Name: particle image velocimetry investigation of hemodynamics via aortic phantom
Uploaded: 2022-02-25T15:00:00.000+00:00
Duration: 6 min 26 s
Description: Watch this Scientific Journal Video about Particle Image Velocimetry Investigation of Hemodynamics via Aortic Phantom at JoVE.com

This research was supported by the Basic Science Research Program of the National Research Foundation of Korea, which is funded by the Ministry of Education (NRF-2021R1I1A3040346 and NRF-2020R1A4A1019475). This study was also supported by 2018 Research Grant (PoINT) from Kangwon National University.

1. In vitro setup
<ol>
	<li>Prepare the experimental setup on an optic table, including a piston pump, data acquisition device (DAQ), and a computer with the required system engineering software and a motor controlling software (see Table of Materials) (Figure 1). 
	NOTE: The piston pump has been previously tested and calibrated and consists of a motor, motor driver, and linear actuator9.</li>
	<li>Import the spreadsheet file with the flow rate information to the system engineering software. 
	NOTE: For example, heart rate is 60 bpm, the maximum flow rate is 20 L/min, cardiac output is 4.8 L/min, and stroke volume is 70 mL.</li>
	<li>Set the parameter in systems engineering software, such as DAQ input and output channel; sample clock is 1,000 and feedback iteration is 10.</li>
	<li>Set the parameter in the motor controlling software; lead screw length is 10 mm, analog input and output are 14.5 mm/voltage.</li>
	<li>Install the check valve and resistance valve to the reservoir10. 
	NOTE: The check valve is connected to the piston pump as an inlet of the system, and the ball valve is connected to the acrylic sinus model as an outlet of the system.</li>
	<li>Fix the acrylic sinus model (Figure 2) with a square aluminum bar to the optic table. 
	NOTE: The dimensions of the acrylic sinus model are presented in Table 1.</li>
	<li>Install the pressure gauge (~0-15 psi) to the pressure tap of the acrylic sinus model for receiving a pressure signal from another computer. 
	NOTE: The pressure taps are located 140 mm from the sinotubular junction (STJ).</li>
	<li>Prepare a working fluid by mixing saline and glycerin (see Table of Materials) in a mass ratio of 60:40. 
	NOTE: A viscometer and a refractometer were used to measure the working fluid&#39;s viscosity and refractive index. The viscosity is ~4 cp, the refractive index is 1.45, and the density is 1,100 kg/m3.</li>
	<li>Connect the reservoir, piston pump, and the acrylic sinus model with a silicone hose (see Table of Materials).</li>
	<li>Tie the transcatheter aortic valve (TAV) (see Table of Materials) to the native leaflet made by a 3D printer with a thread11.</li>
	<li>Combine the fixed TAV on the native leaflet with the acrylic sinus model. 
	NOTE: The TAV used here (obtained commercially) has a diameter of 23 mm and 26 mm, and height is 18 mm and 20 mm, respectively12. For TAV (23 mm), the deployment depth and native leaflet length were 1.8 mm and 9 mm, and for TAV (26 mm), it is 2.0 mm and 10 mm, respectively. The inner diameter of the native leaflet was 21 mm, considering the patient&#39;s annulus size. 
	CAUTION: The TAV dries out if not preserved in saline solution. It is kept in the liquid even after being tied to the native leaflet.</li>
	<li>Fill the working fluid (step 1.8) in the in vitro system. 
	&#8203;CAUTION. Avoid making bubbles on the acrylic sinus model because it affects PIV results.</li>
</ol>
2. PIV setup
<ol>
	<li>Locate the laser on another optic table and one-axis rail. 
	NOTE: The laser is a continuous Nd: YAG laser that emits light with a wavelength of 532 nm and whose power can increase to 10 W (see Table of Materials). The laser sheet passed through optics has a 1 m distance from the acrylic sinus model.</li>
	<li>Locate the high-speed camera on a 2-axis traverse and move the traverse. 
	CAUTION: The high-speed camera is perpendicular to the laser sheet and acrylic sinus model.</li>
	<li>Equip the lens to the high-speed camera. 
	NOTE: The macro lens mounted on the high-speed camera has a focal length of 105 mm, and the aperture is f/2.8.</li>
	<li>Seed particle (see Table of Materials) in the reservoir. 
	NOTE: The particle is a hollow glass sphere with an average diameter of 10 &#181;m and a density of 1,090 kg/m3. The reservoir has a rectangular shape and, the width, length, and height inside are 23 cm, 23 cm, 35 cm, respectively. There is a hole for fastening in the upper part. The lid also had a hole for fastening and a bolt tap to install the bulb pump to apply pressure.</li>
	<li>Program an external trigger using an open-source electronic prototypic platform, Arduino (see Table of Materials). 
	NOTE: When the piston pump moves a predetermined distance, the output of Arduino becomes 1, which is transmitted to the high-speed camera as a trigger to be photographed.</li>
	<li>Run the camera controlling software (see Table of Materials), click on Current Session Reference (CSR), and remove the lens cap.</li>
	<li>Turn on the laser, set it to 7 W, and locate the laser sheet to the center of the TAV.</li>
	<li>Take a snapshot and check the particle density and diameter. 
	NOTE: To reduce errors, confirm that ~8-10 particles are in the interrogation window, with a particle diameter of 2-4 pixels13.</li>
	<li>Set the parameters, such as resolution (1280 x 720), random frame rate, exposure time to maximum according to random frame rate in the camera controlling software.</li>
	<li>Click on the Enable button in the motor controlling software at first and click on the Start button in the systems engineering software to operate the piston pump.</li>
	<li>Take a picture and check whether the maximum particle distance is less than 4-6 pixels. 
	NOTE: This study corresponds to 50% of the interrogation window, which set 16 pixels between velocity vectors. The maximum distance of particles in the interrogation window is limited to 8 pixels.</li>
	<li>Repeat step 2.11 to ensure the maximum particle distance within that range by adjusting the frames per second (fps) if it is more than 6 pixels and lowering the fps if it is less than 4 pixels.</li>
