Dataset Information

Line-scanning particle image velocimetry: an optical approach for quantifying a wide range of blood flow speeds in live animals.

ABSTRACT:

Background

The ability to measure blood velocities is critical for studying vascular development, physiology, and pathology. A key challenge is to quantify a wide range of blood velocities in vessels deep within living specimens with concurrent diffraction-limited resolution imaging of vascular cells. Two-photon laser scanning microscopy (TPLSM) has shown tremendous promise in analyzing blood velocities hundreds of micrometers deep in animals with cellular resolution. However, current analysis of TPLSM-based data is limited to the lower range of blood velocities and is not adequate to study faster velocities in many normal or disease conditions.

Methodology/principal findings

We developed line-scanning particle image velocimetry (LS-PIV), which used TPLSM data to quantify peak blood velocities up to 84 mm/s in live mice harboring brain arteriovenous malformation, a disease characterized by high flow. With this method, we were able to accurately detect the elevated blood velocities and exaggerated pulsatility along the abnormal vascular network in these animals. LS-PIV robustly analyzed noisy data from vessels as deep as 850 µm below the brain surface. In addition to analyzing in vivo data, we validated the accuracy of LS-PIV up to 800 mm/s using simulations with known velocity and noise parameters.

Conclusions/significance

To our knowledge, these blood velocity measurements are the fastest recorded with TPLSM. Partnered with transgenic mice carrying cell-specific fluorescent reporters, LS-PIV will also enable the direct in vivo correlation of cellular, biochemical, and hemodynamic parameters in high flow vascular development and diseases such as atherogenesis, arteriogenesis, and vascular anomalies.

SUBMITTER: Kim TN

PROVIDER: S-EPMC3383695 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Publications

Line-scanning particle image velocimetry: an optical approach for quantifying a wide range of blood flow speeds in live animals.

Kim Tyson N TN Goodwill Patrick W PW Chen Yeni Y Conolly Steven M SM Schaffer Chris B CB Liepmann Dorian D Wang Rong A RA

PloS one 20120626 6

<h4>Background</h4>The ability to measure blood velocities is critical for studying vascular development, physiology, and pathology. A key challenge is to quantify a wide range of blood velocities in vessels deep within living specimens with concurrent diffraction-limited resolution imaging of vascular cells. Two-photon laser scanning microscopy (TPLSM) has shown tremendous promise in analyzing blood velocities hundreds of micrometers deep in animals with cellular resolution. However, current an ...[more]

PMID: 22761686

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Project description:BackgroundThe technical development of imaging techniques in life sciences has enabled the three-dimensional recording of living samples at increasing temporal resolutions. Dynamic 3D data sets of developing organisms allow for time-resolved quantitative analyses of morphogenetic changes in three dimensions, but require efficient and automatable analysis pipelines to tackle the resulting Terabytes of image data. Particle image velocimetry (PIV) is a robust and segmentation-free technique that is suitable for quantifying collective cellular migration on data sets with different labeling schemes. This paper presents the implementation of an efficient 3D PIV package using the Julia programming language-quickPIV. Our software is focused on optimizing CPU performance and ensuring the robustness of the PIV analyses on biological data.ResultsQuickPIV is three times faster than the Python implementation hosted in openPIV, both in 2D and 3D. Our software is also faster than the fastest 2D PIV package in openPIV, written in C++. The accuracy evaluation of our software on synthetic data agrees with the expected accuracies described in the literature. Additionally, by applying quickPIV to three data sets of the embryogenesis of Tribolium castaneum, we obtained vector fields that recapitulate the migration movements of gastrulation, both in nuclear and actin-labeled embryos. We show normalized squared error cross-correlation to be especially accurate in detecting translations in non-segmentable biological image data.ConclusionsThe presented software addresses the need for a fast and open-source 3D PIV package in biological research. Currently, quickPIV offers efficient 2D and 3D PIV analyses featuring zero-normalized and normalized squared error cross-correlations, sub-pixel/voxel approximation, and multi-pass. Post-processing options include filtering and averaging of the resulting vector fields, extraction of velocity, divergence and collectiveness maps, simulation of pseudo-trajectories, and unit conversion. In addition, our software includes functions to visualize the 3D vector fields in Paraview.

Project description:An experimental set-up is presented for the in vitro characterization of the fluid dynamics in personalized phantoms of healthy and stenosed coronary arteries. The proposed set-up was fine-tuned with the aim of obtaining a compact, flexible, low-cost test-bench for biomedical applications. Technically, velocity vector fields were measured adopting a so-called smart-PIV approach, consisting of a smartphone camera and a low-power continuous laser (30 mW). Experiments were conducted in realistic healthy and stenosed 3D-printed phantoms of left anterior descending coronary artery reconstructed from angiographic images. Time resolved image acquisition was made possible by the combination of the image acquisition frame rate of last generation commercial smartphones and the flow regimes characterizing coronary hemodynamics (velocities in the order of 10 cm/s). Different flow regimes (Reynolds numbers ranging from 20 to 200) were analyzed. The smart-PIV approach was able to provide both qualitative flow visualizations and quantitative results. A comparison between smart-PIV and conventional PIV (i.e., the gold-standard experimental technique for bioflows characterization) measurements showed a good agreement in the measured velocity vector fields for both the healthy and the stenosed coronary phantoms. Displacement errors and uncertainties, estimated by applying the particle disparity method, confirmed the soundness of the proposed smart-PIV approach, as their values fell within the same range for both smart and conventional PIV measured data (≈5% for the normalized estimated displacement error and below 1.2 pixels for displacement uncertainty). In conclusion, smart-PIV represents an easy-to-implement, low-cost methodology for obtaining an adequately robust experimental characterization of cardiovascular flows. The proposed approach, to be intended as a proof of concept, candidates to become an easy-to-handle test bench suitable for use also outside of research labs, e.g., for educational or industrial purposes, or as first-line investigation to direct and guide subsequent conventional PIV measurements.

Dataset Information

Line-scanning particle image velocimetry: an optical approach for quantifying a wide range of blood flow speeds in live animals.

Background

Methodology/principal findings

Conclusions/significance

Publications

Line-scanning particle image velocimetry: an optical approach for quantifying a wide range of blood flow speeds in live animals.

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