Title: Averaged results of blood flow simulations with discrete RBC tracking for microvascular networks

Type Dataset Franca Schmid (2017): Averaged results of blood flow simulations with discrete RBC tracking for microvascular networks. Zenodo. Dataset. https://zenodo.org/record/269650

Authors: Franca Schmid (Institue of Fluid Dynamics, ETH Zurich, Switzerland) ; Jenny, Patrick (Institue of Fluid Dynamics, ETH Zurich, Switzerland) ; Weber, Bruno (Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland) ; Kleinfeld, David (Department of Physics, University of California at San Diego, La Jolla, California, USA) ;

Links

• Item record in Zenodo
• Digital object URL

Summary

The dataset contains the results for blood flow simulations in 3 cerebral micorvascular networks.The microvascular networks are from the mouse parietal cortex (Blinder et al., 2013) and embedded in a tissue volume of approximately 1 cubic mm. For the blood flow simulations we used a numercial model with discrete tracking of RBCs which is described in Schmid et al., 2017.

For each network the following data are provided: - Microvascular network with averaged flow and pressure field, as well as averaged values for the distribution and motion of red blood cells (RBCs). - RBC trajectories describing the motion of individual RBCs through the microvascular networks. - The data is stored as a graph, i.e. vertices connected by edges. - Details regarding the simulation parameters can be found in Schmid et al., 2017. - Data format (pickle - files containing python dictonairies). Microvascular networks: edgesDict.pkl: dictionary with edge related data (dictionary keys: flow [um^3/ms], diameter [um], tuple [-], httBC [-], nkind [-], length [um], htt [-], nRBC [-], diameters [um], points [um]) verticesDict.pkl: dictionary with vertex related data (dictionary keys: pressure [mmHg], coordinates [um], pBC [mmHg])

Additional comments on dictionary keys: - pBC: pressure boundary conditions. 'None' for internal nodes. Assigned based on the hierarchical boundary conditions approach (see Schmid et al. 2017 for details) - tuple: connectivity of graph, tuple of vertices - httBC: tube hematocrit boundary conditions. 'None' for internal nodes. Constant value assigned. - nkind: integere to describe the vessel type. 0: pial artery, 1: pial venule, 2: descending arteriole, 3: ascending venule, 4: capillaries, 5: unknown - htt: tube hematocrit - nRBC: number of red blood cells - points: list of tortuous vessel coordinates per edge - diameters: local diameter measurements associated to the 'points' key.

RBC trajectories: RBC_trajectories.pkl: dictonary for each RBC with relevant tracking data (dictionary key: RBC index). The relavant tracking data per RBC is stored in another dictionary with the following keys: edges, lengths, times, pressure, nkindsMod, RBCleft

Additional comments on dictionary keys per RBC: - RBCleft: bool to indicate that RBC left the computational domain - edges: edge indices through which the RBC moves on its way through the vasculature - pressure: pressure [mmHg] values at the nodes along the RBC trajectory - times: time [ms] the RBC spends in the respective edge segment - nkindsMod: nkind at the nodes along the RBC trajectory  - lengths: cummulative length travelled [um]

More information

• DOI: 10.5281/zenodo.269650

Subjects

• Microvascular blood flow, hemodynamics, cerebral microvascular networks, RBC trajectories, numerical simulation

Dates

• Publication date: 2017
• Issued: February 07, 2017

Notes

Rights

• https://creativecommons.org/licenses/by/4.0/legalcode Creative Commons Attribution 4.0 International
• info:eu-repo/semantics/openAccess Open Access

Format

electronic resource

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