</ol>
3. Investigation of hemodynamics
<ol>
	<li>Check whether there is leakage from the connection part of the acrylic sinus model or the silicone hose becomes folded.</li>
	<li>Import the excel file having stored flow rate and bpm information in the system engineering software. 
	NOTE: For example, heart rate is 60 bpm, the maximum flow rate is 20 L/min, cardiac output is 4.8 L/min, stroke volume is 70 mL (Figure 3A).</li>
	<li>Confirm the system engineering software parameter, such as DAQ device input and output channel. The sample clock is 1,000, and the feedback iteration is 10.</li>
	<li>Confirm the motor controlling software parameter, e.g., lead screw length is 10 mm, analog input and output are 14.5 mm/voltage.</li>
	<li>Turn on the high-speed camera and run the camera controlling software.</li>
	<li>Click on CSR and remove a lens cap.</li>
	<li>Set the camera controlling software parameters, e.g., the resolution of 1280 x 720, a frame rate of 300 fps, burst period of 200 &#181;s and 150 &#181;s, burst count of 3, and exposure (forced by the burst period).</li>
	<li>Turn on the laser, set it to 7 W, and locate the laser sheet on the center of TAV. Focus on the laser sheet by controlling the lens.</li>
	<li>Adjust the pressure to the reservoir. 
	NOTE: The mean post-valvular pressure is 100 mmHg while operating the piston pump (Figure 3B,C).</li>
	<li>Click on the Enable button in the motor controlling software at first and click on the Start button in the system engineering software to operate the piston pump.</li>
	<li>Wait until the flow rate stabilizes. 
	NOTE: The flow rate calculates the difference based on the signal from the piston pump and executes negative feedback, so it takes time to wait until it stabilizes.</li>
	<li>Check a trigger that works in the Arduino serial plotter.</li>
	<li>Capture particle images for continuous 14 cycles and repeat a total of seven times. 
	&#8203;NOTE: The storage capacity of a high-speed camera is related to the resolution and the number of particle images. According to the parameter set in step 3.7, it is possible to take a picture only for 14 cycles at a time.</li>
</ol>
4. Data processing
<ol>
	<li>Convert from .cine file to .tiff files using the camera controlling software.</li>
	<li>Calculate the average image for all the particle images over time. Remove the area corresponding to the reflection of the laser at the wall or the TAV by subtracting the average image14.</li>
	<li>Make the mask by separating the areas to be analyzed from those to discard. 
	NOTE: In this study, two masks were used: one to analyze the sinus region alone and the other to analyze the entire region, which contains the region after STJ.</li>
	<li>Perform PIV using the PIVlab, an open-source tool based on MATLAB15 (see Table of Materials).
	<ol>
		<li>Import particle images saved by the time-resolved method or pairwise method.</li>
		<li>Execute contrast limited adaptive histogram equalization (CLAHE)16. 
		NOTE: CLAHE is a method for image pre-processing. The contrast of the particle image is redistributed so that the laser reflects the increase and decrease in the particle intensity. The particle image is divided by a window with 20 pixels.</li>
		<li>Import the mask and apply it to all the particle images.</li>
		<li>Set the multi-pass interrogation window. 
		NOTE: The interrogation window is decreased from 64 x 64 to 32 x 32 with a 50% overlap. The distance between the two vectors corresponds to 16 pixels.</li>
		<li>Execute the cross-correlation13 about the particle image pair converted into the frequency domain using fast Fourier transform (FFT)13.</li>
		<li>Find a peak value using a 2 x 3 Gaussian fit in correlation result. 
		NOTE: The peak value selected in the Gaussian fitting determined the particle distance.</li>
	</ol>
	</li>
	<li>Run the smoothing process, which involves the following processes.
	<ol>
		<li>Remove the outliers into a &#34;NaN&#34; using the &#34;isoutlier&#34; built-in function in MATLAB.</li>
		<li>Interpolate a nan to value using the &#34;inpaint_nans&#34; function in MATLAB15.</li>
		<li>Convert from &#34;pixel/frame&#34; to &#34;m/s&#34; according to the frame rate and burst period. 
		NOTE: The conversion is related to the time interval, determined by the frame rate and burst period. Specifically, the coefficient of the time-resolved method is derived by the frame rate, and that of a pairwise method is derived by the burst period.</li>
		<li>Merge the pairwise method and time-resolved method using the weighting factor. 
		NOTE: The weighting factor depends on the velocity magnitude and has a total value of 1 in each section. If the velocity magnitude exceeds a certain threshold, the factor for the pairwise method is higher than that for the time-resolved method.</li>
		<li>Run the &#34;smoothn&#34; function of DCT-PLS using a smoothing factor of 0.5915,17. 
		&#8203;NOTE: The &#34;smoothn&#34; and &#34;inpaint_nans&#34; functions are present in the PIVlab.</li>
	</ol>
	</li>
</ol>
5. Data analysis
<ol>
	<li>Load PIV data into MATLAB.</li>
	<li>Extract &#34;u&#34; and &#34;v&#34; components from the PIV data.</li>
	<li>Calculate the velocity field18 (Equation 1, Supplementary File 1).</li>
	<li>Derive hemodynamics parameters using the in-house code and built-in function19.
	<ol>
		<li>Derive the vorticity with MATLAB built-in function &#34;curl&#34;18 (Equation 2, Supplementary File 1).</li>
		<li>Derive the stasis with the in-house code20 (Equation 3, Supplementary File 1).</li>
		<li>Derive the &#915;1 with the in-house code21 (Equation 4, Supplementary File 1).</li>
		<li>Derive the particle residence with an in-house code19 (Equation 5, Supplementary File 1).</li>
	</ol>
	</li>
	<li>Calculate the average and standard deviation of hemodynamics parameters (Table 2). 
	NOTE: Peak velocity, vorticity, &#915;1, and stasis were calculated for a total of 98 cycles. The decay was obtained through exponential fitting to the percentage of particle residence. The decay set 14 cycles as one dataset and calculated the average and standard deviation for seven times.</li>
</ol>

