Volume 18 Supplement 1
How well do individual first pass perfusion images correlate with fully quantitative myocardial blood flow pixel maps?
© Timoh et al. 2016
Published: 27 January 2016
Quantitative pixel mapping in cardiac magnetic resonance (CMR) has been shown to be an accurate estimate of myocardial blood flow (MBF) in perfusion imaging when compared to a microsphere reference standard. We hypothesize that there is a time period during contrast enhancement when individual perfusion images correlate closely with MBF pixel maps.
27 subjects (17 male; age 62 ± 11 years) with coronary artery disease as defined by quantitative coronary angiography were included in this study. Greater than 70% luminal coronary stenosis of the major epicardial coronaries was present in 2/3rd of the subjects. Perfusion imaging was performed using a saturation recovery steady-state free precession dual-sequence method at 1.5 T during Regadenoson vasodilator stress and at rest, using 0.05 mmol/kg Gadolinium-DTPA. Pixel-wise MBF maps were calculated from a mid-ventricular motion-corrected image series. Myocardial regions of interest were manually traced on the perfusion image series to restrict analysis to the myocardium. The MBF pixel values were then correlated with individual perfusion image signal intensities on a pixel-by-pixel basis for each perfusion image in the series (Pearson's correlation coefficient).
For stress myocardial perfusion images, the average time from start of myocardial enhancement to peak myocardial signal intensity was 7.4 ± 2.2 seconds. The highest correlation between stress perfusion images and MBF pixel maps occurred at 4.5 ± 1.9 seconds after the start of myocardial enhancement and had an R value of 0.80 ± 0.08. The correlation remained within 5 percent of peak correlation for 2.8 ± 1.7 seconds.
There is a characteristic and consistent time period during contrast enhancement on the perfusion images that correlate best to the quantitative MBF perfusion pixel maps. The correlation, as expected, is higher with stress perfusion imaging than rest perfusion imaging. It may be possible to target perfusion images in this time period to improve the efficiency of clinical image interpretation.
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.