CT-based lung geometry for high-resolution modelling of cardiopulmonary interaction

Background Accurate representation of lung geometry is a prerequisite for reliable computational modelling of cardiopulmonary interaction, particularly for simulating regional ventilation–perfusion (V/Q) heterogeneity driven by gravity and vascular anatomy. Most existing models either simplify lung geometry or lack explicit spatial anchoring to physiological reference points. Material and methods In this study, diagnostic computed tomography (CT) images were used to reconstruct anatomically faithful lung geometry and subdivide both lungs into 80 spatially coherent regions. For each region, a set of geometric parameters was extracted, including volume and three-dimensional coordinates of the centre of gravity relative to the pulmonary trunk and to horizontal and vertical reference planes. Image preprocessing, vectorization, and geometric reconstruction were performed using widely available software tools, and the resulting feature matrix was implemented in a mathematical model of cardiopulmonary interaction within a virtual patient environment. Results The CT-based approach enabled precise localization of the pulmonary trunk and consistent definition of a global coordinate system, allowing simultaneous representation of vertical (gravitational) and radial (vascular resistance–related) perfusion gradients. Example simulations demonstrated physiologically plausible distributions of ventilation and perfusion across lung regions, including reduced ventilation in dependent areas and heterogeneous perfusion within iso-gravitational planes. Conclusions CT-based reconstruction of lung geometry from diagnostic images enables high-resolution regional partitioning and generation of a robust geometric matrix suitable for cardiopulmonary simulations. Although limited to a single anatomical dataset and static geometry, the presented model provides a solid foundation for future integration of regional mechanics, dynamic deformation, and validation with functional imaging, supporting advanced research, educational, and clinical applications.