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The authors have nothing to disclose.

The sinus flow changed due to different sinus geometry after TAVI. The vortex was formed by the aortic valve opening and the interaction with the forward jet of systole22. In the study of the artificial surgical valve without native leaflets, vortex observed in the sinus region at systole was normal23. This study forms the vortex presented at diastole by reducing the forward jet and coming into the sinus. The sinus flow encountered the native leaflet; as a result, it splits...

Aortic stenosis is a common disease in older adults, and it is when the aortic valve doesn&#39;t open, reducing blood flow. The problem is caused by the thickening or calcification of the aortic valve1. Therefore, it is a necessary treatment to enhance the blood flow and decrease the load on the heart. It is treated by remodeling the aortic valve or replacing it with an artificial valve. This study focuses on transcatheter aortic valve implantation (TAVI), replacing the malfunctioning aortic valve with an artificial one using a catheter.
TAVI has been recommended for patients challenged in surgery, and the mortality has also been low2. Recently, it has been reported that thrombus in patients after TAVI caused valve dysfunction and stroke3,4. Thrombus in the aortic sinus and neo-sinus is suspected, with its cause probably being the changes in the hemodynamics caused by TAVI. It is performed without removing the native leaflets; these leaflets can disturb the sinus flow and elevate the risk of thrombosis5.
It is difficult to determine how blood flow is affected by TAVI and how thrombosis is induced in patients. It is desirable to elucidate the relationship between blood flow and thrombus formation in vivo. However, a lack of practical techniques for measuring blood flow makes this problematic. On the other hand, in vitro techniques have the advantage of allowing one to monitor the changes in the blood flow by limiting the parameters that must be investigated. In vitro setup and particle image velocimetry (PIV) have been used to identify velocity in medical fields6,7,8. Therefore, in vitro and PIV are sufficient for determining the parameters to be reported by mimicking the patient&#39;s condition: the heart rate and pressure, viscosity, and sinus geometry, and allowing one to control these parameters.
In this study, in vitro setup and PIV are used to investigate the flow in the aortic sinus after TAVI. The aortic phantom and TAVI for the PIV and the data acquisition process and post-processing flow analysis are described in this protocol. Various hemodynamic parameters are derived, including the velocity, stasis, vortex, vorticity, and particle residence. The results demonstrate that in vitro setup and PIV help investigate the hemodynamic features in the aortic sinus.

