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

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 x 1.4 mm(2) was achieved by using 5x 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

Investigation into diagnostic accuracy of common strategies for automated perfusion motion correction

Respiratory motion is a significant obstacle to the use of quantitative perfusion in clinical practice. Increasingly complex motion correction algorithms are being developed to correct for respiratory motion. However, the impact of these improvements on the final diagnosis of ischemic heart disease has not been evaluated. The aim of this study was to compare the performance of four automated correction methods in terms of their impact on diagnostic accuracy. Three strategies for motion correction were used: (1) independent translation correction for all slices, (2) translation correction for the basal slice with transform propagation to the remaining two slices assuming identical motion in the remaining slices, and (3) rigid correction (translation and rotation) for the basal slice. There were no significant differences in diagnostic accuracy between the manual and automatic motion-corrected datasets (p=0.88). The area under the curve values for manual motion correction and automatic motion correction were 0.93 and 0.92, respectively. All of the automated motion correction methods achieved a comparable diagnostic accuracy to manual correction. This suggests that the simplest automated motion correction method (method 2 with translation transform for basal location and transform propagation to the remaining slices) is a sufficiently complex motion correction method for use in quantitative myocardial perfusion