Mackenzie, J. A. | Search bioRxiv

bioRxiv2026

A Computational Framework for Pulmonary Assessing Wave Intensity Following Simulated Lung Resection

Background and ObjectivesLung cancer is one of the most frequently diagnosed cancers worldwide. While non-surgical treatment options have increased in number and efficacy, lung resection for primary cancers is still a mainstay of treatment. Lung resection has been shown to impair right ventricular function, although the mechanism for the impairment remains unclear. Wave intensity is increasingly used as a metric for increased post-operative afterload. Here, we develop a computational framework to assess the impact of simulated lung resection on wave intensity to establish that post-operative changes in wave intensity are attributable to the change in pulmonary artery morphometry. MethodsWe analyse a 48 pulmonary arterial surfaces segmented from CT images in patients with no evidence of lung disease to obtain 1D representations of the pulmonary vasculature. For each pulmonary vasculature we sequentially remove vessel branches to mimic post-operative morphometric changes to the arterial network. Using an established 1D computational flow model, we simulate pulsate blood flow in 44 pre-operative cases and 1596 post-operative cases. We compute wave intensity in the main, right, and left pulmonary arteries for all simulations. ResultsWe compare the change in computed wave intensities pre-versus post-operatively to the results of an experimental clinical study comparing pre- and post-operative wave intensity in a 27 patient cohort. We see good agreement between the changes in the parameters of wave intensity between this study and those reported in the clinical study. Further, we capture flow distribution the changes pre-versus post-operatively which indicates that the computational model behaves as expected. ConclusionsIn this preliminary study on a computational framework to capture changes in pulmonary arterial haemodynamics following lung resection, we have shown that our model and analysis pipeline is capable of capturing post-operative changes to wave intensity and flow redistribution between the pulmonary arteries following lung resection. These results motivate further research to develop and validate a patient specific model which is an area of active research for us.

biophysics↗

bioRxiv2025

Identifying myocardial regions perfused by coronary arteriesthrough detailed human microvasculature data

Purposeclinical imaging can resolve the main coronary arteries but not the smaller side branches that penetrate the heartwall. However, precise association between the main coronary branches and the myocardial mass they perfuse is crucial to achieve a correct description of haemodynamics from the large arteries to the cardiac tissue. In this work, we use ex-vivo detailed morphometric data of human coronary microcirculation to build and validate a tool for a personalized coronary-myocardium association, and we use it in a multiscale computational model of cardiac perfusion. Methodsfrom the digitalized dataset of an entire human coronary microcirculation, vascular beds associated to single branches are extracted and analysed to infer patterns in epicardial branching. 3D segmentations of the coronaries with and without this information are used to generate two different myocardial subdivisions, which are compared to the one obtained from the microcirculation data. The impact on haemodynamics is assessed through computational simulations. Resultsepicardial arteries exhibit characteristic patterns of transverse branching, with branching angles [~=] 90{degrees} and rate of branching, with respect to the distance along the vessel, depending on the core diameter. The addition of transverse outflows to the segmentations greatly increases accuracy in the myocardial subdivision, allowing discrimination between mass perfused by the proximal and distal arterial segments. Perfusion simulations including transverse outflows show more homogeneous blood flow across the myocardium, consistently with experimental findings. Conclusionsthe inclusion of transverse outflows in 3D coronary segmentations is essential to correctly capture the coronary-myocardium association and the distribution of myocardial blood flow.

bioengineering↗