The Cardiac Image Modeling tool showed high correlation with the Argus tool for global ejection fraction (r2 = 0.85, P < 0.001) and 79% overall agreement for regional wall motion.
Cross-Sectional (n=31)
Blinded reviewers
Effect estimate: r2 = 0.85
p-value: p=< 0.001
OBJECTIVE: To evaluate the Cardiac Image Modeling (CIM 4.6; University of Auckland, Auckland, New Zealand) tool's ability to assess cardiac function via quantitative calculations of global and regional ejection fraction (EF) from magnetic resonance imaging in comparison with a current method of global analysis with Argus (Siemens Medical Solutions) and regional analysis with visual analysis. BACKGROUND: Global cardiac function is commonly assessed quantitatively by post processing tools that calculate global EF. Currently, regional cardiac function is assessed by subjective visual analysis of wall motion, which can have significant interobserver variability. CIM is a tool that may reduce variability by generating a semi-automated 3-dimensional heart model to calculate quantitative global and regional EF. MATERIALS AND METHODS: Thirty-one patients (22 men, 9 women; mean age 55.1 +/- 17.5 years) were selected based on global EFs calculated at the time of the clinical visit with the Argus postprocessing tool (Siemens Medical Solutions). Patients were then placed into 2 predetermined categories of normal: EF >or=50% and abnormal: EF or=50%) and abnormal (EF 0.60. Regional wall motion by short axis slices showed pa averages >0.75, and combined analyses of all 3 reviewers' 16-segment regional data showed an overall total p(a) = 0.79 (sensitivity = 72%, specificity = 88%). Interobserver and intraobserver variability were low (p(a) > 0.65) in this study. CONCLUSIONS: Global EF analysis of cardiac magnetic resonance imaging by CIM showed high agreement with the commonly used Argus postprocessing tool. Furthermore, CIM is capable of evaluating regional EF with good agreement in comparison with the current visual method. In addition to determining abnormal versus normal cardiac wall motion, CIM is able to add to the analysis a quantitative regional EF for each given segment. As a semi-automated tool, CIM has the potential to reduce reviewer variability and decrease the time required for analysis. In the future, CIM can potentially quantitatively track global and regional changes in patients with heart disease and aid the clinical management throughout the course of the disease.
Hung et al. (Fri,) conducted a cross-sectional in Cardiac dysfunction (n=31). Cardiac Image Modeling (CIM 4.6) tool vs. Argus postprocessing tool and visual analysis was evaluated on Correlation of global ejection fraction between Argus and CIM analyses (r2 = 0.85, p=< 0.001). The Cardiac Image Modeling tool showed high correlation with the Argus tool for global ejection fraction (r2 = 0.85, P < 0.001) and 79% overall agreement for regional wall motion.