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Research Papers

Optimized Time-Resolved Echo Particle Image Velocimetry– Particle Tracking Velocimetry Measurements Elucidate Blood Flow in Patients With Left Ventricular Thrombus

[+] Author and Article Information
Kaushik Sampath

Department of Mechanical Engineering,
Johns Hopkins University,
3400 North Charles Street, Latrobe 223,
Baltimore, MD 21218
e-mail: kaushik@jhu.edu

Thura T. Harfi

Division of Cardiology,
Department of Medicine,
Johns Hopkins University,
600 North Wolfe Street,
Baltimore, MD 21287
e-mail: thura.harfi@gmail.com

Richard T. George

Division of Cardiology,
Department of Medicine,
Johns Hopkins University,
600 North Wolfe Street,
Baltimore, MD 21287
e-mail: rtgeorge3@gmail.com

Joseph Katz

Department of Mechanical Engineering,
Johns Hopkins University,
3400 North Charles Street, Latrobe 122,
Baltimore, MD 21218
e-mail: katz@jhu.edu

1Corresponding author.

Manuscript received May 15, 2017; final manuscript received December 19, 2017; published online February 12, 2018. Assoc. Editor: Alison Marsden.

J Biomech Eng 140(4), 041010 (Feb 12, 2018) (14 pages) Paper No: BIO-17-1218; doi: 10.1115/1.4038886 History: Received May 15, 2017; Revised December 19, 2017

Contrast ultrasound is a widely used clinical tool to obtain real-time qualitative blood flow assessments in the heart, liver, etc. Echocardiographic particle image velocimetry (echo-PIV) is a technique for obtaining quantitative velocity maps from contrast ultrasound images. However, unlike optical particle image velocimetry (PIV), routine echo images are prone to nonuniform spatiotemporal variations in tracer distribution, making analysis difficult for standard PIV algorithms. This study introduces optimized procedures that integrate image enhancement, PIV, and particle tracking velocimetry (PTV) to obtain reliable time-resolved two-dimensional (2D) velocity distributions. During initial PIV analysis, multiple results are obtained by varying processing parameters. Optimization involving outlier removal and smoothing is used to select the correct vector. These results are used in a multiparameter PTV procedure. To demonstrate their clinical value, the procedures are implemented to obtain velocity and vorticity distributions over multiple cardiac cycles using images acquired from four left ventricular thrombus (LVT) patients. Phase-averaged data elucidate flow structure evolution over the cycle and are used to calculate penetration depth and strength of left ventricular (LV) vortices, as well as apical velocity induced by them. The present data are consistent with previous time-averaged results for the minimum vortex penetration depth associated with LVT occurrence. However, due to decay and fragmentation of LV vortices, as they migrate away from the mitral annulus, in two cases with high penetration, there is still poor washing near the resolved clot throughout the cycle. Hence, direct examination of entire flow evolution may be useful for assessing risk of LVT relapse before prescribing anticoagulants.

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Figures

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Fig. 1

Echocardiographic images with varying concentrations of ultrasound contrast agent. (a) Peak concentration phase, widely used by clinicians to delineate endocardial borders and obtain qualitative information about wall motion and overall cardiac function, (b) optimum concentration phase, where traces of individual contrast microbubbles or agglomerates are discernable and well distributed making it optimum for echo-PIV, and (c) low concentration phase, where individual microbubbles can still be traced, however, their number is too low for mapping of blood flow.

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Fig. 2

(a) Sample image acquired at time t = t0, and (b) image from the same sequence after 0.4 s show that the (1) seeding is nonuniform, (2) image size distribution varies significantly, (3) images of the walls are similar to bubble agglomerates, and (4) background noise varies intermittently in routine contrast echo images. Subplots (c)–(f) highlight the image enhancement procedure. (c) Raw image. (d) Scaled image gradient of (c) after masking. (e) phase average intensity subtracted regions of (c) where (d) exceeds a threshold for every segmented object. (f) final enhanced image after particle specific enhancement applied on (e).

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Fig. 3

Flowchart of processing steps to obtain final PIV velocity field (upiv, vpiv) from the raw PIV data (uraw, vraw)

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Fig. 4

Distributions of (a) Δ̃¯θ, (b) Δ̃¯m, (c) Δ¯θ, (d) Δ¯m, and (e) EKG signal, for a sample dataset for raw data (uraw, vraw) and (uopt,vopt)1 with different map and grid point threshold levels. Distributions of (f) Δθ and (g) Δm over sample dataset for (uraw, vraw) (black) and (uopt,vopt)1 (gray).

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Fig. 5

Number distribution of all successfully matched candidates in a sample dataset for (a) location deviation (Δ) in pixels from PIV with a threshold (G) of 3 pixels, (b) percentage area change (ΔA) with a threshold of 20%, (c) percentage perimeter change (ΔP) with a threshold of 20%, (d) peak cross-correlation value (C) with a threshold of 70%, and (e) final candidate score (S)

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Fig. 6

Sample contrast echo image covering entire LV, overlaid with a contour plot of instantaneous vorticity (ωz) and velocity vector field in full resolution after all velocity calculation steps are complete

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Fig. 7

Contour representing the maximum phase-averaged velocity magnitude over all phases overlaid on sample echo images for patients AD. LVT statuses of clots at the time of scanning are marked on their corresponding locations. Patients whose LVT was cured in the past are labeled as cured (LVT-CU), while those that have not shown any improvement despite prolonged anticoagulation are labeled as chronic (LVT-CH). Patients who were scanned for this study shortly after LVT detection and are responding to anticoagulation treatment are labeled as recovering (LVT-R). LV wall segments are labeled based on the degree of myocardial thickening during systole. Segments that contract normally are labeled normokinetic, while those that do not thicken at all are labeled akinetic. Segments that appear to have subnormal thickening are labeled hypokinetic. Note the difference between scan areas for different patients (as marked by the reference scales).

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Fig. 8

Contour plot of phase-averaged velocity magnitude (u¯2+ v¯2)1/2 overlaid by velocity vectors for patients AD at ten phases (0-9) spanning the cardiac cycle. Vectors are diluted by half in each direction for clarity. A generic EKG signal is marked with cardiac phases as a reference along the horizontal axis, patient-specific phase-averaged EKG signals are provided in Fig. 10.

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Fig. 9

Contour plot of phase-averaged vorticity (ω¯z) for patients AD at ten phases (0–9) spanning the cardiac cycle. A generic EKG signal is marked with cardiac phases as reference along the horizontal axis, patient specific phase-averaged EKG signals are provided in Fig. 10. The phase-averaged vorticity conditioned on a swirling strength threshold ω¯z(λci > 1 s−1) is shown for patient B, phase 8. Solid and dotted lines represent contours at 0.5ω¯zmax around vortices. For consistency, the vortices are labeled with the same marker used in Fig. 10 to represent their parameters.

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Fig. 10

Profiles of peak vorticity (ω¯zmax/HR), strength (Γ̂), penetration (YV/YLV), total induced apical velocity (v̂ind), and directly measured tangential velocity component near apical wall (v̂apx) for LV vortices (each colored and marked differently) over the cardiac cycle along with patient-specific EKG signal. Black solid lines represent total Γ̂ for cases with multiple structures. Vertical gridlines correspond to the ten phases (0–9) in Figs. 8 and 9. The condition for averaged Yv/YLV= 0.45 for LVT presence based on Son et al. is shown as a dotted line. Values of ΔΓ̂ and EF are marked on Γ̂ subplots.

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Fig. 11

Distributions of directly measured tangential velocity component near apical wall, |vapx| [cm/s] over the cardiac cycle for patients AD

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