NDLI: Real-time three-dimensional echocardiographic evaluation of global and regional left ventricular function

Content Provider	IEEE Xplore Digital Library
Author	Corsi, C. Lang, R.M. Veronesi, F. Weinert, L. Caiani, E.G. MacEneaney, P. Lamberti, C. Mor-Avi, V.
Copyright Year	2005
Description	Author affiliation: Noninvasive Cardiac Imaging Lab., Chicago Univ., IL (Corsi, C.; Lang, R.M.)
Abstract	Quantification of left ventricular (LV) function from the analysis of real-time 3D echocardiographic (RT3DE) data does not fully exploit the dynamic volumetric information contained in these data. Accordingly, we developed a volumetric analysis technique aimed at quantification of global and regional LV function. RT3DE images were analyzed using custom software to extract global and partial volumes and regional wall motion (RWM) throughout the cardiac cycle. RT3DE were compared point-by-point with magnetic resonance (MR) data (linear regression and Bland-Altman analysis) and the feasibility of automated detection of RWM abnormalities was tested by comparison with expert reading of 2D echocardiographic images. LV volumes correlated highly with MR with minimal bias and narrow limits of agreement. RWM correlated highly in basal and mid segments. The automated detection of RWM agreed with expert interpretation in 86% of LV segments. Volumetric analysis of RT3DE data is clinically feasible and allows fast and accurate assessment of cardiac function and automated detection of RWM abnormalities
Starting Page	133
Ending Page	136
File Size	365541
Page Count	4
File Format	PDF
ISBN	0780393376
DOI	10.1109/CIC.2005.1588052
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-09-25
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Magnetic analysis Image analysis Image segmentation Information analysis Image motion analysis Data mining Motion analysis Magnetic resonance Linear regression Automatic testing
Content Type	Text
Resource Type	Article

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