European Journal of Echocardiography Advance Access originally published online on September 1, 2008
European Journal of Echocardiography 2009 10(4):485-490; doi:10.1093/ejechocard/jen209
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Semi-automated analysis of dynamic changes in myocardial contrast from real-time three-dimensional echocardiographic images as a basis for volumetric quantification of myocardial perfusion
1 Department of Electronics, Computer Science and Systems, Università di Bologna, Bologna, Italy;
2 Department of Biomedical Engineering, Politecnico di Milano, Milan, Italy
3 Department of Medicine, Section of Cardiology, University of Chicago Medical Center, Chicago, Illinois, USA
Received 7 April 2008; accepted after revision 14 July 2008; online publish-ahead-of-print 1 September 2008.
* Corresponding author. Tel: +1 773 702 1842; fax: +1 773 702 1034.E-mail address: vmoravi{at}medicine.bsd.uchicago.edu
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Abstract |
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Aims: Despite the potential of real-time three-dimensional (3D) echocardiography (RT3DE) to assess myocardial perfusion, there is no quantification method available for perfusion analysis from RT3DE images. Such method would require 3D regions of interest (ROI) to be defined and adjusted frame-by-frame to compensate for cardiac translation and deformation. Our aims were to develop and test a technique for automated identification of 3D myocardial ROI suitable for translation-free quantification of myocardial videointensity over time, MVI(t), from contrast-enhanced RT3DE images.
Methods and results: Twelve transthoracic RT3DE (Philips) data sets obtained in pigs during transition from no contrast to steady-state enhancement (Definity) were analysed using custom software. Analysis included: (i) semi-automated detection of left ventricular endo- and epicardial surfaces using level-set techniques in one frame to define a 3D myocardial ROI, (ii) rigid 3D registration to reduce translation and rotation, (iii) elastic 3D registration to compensate for deformation, and (iv) quantification of MVI(t) in the 3D ROI from the registered and non-registered data sets to assess the effectiveness of registration. For each MVI(t) curve we computed % variability during steady-state enhancement (100 x SD/mean) and goodness of fit (r2) to the indicator dilution equation MVI(t) = A[1–exp(–βt)]. Analysis of myocardial contrast throughout contrast inflow was feasible in all data sets. Three-dimensional registration improved MVI(t) curves in terms of both % variability (2.8 ± 1.8 to 1.5 ± 0.9%; P < 0.05) and goodness of fit (r2 from 0.79 ± 0.2 to 0.90 ± 0.1; P < 0.05).
Conclusion: This is the first study to describe a new technique for semi-automated volumetric quantification of myocardial contrast from RT3DE images that includes registration and thus provides the basis for 3D measurement of myocardial perfusion.
Keywords: Real-time 3D echocardiography; Myocardial perfusion; Image registration
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