Registration of Coronary MRA to DCE-MRI Myocardial Perfusion Series Improves Diagnostic Accuracy Through the Computation of Patient-Specific Coronary Supply Territories: A CE-MARC Sub-Study

Background: It is generally acknowledged that the 17-segment AHA model provides a suitable approximation for mapping the results of X-ray angiography onto myocardial anatomy in a consistent way in the absence of a more exact method. In practice, coronary anatomy varies from patient to patient which is acknowledged as the main limitation of the AHA model. The aim of this study was to establish whether the generation of a patient-specific coronary artery to perfusion segment map improved diagnosis of myocardial ischaemia

Automated Registration of Dynamic Contrast Enhanced DCE-MRI Cardiac Perfusion Achieves Comparable Diagnostic Accuracy to Manual Motion Correction: a CE-MARC sub-study

The human interaction required for manual motion correction/contouring of cardiac perfusion series remains a significant obstacle to quantitative perfusion gaining a wider acceptance in clinical practice. The use of image registration for motion correction in perfusion data offers a considerable time saving. Numerous registration methods have been proposed, with evaluation limited to the image registration accuracy. However, the important clinical question is how do these methods affect diagnosis? The aim of this study is to evaluate perfusion series registration in terms of its affect on the diagnostic accuracy of myocardial ischaemia

A Comparison of Methods for Automated Motion Correction of DCE-MRI Perfusion Datasets Evaluated in Terms of Diagnostic Accuracy: A CE-MARC sub-study

Automated mage registration in cardiac myocardial perfusion is a necessity before quantitative perfusion can be widely accepted in clinical practice. Increasingly complex motion correction algorithms are being developed to deal with cardiac motion. However, the impact of these improvements has not been evaluated in terms of the final clinical diagnosis. Advanced motion correction methods are associated with increased computational overhead and the potential of introducing subtle registration errors, which can be hard to detect and quantify. The aim of this study was to compare the performance of the various automated correction methods in terms of their impact on diagnostic accuracy

Susceptibility-weighted cardiovascular magnetic resonance in comparison to T2 and T2 star imaging for detection of intramyocardial hemorrhage following acute myocardial infarction at 3 Tesla

BACKGROUND:Intramyocardial hemorrhage (IMH) identified by cardiovascular magnetic resonance (CMR) is an established prognostic marker following acute myocardial infarction (AMI). Detection of IMH by T2-weighted or T2 star CMR can be limited by long breath hold times and sensitivity to artefacts, especially at 3T. We compared the image quality and diagnostic ability of susceptibility-weighted magnetic resonance imaging (SW MRI) with T2-weighted and T2 star CMR to detect IMH at 3T.METHODS:Forty-nine patients (42 males; mean age 58years, range 35-76) underwent 3T cardiovascular magnetic resonance (CMR) 2days following re-perfused AMI. T2-weighted, T2 star and SW MRI images were obtained. Signal and contrast measurements were compared between the three methods and diagnostic accuracy of SW MRI was assessed against T2w images by 2 independent, blinded observers. Image quality was rated on a 4-point scale from 1 (unusable) to 4 (excellent).RESULTS:Of 49 patients, IMH was detected in 20 (41%) by SW MRI, 21 (43%) by T2-weighted and 17 (34%) by T2 star imaging (p=ns). Compared to T2-weighted imaging, SW MRI had sensitivity of 93% and specificity of 86%. SW MRI had similar inter-observer reliability to T2-weighted imaging (kappa=0.90 and kappa=0.88 respectively); both had higher reliability than T2 star (kappa=0.53). Breath hold times were shorter for SW MRI (4seconds vs. 16seconds) with improved image quality rating (3.8+/-0.4, 3.3+/-1.0, 2.8+/-1.1 respectively; p<0.01).CONCLUSIONS:SW MRI is an accurate and reproducible way to detect IMH at 3T. The technique offers considerably shorter breath hold times than T2-weighted and T2 star imaging, and higher image quality scores

Evaluation of an automated method for arterial input function detection for first-pass myocardial perfusion cardiovascular magnetic resonance

ABSTRACT: Background: Quantitative assessment of myocardial blood flow (MBF) with first-pass perfusion cardiovascular magnetic resonance (CMR) requires a measurement of the arterial input function (AIF). This study presents an automated method to improve the objectivity and reduce processing time for measuring the AIF from first-pass perfusion CMR images. This automated method is used to compare the impact of different AIF measurements on MBF quantification.Methods: Gadolinium-enhanced perfusion CMR was performed on a 1.5 T scanner using a saturation recovery dual-sequence technique. Rest and stress perfusion series from 270 clinical studies were analyzed. Automated image processing steps included motion correction, intensity correction, detection of the left ventricle (LV), independent component analysis, and LV pixel thresholding to calculate the AIF signal. The results were compared with manual reference measurements using several quality metrics based on the contrast enhancement and timing characteristics of the AIF. The median and 95 % confidence interval (CI) of the median were reported. Finally, MBF was calculated and compared in a subset of 21 clinical studies using the automated and manual AIF measurements.Results: Two clinical studies were excluded from the comparison due to a congenital heart defect present in one and a contrast administration issue in the other. The proposed method successfully processed 99.63 % of the remaining image series. Manual and automatic AIF time-signal intensity curves were strongly correlated with median correlation coefficient of 0.999 (95 % CI [0.999, 0.999]). The automated method effectively selected bright LV pixels, excluded papillary muscles, and required less processing time than the manual approach. There was no significant difference in MBF estimates between manually and automatically measured AIFs (p = NS). However, different sizes of regions of interest selection in the LV cavity could change the AIF measurement and affect MBF calculation (p = NS to p = 0.03).Conclusion: The proposed automatic method produced AIFs similar to the reference manual method but required less processing time and was more objective. The automated algorithm may improve AIF measurement from the first-pass perfusion CMR images and make quantitative myocardial perfusion analysis more robust and readily available