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Journal of Cardiovascular Magnetic Resonance

, 18:17

First Online: 08 April 2016Received: 25 November 2015Accepted: 29 March 2016


BackgroundQuantitative 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.

MethodsGadolinium-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.

ResultsTwo 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.

ConclusionThe 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.

KeywordsCardiovascular magnetic resonance Myocardial perfusion imaging Arterial input function AbbreviationsAIFarterial input function

AUarbitrary units

CIconfidence interval

CMRcardiovascular magnetic resonance

CPUcentral processing unit

FLASHfast low-angle shot

FWHMfull width at half maximum

ICAindependent component analysis

IDLinteractive data language

LVleft ventricle

MBFmyocardial blood flow

NHLBINational Heart, Lung and Blood Institute

NSnot statistically significant

PDproton density

PVpeak value

RMSEroot mean square error

ROIregion of interest

RVright ventricle

SSFPsteady-state free precession

TTPtime to peak

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Autor: Matthew Jacobs - Mitchel Benovoy - Lin-Ching Chang - Andrew E. Arai - Li-Yueh Hsu

Fuente: https://link.springer.com/article/10.1186/s12968-016-0239-0

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