A Data‐Efficient Computational Framework for Reconstruction and Simulation of Microcirculatory Perfusion

PURPOSE: Microcirculation is essential for maintaining tissue viability and organ function by delivering oxygen and nutrients. Impaired microvascular perfusion may result in tissue dysfunction despite apparently normal macrocirculatory parameters. However, comprehensive experimental characterization of entire microvascular networks remains technically challenging, limiting quantitative assessment of network-level perfusion. This study aimed to develop a data-efficient computational framework for reconstructing microcirculatory network structure and estimating blood perfusion from sparse local vascular measurements. METHODS: Rat mesenteric microvascular networks, which share selected quasi-two-dimensional structural characteristics with human sublingual microcirculation, were used for methodological validation. Morphological and flow information was extracted from individual vessel segments, and three datasets, each containing 10 randomly selected venous bifurcations, were used to emulate limited data availability. A conditional generative adversarial network (cGAN) was pretrained on a larger source corpus and fine-tuned using each sparse target dataset. Venous trees were subsequently generated and integrated with derived arterial trees and capillary connections to reconstruct complete microvascular networks. Initial flow rates were assigned from local measurements, followed by network optimization using a structural adaptation model. RESULTS: Five microcirculatory networks were generated for each sparse bifurcation dataset, enabling quantification of network-level perfusion descriptors, including blood-flow fractal dimension, multifractal spectrum, and capillary-flow heterogeneity. Under controlled distribution shift at the bifurcation-generation level, fine-tuning successfully shifted the distributions of generated bifurcation exponent and asymmetry ratio toward the corresponding sparse target data. The reconstructed networks exhibited broadly similar diameter-conditioned velocity and pressure trends to those in the reference network. Structural adaptation further differentiated the diameter-flow relationships in arterial and venous, with the fitted slopes approaching the corresponding reference values. CONCLUSION: The proposed framework provides a data-efficient framework for physiologically plausible reconstruction of microcirculatory networks and estimation of network-level perfusion from sparse local bifurcation data. Validation in rat mesenteric microvascular networks demonstrates the methodological feasibility of the framework and highlights its potential as a quantitative tool for perfusion analysis and advancing translational studies of microcirculatory modeling.