µPIV Velocity Data Processing and Rheological Model Fitting

MATLAB framework to automate the extraction, processing, and analysis of micro-PIV experimental velocity data of microbloodflow.

Author: Maya Salame & Marianne Fenech

Reference: Salame, Maya, author; Fenech, Marianne (Biomedical researcher), degree supervisor; University of Ottawa. Department of Mechanical Engineering. Blood flow in microcirculation : investigating the cell-free layer with capillary microchannels 2025. https://doi.org/10.20381/ruor-31082

Associate papers: Salame and Fenech. A Two-Phase Core-Plasma Model for Microvascular Blood Flow: Comparative Analysis of Hemodynamic Models. https://doi.org/10.1101/2025.06.25.661657

Associate data: Salame and Fenech. High-Resolution Micro-PIV Velocity Profiles and Cell-Free Layer Imaging of Blood Flow in Capillary Microchannels - Federated Research Data Repository / dépôt fédéré de données de recherche. 2025. https://doi.org/10.20383/103.01332

This framework automates the extraction and analysis of µPIV velocity data of blood flow, including velocity alignment and numerical differentiation. It extracts key parameters—such as velocity profiles, shear rates, viscosity, and hydrolic CFL thickness—and performs model fittings to characterize blood flow. Supported models include Newtonian, Power Law, Carreau, Double-Parameter Power Fit, and Plasma-Core.

Automated Data Extraction and Naming Convention. The framework begins with an automated data extraction function, RoundExtractInfo, which systematically retrieves velocity profile datasets from µPIV measurements. This function enforces consistency in data acquisition by structuring each dataset into a standardized format for subsequent analysis. A structured file-naming convention is implemented to encode experimental conditions, allowing automated parsing of the following parameters:

• u: Velocity measurements from µPIV

• r: Radial distance from the channel center

• R: Channel radius

• Qexp: Measured flow rate

• Pexp: Measured pressure

• δo: Optical cell-free layer thickness

• L: Channel length

Model and Fit Computation Table 1 provides a structured overview of the computational models and fitting methods used in the MATLAB framework. It outlines how we extracted key rheological and fitting parameters, which we will explore further in this study. The table is designed to track the progression from initial conditions to final computed values, ensuring a clear understanding of how each parameter is derived. At the start, initialized parameters provide the initial condition for optimization functions. These values come from experimental measurements or literature data. The experimental pressure gradient, PGexp, is obtained by dividing the applied pressure by the length of the channel.

The PGexp and Poiseuille viscosity calculations are frequent initialized parameters. Since pressure drop within the tubing is found to be negligible (see Appendix A.4), PGexp is used as an initial estimation of fluid properties. Next, fixed parameters are constants that remain unchanged during calculations. These are based on experimentally determined values that directly feed into the models, such as the channel radius (R). Finally, the computed output parameters are extracted from each fit or model.

Table1 : Summary of models and computational methods with initialized, fixed, and output parameters used in the MATLAB framework. *Average values from Mehri et al. (https://doi.org/10.1371/journal.pone.0199911)

Graphical and Data Output. The MATLAB framework generates a series of graphical outputs to visualize velocity profiles, shear rate distributions, and viscosity variations across different models. Additionally, all computed parameters and model fits are systematically logged in structured text files for reproducibility and further analysis. A dedicated Results Log.txt file records key computational results and output parameters.