Dataset Information

Characterization and quantification of vortex flow in the human left ventricle by contrast echocardiography using vector particle image velocimetry.

ABSTRACT: The aims of this study were to: 1) assess the feasibility of left ventricular (LV) vortex flow analysis using contrast echocardiography (CE); and 2) characterize and quantify LV vortex flow in normal subjects and patients with LV systolic dysfunction.Vortices that form during LV filling have specific geometry and anatomical locations that are critical determinants of directed blood flow during ejection. Therefore, it is clinically relevant to assess the vortex flow patterns to better understand the LV function.Twenty-five patients (10 normal and 15 patients with abnormal LV systolic function) underwent CE with intravenous contrast agent, Definity (Bristol-Myers Squibb Medical Imaging, Inc., North Billerica, Massachusetts). The velocity vector and vorticity were estimated by particle image velocimetry. Average vortex parameters including vortex depth, transverse position, length, width, and sphericity index were measured. Vortex pulsatility parameters including relative strength, vortex relative strength, and vortex pulsation correlation were also estimated.Vortex depth and vortex length were significantly lower in the abnormal LV function group (0.443 +/- 0.04 vs. 0.482 +/- 0.06, p < 0.05; 0.366 +/- 0.06 vs. 0.467 +/- 0.05, p < 0.01, respectively). Vortex width was greater (0.209 +/- 0.05 vs. 0.128 +/- 0.06, p < 0.01) and sphericity index was lower (1.86 +/- 0.5 vs. 3.66 +/- 0.6, p < 0.001) in the abnormal LV function group. Relative strength (1.13 +/- 0.4 vs. 2.10 +/- 0.8, p < 0.001), vortex relative strength (0.57 +/- 0.2 vs. 1.19 +/- 0.5, p < 0.001), and vortex pulsation correlation (0.63 +/- 0.2 vs. 1.31 +/- 0.5, p < 0.001) were significantly lower in the abnormal LV function group.It was feasible to quantify LV vorticity arrangement by CE using particle image velocimetry in normal subjects and those with LV systolic dysfunction, and the vorticity imaging by CE may serve as a novel approach to depict vortex, the principal quantity to assess the flow structure.

SUBMITTER: Hong GR

PROVIDER: S-EPMC4695212 | biostudies-literature | 2008 Nov

REPOSITORIES: biostudies-literature

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Characterization and quantification of vortex flow in the human left ventricle by contrast echocardiography using vector particle image velocimetry.

Hong Geu-Ru GR Pedrizzetti Gianni G Tonti Giovanni G Li Peng P Wei Zhao Z Kim Jin Kyung JK Baweja Abinav A Liu Shizhen S Chung Namsik N Houle Helene H Narula Jagat J Vannan Mani A MA

JACC. Cardiovascular imaging 20081118 6

<h4>Objectives</h4>The aims of this study were to: 1) assess the feasibility of left ventricular (LV) vortex flow analysis using contrast echocardiography (CE); and 2) characterize and quantify LV vortex flow in normal subjects and patients with LV systolic dysfunction.<h4>Background</h4>Vortices that form during LV filling have specific geometry and anatomical locations that are critical determinants of directed blood flow during ejection. Therefore, it is clinically relevant to assess the vort ...[more]

PMID: 19356506

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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.

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.

Dataset Information

Characterization and quantification of vortex flow in the human left ventricle by contrast echocardiography using vector particle image velocimetry.

Publications

Characterization and quantification of vortex flow in the human left ventricle by contrast echocardiography using vector particle image velocimetry.

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