<table><tbody><tr><td>3D Printer</td><td>Prusa Research</td><td></td><td>Original Prusa i3 MK2; FDM printer</td></tr><tr><td>Aluminum bar (square)</td><td>APSPRO</td><td>KHP-3030, KHP-6060</td><td>Dimension: 30 mm x 30 mm, 60 mm x 60 mm</td></tr><tr><td>Bulb pump</td><td>Skyhope</td><td></td><td>MHL-1</td></tr><tr><td>Camera controlling software</td><td>Phantom</td><td>PCC 3.4 software</td><td>The software controll the high speed camera</td></tr><tr><td>Check valve</td><td>HANJU STEEL PIPE</td><td></td><td>Check valve; 1/2 inch (15A)</td></tr><tr><td>Digital Aqusition device</td><td>National Instruments</td><td></td><td>USB-6001</td></tr><tr><td>Glycerin</td><td>ANU Korea</td><td></td><td>It used for making a working fluid</td></tr><tr><td>High-speed camera</td><td>Phantom</td><td></td><td>Phantom VEO 710E-L</td></tr><tr><td>Laser</td><td>Changchun New Industries Optoelectronics Technology</td><td></td><td>MGL-W-532; CW Nd:YAG Laser</td></tr><tr><td>Linear actuator</td><td>THOMSON</td><td></td><td>PC-40; it converts the rotational motion to lenear motion</td></tr><tr><td>Macro lens</td><td>Nikon</td><td></td><td>VR Micro-NIKKOR 105mm, f/2.8 G</td></tr><tr><td>Motor</td><td>KOLLMORGEN</td><td></td><td>AKM33H-ANCNR-00; DC servo motor</td></tr><tr><td>Motor controlling software</td><td>KOLLMORGEN</td><td></td><td>Kollmorgen software; the software controll the motor driver</td></tr><tr><td>Motor driver</td><td>KOLLMORGEN</td><td></td><td>AKD-B00606-NBAN-0000</td></tr><tr><td>Open-source electronic prototypic platform</td><td>Arduino</td><td>A000066</td><td>Arduino Uno R3. It used for making a external trigger</td></tr><tr><td>Optic table</td><td>SMTECH</td><td></td><td>1800 (W) x 900 (B) x 800 (H)</td></tr><tr><td>Particle</td><td>Dantec Dynamics</td><td>80A6011</td><td>Hollow Glass Sphere. Mean diameter:10 &amp;micro;m, Density: 1090 kg/m&lt;sup&gt;3&lt;/sup&gt;</td></tr><tr><td>PIVlab</td><td>PIVlab</td><td></td><td>Open source algorithm based on MATLAB&lt;br/&gt; https://kr.mathworks.com/matlabcentral/fileexchange/27659-pivlab-particle-image-velocimetry-piv-tool-with-gui</td></tr><tr><td>Pressure gauge</td><td>OMEGA</td><td></td><td>PX309-015A5V. Measurement range: 0~15psi</td></tr><tr><td>Refractometer</td><td>ATAGO</td><td>2350</td><td>R-5000. Hand held refractometer; measurement range: 1.333-1.520</td></tr><tr><td>Resistance valve</td><td>HANJU STEEL PIPE</td><td></td><td>Ball valve; 1/2 inch (15A)</td></tr><tr><td>Saline</td><td>DAI HAN PHARM</td><td></td><td>It is used for making a working fluid and for preserving the TAV</td></tr><tr><td>Silicone hose</td><td>HSW</td><td></td><td>Inner diameter 26mm, Outter diameter 30mm; Inlet length 5m, Outlet length 1.5m</td></tr><tr><td>System enginnering software</td><td>National Instruments</td><td></td><td>LabVIEW software. The software controlls the DAQ.</td></tr><tr><td>Transcatheter Aortic Valve, TAV (23 mm) and TAV (26 mm)</td><td>Edwards Lifesciences</td><td></td><td>SAPIEN3 23mm, SAPIEN3 26mm. It is supported by Seoul Asan Medical</td></tr><tr><td>Viscosmeter</td><td>Brookfiled</td><td></td><td>DVELV; Measurement range: 1-2x10&lt;sup&gt;9 &lt;/sup&gt;cp</td></tr></tbody></table>

particle image velocimetry investigation of hemodynamics via aortic phantom

Aortic valve dysfunction and stroke have recently been reported in transcatheter aortic valve implantation (TAVI) patients. Thrombus in the aortic sinus and neo-sinus due to hemodynamic changes has been suspected. In vitro experiments help investigate the hemodynamic characteristics in the cases where an in vivo assessment proves to be limited. In vitro experiments are also more robust, and the variable parameters are controlled readily. Particle image velocimetry (PIV) is a popular velocimetry method for in vitro studies. It provides a high-resolution velocity field such that even small-scale flow features are observed. The purpose of this study is to show how PIV is used to investigate the flow field in the aortic sinus after TAVI. The in vitro setup of the aortic phantom, TAVI for PIV, and the data acquisition process and post-processing flow analysis are described. The hemodynamic parameters are derived, including the velocity, flow stasis, vortex, vorticity, and particle residence. The results confirm that in vitro experiments and PIV help investigate the hemodynamic features in the aortic sinus.

