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Updated: Jan 13, 2026

Micro-particle Image Velocimetry for Velocity Profile Measurements of Micro Blood Flows
Published on: April 25, 2013
In vitro blood flow dataset in circular microchannels: High-speed videomicroscopy, micro-piv, and cell-free layer
Maya Salame1, Marianne Fenech1
1University of Ottawa, Department of Mechanical Engineering, 161 Louis-Pasteur Private, CBY Building, Ottawa, Ontario, K1N 6N5, Canada.
Abstract:
This dataset provides micro-particle image velocimetry (µPIV) and high-speed imaging of red blood cell (RBC) suspensions flowing through circular glass microchannels with inner diameters of 25 µm and 50 µm. Suspensions were prepared in phosphate-buffered saline (PBS) and in native plasma at hematocrit levels of 5 %, 10 %, 15 %, and 20 %, covering physiologically relevant concentration ranges. These conditions allow systematic assessment of flow dynamics and the formation of cell-free layers (CFLs) in confined microvascular geometries. The dataset includes raw and processed µPIV image sequences, providing two-dimensional velocity fields of RBC suspensions under varying flow conditions. Complementary high-speed brightfield recordings capture RBC distribution near channel walls, enabling quantification of CFL thickness. Extracted CFL measurements are included alongside the video sequences. Data are organized into a structured folder hierarchy by channel diameter, hematocrit level, and suspending medium. Each folder contains raw image files, processed velocity fields, CFL thickness measurements, and corresponding flow rate and pressure logs. Standardized file naming facilitates straightforward navigation, reproducibility, and integration into external workflows. This resource can be reused by researchers investigating blood rheology, microfluidics, and hemocompatibility to study the effects of hematocrit, shear rate, and suspending medium on microchannel flow. The dataset also provides benchmark data for validating computational models of microcirculation and can support the development of machine learning approaches for flow prediction or RBC core-plasma segmentation. Its combination of raw and processed outputs enables both direct analysis and cross-study integration into broader multi-scale investigations of microvascular transport.
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