Premium
Multicenter trial of automated border detection in cardiac MR imaging
Author(s) -
Fleagle Steven R.,
Thedens Daniel R.,
Stanford William,
Pettigrew Roderic I.,
Reichek Nathaniel,
Skorton David J.
Publication year - 1993
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.1880030217
Subject(s) - robustness (evolution) , computer vision , computer science , artificial intelligence , magnetic resonance imaging , short axis , cardiac magnetic resonance , radiology , pattern recognition (psychology) , medicine , mathematics , long axis , biochemistry , chemistry , geometry , gene
The purpose of the present study was to evaluate the robustness of a method of automated border detection in cardiac magnetic resonance (MR) imaging. Thirty‐seven short‐axis spin‐echo cardiac images were acquired from three medical centers, each with its own image‐acquisition protocol. Endo‐ and epicardial borders and areas were derived from these images with a graph‐searching‐based method of edge detection. Computer results were compared with observer‐traced borders. The method accurately defined myocardial borders in 36 of 37 images (97%), with excellent agreement between computer‐ and observer‐derived endocardial and epicardial areas (correlation coefficients,.94‐.99). The algorithm worked equally well for data from all three centers, despite differences in image‐acquisition protocols, MR systems, and field strengths. These data suggest that a method of computer‐assisted edge detection based on graphsearching principles yields endocardial and epicardial areas that correlate well with those derived by an independent observer.