The velocity fields showed a different sinus flow structure depending on the valve diameter in Figure 4. For TAV (23 mm), the velocity was higher than 0.05 m/s between TAV and STJ from early systole to peak systole that TAV was opened using the forwarding jet. High velocity was then distributed in a narrow range near the stent at late systole. The velocity at diastole was lower than 0.025 m/s, and two vortexes with low velocity appeared. For TAV (26 mm), when the valve opened, high velocity ...

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Particle Image Velocimetry Investigation of Hemodynamics via Aortic Phantom

The present protocol describes particle image velocimetry (PIV) measurements performed to investigate the sinus flow through the in vitro setup of the transcatheter aortic valve (TAV). The hemodynamic parameters based on velocity are also determined.

Particle Image Velocimetry Investigation of Hemodynamics via Aortic Phantom

Aortic valve dysfunction and stroke have recently been reported in transcatheter aortic valve implantation (TAVI) patients. Thrombus in the aortic sinus ...

particle-image-velocimetry-investigation-hemodynamics-via-aortic

Research

JoVE Journal

Engineering

3.4K Views.  Kangwon National University. The present protocol describes particle image velocimetry (PIV) measurements performed to investigate the sinus flow through the in vitro setup of the transcatheter aortic valve (TAV). The hemodynamic parameters based on velocity are also determined.

Video: Particle Image Velocimetry Investigation of Hemodynamics via Aortic Phantom

High-speed Particle Image Velocimetry Near Surfaces

Multi-dimensional and transient flows play a key role in many areas of science, engineering, and health sciences but are often not well understood. The complex nature of these flows may be studied using particle image velocimetry (PIV), a laser-based imaging technique for optically accessible flows. Though many forms of PIV exist that extend the technique beyond the original planar two-component velocity measurement capabilities, the basic PIV system consists of a light source (laser), a camera, tracer particles, and analysis algorithms. The imaging and recording parameters, the light source, and the algorithms are adjusted to optimize the recording for the flow of interest and obtain valid velocity data. 
Common PIV investigations measure two-component velocities in a plane at a few frames per second. However, recent developments in instrumentation have facilitated high-frame rate (&gt; 1 kHz) measurements capable of resolving transient flows with high temporal resolution. Therefore, high-frame rate measurements have enabled investigations on the evolution of the structure and dynamics of highly transient flows. These investigations play a critical role in understanding the fundamental physics of complex flows.
A detailed description for performing high-resolution, high-speed planar PIV to study a transient flow near the surface of a flat plate is presented here. Details for adjusting the parameter constraints such as image and recording properties, the laser sheet properties, and processing algorithms to adapt PIV for any flow of interest are included.

A procedure for studying transient flows near boundaries using high-resolution, high-speed particle image velocimetry (PIV) is described here. PIV is a non-intrusive measurement technique applicable to any optically accessible flow by optimizing several parameter constraints such as the image and recording properties, the laser sheet properties, and analysis algorithms.

Multi-dimensional and transient flows play a key role in many areas of science, engineering, and health sciences but are often not well understood. The ...

Micro-particle Image Velocimetry for Velocity Profile Measurements of Micro Blood Flows

Micro-particle image velocimetry (&mu;PIV) is used to visualize paired images of micro particles seeded in blood flows. The images are cross-correlated to give an accurate velocity profile. A protocol is presented for &mu;PIV measurements of blood flows in microchannels. At the scale of the microcirculation, blood cannot be considered a homogeneous fluid, as it is a suspension of flexible particles suspended in plasma, a Newtonian fluid. Shear rate, maximum velocity, velocity profile shape, and flow rate can be derived from these measurements. Several key parameters such as focal depth, particle concentration, and system compliance, are presented in order to ensure accurate, useful data along with examples and representative results for various hematocrits and flow conditions.

Micro-particle image velocimetry (&mu;PIV) is used to visualize paired images of micro particles seeded in blood flows which are cross-correlated to give an accurate velocity profile. Shear rate, maximum velocity, velocity profile shape, and flow rate, each of which has clinical applications, can be derived from these measurements.

Micro-particle image velocimetry (&mu;PIV) is used to visualize paired images of micro particles seeded in blood flows. The images are cross-correlated to ...

Bioengineering

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180&#176; Curved Artery Test Section

