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

Journal of Cardiovascular Magnetic Resonance

Research

Lin-Ching Chang¹  Li-Yueh Hsu²  Andrew E. Arai²  Mitchel Benovoy³  Matthew Jacobs⁴ 

[1] Department of Electrical Engineering and Computer Science, Catholic University of America, Washington DC, USA;National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA;National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA;Department of Biomedical Engineering, Ecole Polytechnique de Montreal, Montreal, Canada;National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA;Department of Electrical Engineering and Computer Science, Catholic University of America, Washington DC, USA;

关键词: Cardiovascular magnetic resonance;    Myocardial perfusion imaging;    Arterial input function;   

DOI  :  10.1186/s12968-016-0239-0

 received in 2015-11-25, accepted in 2016-03-29,  发布年份 2016

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© Jacobs et al. 2016