Automated Identification of Left Ventricular Borders from Spin-Echo Magnetic Resonance Images Experimental and Clinical Feasibility Studies

Abstract

Gated cardiac magnetic resonance imaging (MRI) permits detailed evaluation of cardiac anatomy, including the calculation of left ventricular volume and mass. Current methods of deriving this information, however, require manual tracing of boundaries in several images; such manual methods are tedious, time consuming, and subjective. The purpose of this study is to apply a new computerized method to automatically identify endocardial and epicardial borders in MRIs. The authors obtained serial, short-axis, spin-echo MRIs of 13 excised animal hearts. Also obtained were selected short-axis, spin-echo ventricular images of 11 normal human volunteers. A method of automated edge detection based on graph-searching principles was applied to the ex vivo and in vivo images. Endocardial and epicardial areas were used to compute left ventricular mass and were compared with the anatomic left ventricular mass for the images of excised hearts. The endocardial and epicardial areas calculated from computer-derived borders were compared with areas from observer tracing. There was very close correspondence between computer-derived and observer tracings for excised hearts (r = 0.97 for endocardium, r = 0.99 for epicardium) and in vivo scans (r = 0.92 for endocardium, r = 0.90 for epicardium). There also was a close correspondence between computer-generated and actual left ventricular mass in the excised hearts (r = 0.99). These data suggest the feasibility of automated edge detection in MRIs. Although further validation is needed, this method may prove useful in clinical MRI.