The arterial network in the human vasculature comprises of ubiquitously present blood vessels with complex geometries (branches, curvatures and tortuosity). Secondary flow structures are vortical flow patterns that occur in curved arteries due to the combined action of centrifugal forces, adverse pressure gradients and inflow characteristics. Such flow morphologies are greatly affected by pulsatility and multiple harmonics of physiological inflow conditions and vary greatly in size-strength-shape characteristics compared to non-physiological (steady and oscillatory) flows 1 - 7.
Secondary flow structures may ultimately influence the wall shear stress and exposure time of blood-borne particles toward progression of atherosclerosis, restenosis, sensitization of platelets and thrombosis 4 - 6, 8 - 13. Therefore, the ability to detect and characterize these structures under laboratory-controlled conditions is precursor to further clinical investigations.
A common surgical treatment to atherosclerosis is stent implantation, to open up stenosed arteries for unobstructed blood flow. But the concomitant flow perturbations due to stent installations result in multi-scale secondary flow morphologies 4 - 6. Progressively higher order complexities such as asymmetry and loss in coherence can be induced by ensuing stent failures vis-&#224;-vis those under unperturbed flows 5. These stent failures have been classified as &#34;Types I-to-IV&#34; based on failure considerations and clinical severity 14.
This study presents a protocol for the experimental investigation of the complex secondary flow structures due to complete transverse stent fracture and linear displacement of fractured parts (&#34;Type IV&#34;) in a curved artery model. The experimental method involves the implementation of particle image velocimetry (2C-2D PIV) techniques with an archetypal carotid artery inflow waveform, a refractive index matched blood-analog working fluid for phase-averaged measurements 15 - 18. Quantitative identification of secondary flow structures was achieved using concepts of flow physics, critical point theory and a novel wavelet transform algorithm applied to experimental PIV data 5, 6, 19 - 26.

Stent implants in stenosed arterial curvatures are prone to "Type IV" failures involving the complete transverse fracture of stents and linear displacement of the fractured parts. We present a protocol for detection of secondary flow (vortical) structures in a curved artery model, downstream of clinically relevant "Type IV" stent failures.

The arterial network in the human vasculature comprises of ubiquitously present blood vessels with complex geometries (branches, curvatures and ...

In vitro Assessment of Aortic Regurgitation Using Four-Dimensional Flow Magnetic Resonance Imaging

Aortic regurgitation (AR) refers to backward blood flow from the aorta into the left ventricle (LV) during ventricular diastole. The regurgitant jet arising from the complex shape is characterized by the three-dimensional flow and high-velocity gradient, sometimes limiting an accurate measurement of the regurgitant volume using 2D echocardiography. Recently developed four-dimensional flow magnetic resonance imaging (4D flow MRI) enables three-dimensional volumetric flow measurements, which can be used to accurately quantify the amount of the regurgitation. This study focuses on (i) magnetic resonance compatible AR model fabrication (dilatation, perforation, and prolapse) and (ii) systematic analysis of the performance of 4D flow MRI in AR quantification. The results indicated that the formation of the forward and backward jets over time was highly dependent on the types of AR origin. The amount of regurgitation volume bias for the model types were -7.04%, -33.21%, 6.75%, and 37.04% compared to the ground truth (48 mL) volume measured from the pump stroke volume. The largest error of the regurgitation fraction was around 12%. These results indicate that careful selection of imaging parameters is required when absolute regurgitation volume is important. The suggested in vitro flow phantom can easily be modified to simulate other valvular diseases such as aortic stenosis or bicuspid aortic valve (BAV) and can be used as a standard platform to test different MRI sequences in the future.

In vitro Assessment of Aortic Regurgitation Using Four-Dimensional Flow Magnetic Resonance Imaging

Aortic regurgitation is an aortic valve heart disease. This manuscript demonstrates how four-dimensional flow magnetic resonance imaging can evaluate aortic regurgitation using in vitro heart valves mimicking aortic regurgitation.

Aortic regurgitation (AR) refers to backward blood flow from the aorta into the left ventricle (LV) during ventricular diastole. The regurgitant jet ...

Manufacturing Abdominal Aorta Hydrogel Tissue-Mimicking Phantoms for Ultrasound Elastography Validation

Ultrasound (US) elastography, or elasticity imaging, is an adjunct imaging technique that utilizes sequential US images of soft tissues to measure the tissue motion and infer or quantify the underlying biomechanical characteristics. For abdominal aortic aneurysms (AAA), biomechanical properties such as changes in the tissue's elastic modulus and estimates of the tissue stress may be essential for assessing the need for the surgical intervention. Abdominal aortic aneurysms US elastography could be a useful tool to monitor AAA progression and identify changes in biomechanical properties characteristic of high-risk patients.
A preliminary goal in the development of an AAA US elastography technique is the validation of the method using a physically relevant model with known material properties. Here we present a process for the production of AAA tissue-mimicking phantoms with physically relevant geometries and spatially modulated material properties. These tissue phantoms aim to mimic the US properties, material modulus, and geometry of the abdominal aortic aneurysms. Tissue phantoms are made using a polyvinyl alcohol cryogel (PVA-c) and molded using 3D printed parts created using computer aided design (CAD) software. The modulus of the phantoms is controlled by altering the concentration of PVA-c and by changing the number of freeze-thaw cycles used to polymerize the cryogel. The AAA phantoms are connected to a hemodynamic pump, designed to deform the phantoms with the physiologic cyclic pressure and flows. Ultra sound image sequences of the deforming phantoms allowed for the spatial calculation of the pressure normalized strain and the identification of mechanical properties of the vessel wall. Representative results of the pressure normalized strain are presented.

