Semi-Automated Segmentation and Registration of Triggered 3D Echocardiographic Images as a Basis for Volumetric Analysis of Myocardial Perfusion

We developed a technique for automated identification of 3D myocardial ROI suitable for translation-free quantification of myocardial videointensity over time, MVI(t), from RT3DE images. Our software was tested on 12 ECG-triggered RT3DE datasets obtained in pigs during transient contrast inflow. Analysis included: (1) semi-automated detection of endo- and epicardial surfaces using level-set techniques to define a 3D myocardial ROI, (2) rigid 3D registration to reduce translation and rotation, (3) elastic 3D registration to compensate for deformation, and (4) quantification of MVI(t) with and without registration to assess its effectiveness. Analysis of myocardial contrast throughout contrast inflow was feasible in all datasets. 3D registration improved MVI(t) curves in terms of both % variability during steady-state enhancement: 2.8±1.8% to 1.5±0.9%, and goodness of fit to the indicator dilution equation MVI(t)=A•(1-exp(-􀈕t)): r² from 0.79±0.2 to 0.90±0.1. 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.