Estimation of Blood Flow Rates in Large Microvascular Networks

Fry, Brendan C.; Lee, Jack; Smith, Nicolas P.; Secomb, Timothy W.

doi:10.1111/j.1549-8719.2012.00184.x

Cited by 57 publications

(129 citation statements)

References 28 publications

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“…This permeability tensor was shown to be highly sensitive to capillary diameters, which exhibit a strong dependence on transmural location. Future research will involve comparing a continuum-based Darcy flow model parameterized by these permeability values to a flow solution within the discrete vascular network estimated via the optimization procedure of Fry et al 6 , which constrains capillary shear stress and pressure to within a physiological range.…”

Section: Discussionmentioning

confidence: 99%

Transmural Variation and Anisotropy of Microvascular Flow Conductivity in the Rat Myocardium

et al. 2014

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Transmural variations in the relationship between structural and fluid transport properties of myocardial capillary networks are determined via continuum modelling approaches using recent three-dimensional (3D) data on the microvascular structure. Specifically, the permeability tensor, which quantifies the inverse of the blood flow resistivity of the capillary network, is computed by volume-averaging flow solutions in synthetic networks with geometrical and topological properties derived from an anatomically-detailed microvascular data set extracted from the rat myocardium. Results show that the permeability is approximately ten times higher in the principal direction of capillary alignment (the ‘longitudinal’ direction) than perpendicular to this direction, reflecting the strong anisotropy of the microvascular network. Additionally, a 30% increase in capillary diameter from subepicardium to subendocardium is shown to translate to a 130% transmural rise in permeability in the longitudinal capillary direction. This result supports the hypothesis that perfusion is preferentially facilitated during diastole in the subendocardial microvasculature to compensate for the severely-reduced systolic perfusion in the subendocardium.

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Section: Discussionmentioning

confidence: 99%

Transmural Variation and Anisotropy of Microvascular Flow Conductivity in the Rat Myocardium

et al. 2014

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“…Another possibility is that due to the higher viscosity of the Microfil (∼20cP) compared to blood, there might be structures that were not perfused and therefore not visible to µCT. We treat this underdetermined linear system by applying an optimization technique (Fry et al, 2012), exploiting available literature information on blood perfusion studies of breast cancer, as well as the distinct nature of the tumor vascular network. Our methodological approach is presented in the Supplementary Information and is not biased regarding the choice of flow directionality.…”

Section: Methodsmentioning

confidence: 99%

A bioimage informatics based reconstruction of breast tumor microvasculature with computational blood flow predictions

Stamatelos

Kim

Pathak

et al. 2014

Microvascular Research

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Induction of tumor angiogenesis is among the hallmarks of cancer and a driver of metastatic cascade initiation. Recent advances in high-resolution imaging enable highly detailed three-dimensional geometrical representation of the whole-tumor microvascular architecture. This enormous increase in complexity of image-based data necessitates the application of informatics methods for the analysis, mining and reconstruction of these spatial graph data structures. We present a novel methodology that combines ex-vivo high-resolution micro-computed tomography imaging data with a bioimage informatics algorithm to track and reconstruct the whole-tumor vasculature of a human breast cancer model. The reconstructed tumor vascular network is used as an input of a computational model that estimates blood flow in each segment of the tumor microvascular network. This formulation involves a well-established biophysical model and an optimization algorithm that ensures mass balance and detailed monitoring of all the vessels that feed and drain blood from the tumor microvascular network. Perfusion maps for the whole-tumor microvascular network are computed. Morphological and hemodynamic indices from different regions are compared to infer their role in overall tumor perfusion.

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“…To simulate blood flow through microvascular networks, microscopy‐based topological and morphological information (in the form of vascular graphs) is typically combined with physical laws governing blood flow, and empirical descriptions of rheological effects . The interconnected nature of microvascular networks, however, presents a challenge . Typical vascular graphs have a considerable number of open ends or boundary nodes.…”

Section: Introductionmentioning

confidence: 99%

Model‐based inference from microvascular measurements: Combining experimental measurements and model predictions using a Bayesian probabilistic approach

et al. 2017

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Objective In vivo imaging of the microcirculation and network-oriented modeling have emerged as powerful means of studying microvascular function and understanding its physiological significance. Network-oriented modeling may provide the means of summarizing vast amounts of data produced by high-throughput imaging techniques in terms of key, physiological indices. To estimate such indices with sufficient certainty, however, network-oriented analysis must be robust to the inevitable presence of uncertainty due to measurement errors as well as model errors. Methods We propose the Bayesian probabilistic data analysis framework as a means of integrating experimental measurements and network model simulations into a combined and statistically coherent analysis. The framework naturally handles noisy measurements and provides posterior distributions of model parameters as well as physiological indices associated with uncertainty. Results We applied the analysis framework to experimental data from three rat mesentery networks and one mouse brain cortex network. We inferred distributions for more than five hundred unknown pressure and hematocrit boundary conditions. Model predictions were consistent with previous analyses, and remained robust when measurements were omitted from model calibration. Conclusion Our Bayesian probabilistic approach may be suitable for optimizing data acquisition and for analyzing and reporting large datasets acquired as part of microvascular imaging studies.

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Estimation of Blood Flow Rates in Large Microvascular Networks

Cited by 57 publications

References 28 publications

Transmural Variation and Anisotropy of Microvascular Flow Conductivity in the Rat Myocardium

Transmural Variation and Anisotropy of Microvascular Flow Conductivity in the Rat Myocardium

A bioimage informatics based reconstruction of breast tumor microvasculature with computational blood flow predictions

Model‐based inference from microvascular measurements: Combining experimental measurements and model predictions using a Bayesian probabilistic approach

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