Automated threshold-based 3D segmentation versus short-axis planimetry for assessment of global left ventricular function with dual-source MDCT.
OBJECTIVE
The purpose of this study was to evaluate software for threshold-based 3D segmentation of the left ventricle in comparison with traditional 2D short axis-based planimetry (Simpson method) for measurement of left ventricular (LV) volume and global function with state-of-the-art dual-source CT.


METHODS
Fifty patients with known or suspected coronary artery disease underwent coronary CT angiography. LV end-diastolic, end-systolic, and stroke volumes and ejection fraction were determined from axial images to which 3D segmentation had been applied and from short-axis reformations from 2D planimetry. Interobserver variability was assessed for both approaches.


RESULTS
Threshold-based 3D LV segmentation had excellent correlation with 2D short-axis results (end-diastolic volume, R = 0.99; end-systolic volume, R = 0.99; stroke volume, R = 0.90; ejection fraction, R = 0.97; p < 0.0001). Bland-Altman analyses revealed systematic underestimation of LV end-diastolic volume (-7.4 +/- 8.9 mL) and LV end-systolic volume (-7.0 +/- 4.4 mL) with the 3D segmentation approach and 2.8 +/- 3.3% overestimation of LV ejection fraction. Interobserver variation with 3D segmentation analysis was significantly (p < 0.001) less (e.g., LV ejection fraction, 0.1 +/- 1.7%) than with the 2D technique, and mean analysis time was significantly shorter (172 +/- 20 vs 248 +/- 29 seconds; p < 0.05).


CONCLUSIONS
Automated threshold-based 3D segmentation enables accurate and reproducible dual-source CT assessment of LV volume and function with excellent correlation with results of 2D short-axis analysis. Exclusion of papillary muscles from LV volume results in small systematic differences in quantitative values.
Juergens
Kai Uwe
KU
Department of Clinical Radiology, University of Muenster, D-48149 Muenster, Germany. kujuerg@uni-muenster.de
Seifarth
Harald
H
Range
Felix
F
Wienbeck
Susanne
S
Wenker
Mirja
M
Heindel
Walter
W
Fischbach
Roman
R
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001185:Artificial Intelligence; D003324:Coronary Artery Disease / Q000150:complications / Q000000981:diagnostic imaging; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000295:instrumentation / Q000379:methods*; D018487:Ventricular Dysfunction, Left / Q000000981:diagnostic imaging* / Q000209:etiology