was achieved by using 5× k-space and time sensitivity encoding (k-t SENSE). Perfusion was visually graded for 16 left ventricular and two right ventricular (RV) segments on a scale from 0 = normal to 3 = abnormal, yielding a perfusion score of 0-54. Diagnostic accuracy of the perfusion score to detect coronary artery stenosis of >50% on quantitative coronary angiography was determined. Sources and extent of image artefacts were documented. Two studies (4%) were non-diagnostic because of k-t SENSE-related and breathing artefacts. Endocardial dark rim artefacts if present were small (average width 1.6 mm). Analysis by receiver-operating characteristics yielded an area under the curve for detection of coronary stenosis of 0.85 [95% confidence interval (CI) 0.75-0.95] for all patients and 0.82 (95% CI 0.65-0.94) and 0.87 (95% CI 0.75-0.99) for patients with single and multi-vessel disease, respectively. Seventy-four of 102 (72%) RV segments could be analysed.

Conclusion: High spatial resolution perfusion-CMR is feasible in a clinical population, yields high accuracy to detect single and multi-vessel coronary artery disease, minimizes artefacts and may permit the assessment of RV perfusion.

Publication types

  • Comparative Study
  • Evaluation Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Coronary Angiography
  • Coronary Artery Disease / diagnosis*
  • Feasibility Studies
  • Female
  • Humans
  • Magnetic Resonance Angiography / methods
  • Male
  • Middle Aged
  • Myocardial Perfusion Imaging / methods
  • Observer Variation
  • ROC Curve
  • Sensitivity and Specificity