Here we describe a method to manufacture aneurysmal, aortic tissue-mimicking phantoms for the use in testing ultrasound elastography. The combined use of computer-aided design (CAD) and 3-dimensional (3D) printing techniques produce aortic phantoms with predictable, complex geometries to validate the elastographic imaging algorithms with controlled experiments.

Ultrasound (US) elastography, or elasticity imaging, is an adjunct imaging technique that utilizes sequential US images of soft tissues to measure the ...

Meso-Scale Particle Image Velocimetry Studies of Neurovascular Flows In Vitro

Particle image velocimetry (PIV) is used in a wide variety of fields, due to the opportunity it provides for precisely visualizing and quantifying flows across a large spatiotemporal range. However, its implementation typically requires the use of expensive and specialized instrumentation, which limits its broader utility. Moreover, within the field of bioengineering, in vitro flow visualization studies are also often further limited by the high cost of commercially sourced tissue phantoms that recapitulate desired anatomical structures, particularly for those that span the mesoscale regime (i.e., submillimeter to millimeter length scales). Herein, we present a simplified experimental protocol developed to address these limitations, the key elements of which include 1) a relatively low-cost method for fabricating mesoscale tissue phantoms using 3-D printing and silicone casting, and 2) an open-source image analysis and processing framework that reduces the demand upon the instrumentation for measuring mesoscale flows (i.e., velocities up to tens of millimeters/second). Collectively, this lowers the barrier to entry for nonexperts, by leveraging resources already at the disposal of many bioengineering researchers. We demonstratethe applicability of this protocol within the context of neurovascular flow characterization; however, it is expected to be relevant to a broader range of mesoscale applications in bioengineering and beyond.

Meso-Scale Particle Image Velocimetry Studies of Neurovascular Flows In Vitro

Here we present simplified methods for fabricating transparent neurovascular phantoms and characterizing the flow therein. We highlight several important parameters and demonstrate their relationship to field accuracy.

Particle image velocimetry (PIV) is used in a wide variety of fields, due to the opportunity it provides for precisely visualizing and quantifying flows ...

Blood Flow Imaging with Ultrafast Doppler

The pulsed-Doppler effect is the main technique used in clinical echography to assess blood flow. Applied with conventional focused ultrasound Doppler modes, it has several limits. Firstly, a finely tuned signal filtering operation is needed to distinguish blood flows from surrounding moving tissues. Secondly, the operator must choose between localizing the blood flows or quantifying them. In the last two decades, ultrasound imaging has undergone a paradigm shift with the emergence of ultrafast ultrasound using unfocused waves. In addition to a hundredfold increase in framerate (up to 10000 Hz), this new technique also breaks the conventional quantification/localization trade-off, offering a complete blood flow mapping of the field of view and a simultaneous access to fine velocities measurements at the single-pixel level (down to 50 &#181;m). This data continuity in both spatial and temporal dimensions strongly improves the tissue/blood filtering process, which results in an increase sensitivity to small blood flow velocities (down to 1 mm/s). In this method paper, we aim to introduce the concept of ultrafast Doppler as well as its main parameters. Firstly, we summarize the physical principles of unfocused wave imaging. Then, we present the Doppler signal processing main steps. Particularly, we explain the practical implementation of the critical tissue/blood flow separation algorithms and on the extraction of velocities from these filtered data. This theoretical description is supplemented by in vitro experiences. A tissue phantom embedding a canal with flowing blood-mimicking fluid is imaged with a research programmable ultrasound system. A blood flow image is obtained and the flow characteristics are displayed for several pixels in the canal. Finally, a review of in vivo applications is proposed, showing examples in several organs such as carotids, kidney, thyroid, brain and heart.

This protocol shows how to apply ultrafast ultrasound Doppler imaging to quantify blood flows. After a 1 s long acquisition, the experimenter has access to a movie of the full field of view with axial velocity values for each pixel every &#8776;0.3 ms (depending on the ultrasound time of flight).

The pulsed-Doppler effect is the main technique used in clinical echography to assess blood flow. Applied with conventional focused ultrasound Doppler ...

Optical Coherence Tomography Based Biomechanical Fluid-Structure Interaction Analysis of Coronary Atherosclerosis Progression

In this paper, we present a complete workflow for the biomechanical analysis of atherosclerotic plaque in the coronary vasculature. With atherosclerosis as one of the leading causes of global death, morbidity and economic burden, novel ways of analyzing and predicting its progression are needed. One such computational method is the use of fluid-structure interaction (FSI) to analyze the interaction between the blood flow and artery/plaque domains. Coupled with in vivo imaging, this approach could be tailored to each patient, assisting in differentiating between stable and unstable plaques. We outline the three-dimensional reconstruction process, making use of intravascular Optical Coherence Tomography (OCT) and invasive coronary angiography (ICA). The extraction of boundary conditions for the simulation, including replicating the three-dimensional motion of the artery, is discussed before the setup and analysis is conducted in a commercial finite element solver. The procedure for describing the highly nonlinear hyperelastic properties of the artery wall and the pulsatile blood velocity/pressure is outlined along with setting up the system coupling between the two domains. We demonstrate the procedure by analyzing a non-culprit, mildly stenotic, lipid-rich plaque in a patient following myocardial infarction. Established and emerging markers related to atherosclerotic plaque progression, such as wall shear stress and local normalized helicity, respectively, are discussed and related to the structural response in the artery wall and plaque. Finally, we translate the results to potential clinical relevance, discuss limitations, and outline areas for further development. The method described in this paper shows promise for aiding in the determination of sites at risk of atherosclerotic progression and, hence, could assist in managing the significant death, morbidity, and economic burden of atherosclerosis.

