Accuracy and Test-Retest Reproducibility of Two-Dimensional Knowledge-Based Volumetric Reconstruction of the Right Ventricle in Pulmonary Hypertension
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Background: Right heart function is the key determinant of symptoms and prognosis in pulmonary hypertension (PH), but the right ventricle has a complex geometry that is challenging to quantify by two-dimensional (2D) echocardiography. A novel 2D echocardiographic technique for right ventricular (RV) quantitation involves knowledge-based reconstruction (KBR), a hybrid of 2D echocardiography-acquired coordinates localized in three-dimensional space and connected by reference to a disease-specific RV shape library. The aim of this study was to determine the accuracy of 2D KBR against cardiac magnetic resonance imaging in PH and the test-retest reproducibility of both conventional 2D echocardiographic RV fractional area change (FAC) and 2D KBR.
Methods: Twenty-eight patients with PH underwent same-day echocardiography and cardiac magnetic resonance imaging. Two operators performed serial RV FAC and 2D KBR acquisition and postprocessing to assess inter- and intraobserver test-retest reproducibility.
Results: Bland-Altman analysis (mean bias ± 95% limits of agreement) showed good agreement for end-diastolic volume (3.5 ± 25.0 mL), end-systolic volume (0.9 ± 19.9 mL), stroke volume (2.6 ± 23.1 mL), and ejection fraction (0.4 ± 10.2%) measured by 2D KBR and cardiac magnetic resonance imaging. There were no significant interobserver or intraobserver test-retest differences for 2D KBR RV metrics, with acceptable limits of agreement (interobserver end-diastolic volume, -0.9 ± 21.8 mL; end-systolic volume, -1.3 ± 25.8 mL; stroke volume, -0.2 ± 24.2 mL; ejection fraction, 0.7 ± 14.4%). Significant test-retest variability was observed for 2D echocardiographic RV areas and FAC.
Conclusions: Two-dimensional KBR is an accurate, novel technique for RV volumetric quantification in PH, with superior test-retest reproducibility compared with conventional 2D echocardiographic RV FAC.
Towards deep learning methods for quantification of the right ventricle using 2D echocardiography.
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