Abstract
The right ventricular outflow tract (RVOT) is an anatomically complex and spatially variable cardiac region that is difficult to characterise using isolated manual measurements. Although three-dimensional reconstruction techniques enable detailed visualisation of RVOT morphology, reproducible workflows for quantitative, level-based analysis of RVOT geometry remain limited. The aim of this methodological study was to develop and evaluate a semi-automated workflow for quantitative analysis of RVOT geometry based on 3D point-cloud models. This study represents a secondary methodological analysis of an existing anatomical dataset, introducing a new semi-automated workflow for systematic cross-section generation and quantitative descriptor extraction. Anatomical RVOT models were prepared from post-mortem material using silicone casting and digital photogrammetry. After point-cloud preparation, primary cross-sections were used to estimate a global directional reference. A mathematically defined inferior boundary plane and a predefined superior anatomical limit were then used to generate secondary cross-sections along the standardised RVOT segment. For each retained section, positional, ellipse-based, distance-based and area-based descriptors were extracted. The workflow was applied to 60 RVOT point-cloud models. After quality control, 661 secondary cross-sections were retained for analysis, with 5–16 sections per model and a mean of 11.02 ± 2.47 sections per model. Methodological validation included synthetic testing, comparison with manual measurements, and inter-observer assessment. Agreement with manual dimensions was high (ICC = 0.905–0.937), while inter-observer landmark variability was low (RMSE = 0.41 mm). An additional exploratory inter-observer assessment of three RVOT models yielded mean model-specific RMSE values of 12.97 mm2 for cross-sectional area and 0.54 mm for both ellipse axes. The proposed workflow provides a predefined geometric framework for quantitative description of RVOT geometry and should be regarded as a methodological proof of concept rather than a clinically validated measurement system.