There is a need to determine which atherosclerotic lesions will progress in the coronary vasculature to guide intervention before myocardial infarction occurs. This article outlines the biomechanical modeling of arteries from Optical Coherence Tomography using fluid-structure interaction techniques in a commercial finite element solver to help predict this progression.

In this paper, we present a complete workflow for the biomechanical analysis of atherosclerotic plaque in the coronary vasculature. With atherosclerosis ...

Measurement of Pulse Propagation Velocity, Distensibility and Strain in an Abdominal Aortic Aneurysm Mouse Model

An abdominal aortic aneurysm (AAA) is defined as a localized dilation of the abdominal aorta that exceeds the maximal intraluminal diameter (MILD) by 1.5 times of its original size. Clinical and experimental studies have shown that small aneurysms may rupture, while a subpopulation of large aneurysms may remain stable. Thus, in addition to the measurement of intraluminal diameter of the aorta, knowledge of structural traits of the vessel wall may provide important information to assess the stability of the AAA. Aortic stiffening has recently emerged as a reliable tool to determine early changes in the vascular wall. Pulse propagation velocity (PPV) along with the distensibility and radial strain are highly useful ultrasound-based methods relevant for assessing aortic stiffness. The primary purpose of this protocol is to provide a comprehensive technique for the use of ultrasound imaging system to acquire images and analyze the structural and functional properties of the aorta as determined by MILD, PPV, distensibility and radial strain.

This manuscript describes a detailed protocol for using high frequency ultrasound imaging to measure luminal diameter, pulse propagation velocity, distensibility and radial strain on a mouse model of abdominal aortic aneurysm.

An abdominal aortic aneurysm (AAA) is defined as a localized dilation of the abdominal aorta that exceeds the maximal intraluminal diameter (MILD) by 1.5 ...

Immunology and Infection

Brain Pericyte Calcium and Hemodynamic Imaging in Transgenic Mice In Vivo

Recent advances in protein biology and mouse genetics have made it possible to measure intracellular calcium fluctuations of brain cells in vivo and to correlate this with local hemodynamics. This protocol uses transgenic mice that have been prepared with a chronic cranial window and express the genetically encoded calcium indicator, RCaMP1.07, under the &#945;-smooth muscle actin promoter to specifically label mural cells, such as vascular smooth muscle cells and ensheathing pericytes. Steps are outlined on how to prepare a tail vein catheter for intravenous injection of fluorescent dyes to trace blood flow, as well as how to measure brain pericyte calcium and local blood vessel hemodynamics (diameter, red blood cell velocity, etc.) by two photon microscopy in vivo through the cranial window in ketamine/xylazine anesthetized mice. Finally, details are provided for the analysis of calcium fluctuations and blood flow movies via the image processing algorithms developed by Barrett et al. 2018, with an emphasis on how these processes can be adapted to other cellular imaging data.

Brain Pericyte Calcium and Hemodynamic Imaging in Transgenic Mice In Vivo

This protocol presents steps to acquire and analyze fluorescent calcium images from brain ensheathing pericytes and blood flow data from nearby blood vessels in anesthetized mice. These techniques are useful for studies of mural cell physiology and can be adapted to investigate calcium transients in any cell type.

Recent advances in protein biology and mouse genetics have made it possible to measure intracellular calcium fluctuations of brain cells in vivo and to ...

Particle Image Velocimetry Investigation of Hemodynamics via Aortic Phantom

Summary

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Chapters in this video

High-speed Particle Image Velocimetry Near Surfaces

Micro-particle Image Velocimetry for Velocity Profile Measurements of Micro Blood Flows

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section

In vitro Assessment of Aortic Regurgitation Using Four-Dimensional Flow Magnetic Resonance Imaging

Manufacturing Abdominal Aorta Hydrogel Tissue-Mimicking Phantoms for Ultrasound Elastography Validation

Meso-Scale Particle Image Velocimetry Studies of Neurovascular Flows In Vitro

Blood Flow Imaging with Ultrafast Doppler

Optical Coherence Tomography Based Biomechanical Fluid-Structure Interaction Analysis of Coronary Atherosclerosis Progression

Measurement of Pulse Propagation Velocity, Distensibility and Strain in an Abdominal Aortic Aneurysm Mouse Model

Brain Pericyte Calcium and Hemodynamic Imaging in Transgenic Mice In Vivo