Annals of biomedical engineering · 2026
Hierarchy Aware CT-Driven Constrained Constructive Optimization: A Framework for Coronary Network Generation.
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Vajire S L, Choy Jenny S, Kassab Ghassan S, Lee Lik-Chuan
Аннотация
PURPOSE: Subject-specific three-dimensional models of the coronary arterial tree are widely used for simulations of coronary flow and myocardial perfusion. Current image-based reconstructions, however, cannot capture the full microvascular hierarchy, whereas existing synthetic tree generation methods either operate in idealized left ventricular geometries or lack consistency with experimental vessel hierarchy when constrained by anatomy.
METHODS: To address these issues, we present a hierarchy-aware CT-driven constrained constructive optimization (HCT-CCO) framework that generates coronary arterial trees as extensions from segmented subject-specific base epicardial coronary networks. The method combines a distance field that restricts candidate segments to the segmented myocardial wall, applies Kamiya-type optimization seeded with HK-type daughter radii and a flow-weighted bifurcation point, and a hierarchy-aware, stage-wise branching schedule that progressively shifts growth toward distal segments as the domain fills. The resulting networks are assigned diameter-defined Strahler orders and analyzed in terms of diameter-order scaling, order-order connectivity, and transmural segment distributions.
RESULTS: We show that the HCT-CCO algorithm is more stable across stochastic realizations and produces connectivity matrices and diameter-order curves that show improved agreement with experimental benchmarks compared to the conventional CCO algorithm. Application to one synthetic case and five subject-specific CT datasets yielded coronary trees spanning diameter-defined Strahler orders 1-11 with monotonic diameter-order trends and mean diameters consistent with measured porcine coronary morphometry and transmural segment distributions across cases.
CONCLUSION: The HCT-CCO algorithm thus provides a hierarchy-aware, image-constrained approach for generating subject-specific coronary arterial networks that jointly capture experimentally supported diameter-order relations, hierarchical connectivity, and transmural vessel distributions, and offers anatomically grounded substrates for coronary hemodynamic and perfusion modeling.
METHODS: To address these issues, we present a hierarchy-aware CT-driven constrained constructive optimization (HCT-CCO) framework that generates coronary arterial trees as extensions from segmented subject-specific base epicardial coronary networks. The method combines a distance field that restricts candidate segments to the segmented myocardial wall, applies Kamiya-type optimization seeded with HK-type daughter radii and a flow-weighted bifurcation point, and a hierarchy-aware, stage-wise branching schedule that progressively shifts growth toward distal segments as the domain fills. The resulting networks are assigned diameter-defined Strahler orders and analyzed in terms of diameter-order scaling, order-order connectivity, and transmural segment distributions.
RESULTS: We show that the HCT-CCO algorithm is more stable across stochastic realizations and produces connectivity matrices and diameter-order curves that show improved agreement with experimental benchmarks compared to the conventional CCO algorithm. Application to one synthetic case and five subject-specific CT datasets yielded coronary trees spanning diameter-defined Strahler orders 1-11 with monotonic diameter-order trends and mean diameters consistent with measured porcine coronary morphometry and transmural segment distributions across cases.
CONCLUSION: The HCT-CCO algorithm thus provides a hierarchy-aware, image-constrained approach for generating subject-specific coronary arterial networks that jointly capture experimentally supported diameter-order relations, hierarchical connectivity, and transmural vessel distributions, and offers anatomically grounded substrates for coronary hemodynamic and perfusion modeling.