The software and technical support were courtesy of Correlated Solutions Incorporated (www.correlatedsolutions.com).

NOTE: The procedures described below were performed as part of a protocol approved by the Institutional Animal Care and Use Committee at the University of South Carolina in Columbia, South Carolina.
1. Tissue Acquisition and Dissection
<ol>
	<li>Sterilize all surgical tools prior to tissue dissection. Autoclave surgical scissors and fine standard forceps as well as surgical blades under pressure of 15 psi and temperature of 121 &#176;C for 15 min.</li>
	<li>Acquire one set of fresh porcine (7 month old Landrace males, 60-70 kg) kidneys with intact aorta from a local slaughterhouse. Transport tissue back to the laboratory in iced 1% phosphate buffered saline (PBS) solution.</li>
	<li>Immediately upon arrival, isolate the abdominal aorta from surrounding tissue using surgical scissors and forceps.</li>
	<li>Wash the vessel three times using a 50 ml syringe filled with PBS (pH 7.2). Using the scissors and forceps, remove as much perivascular tissue as possible without compromising the integrity of the sample.</li>
	<li>Vertically position a sharp razor blade onto the middle section of the vessel and make sure it is perpendicular to the vessel longitudinal axis. Create two ring samples each with a width of approximately 20 mm by applying three sequential circumferential cuts with the razor blade.</li>
	<li>Vertically position a sharp razor blade onto one ring sample such that the blade is oriented in the radial direction. Apply an acute force to yield a radial cut, which results in a strip-shaped sample for uniaxial mechanical testing. Place the sample in a 100 mm glass Petri dish and submerge in PBS until the application of surface speckling. Repeat for the second ring sample.</li>
</ol>
2. Creation of Surface Speckle Pattern
<ol>
	<li>Connect the airbrush to the pressure valve.</li>
	<li>Adjust the nozzle diameter of the airbrush to yield speckles of 60-100 &#181;m (appropriate range for nozzle diameter should be determined from preliminary studies).</li>
	<li>Pour approximately 2 ml of black tissue marking dye into the gravity feeder of the airbrush.</li>
	<li>Place the airbrush approximately 0.5 m away from the sample.</li>
	<li>Remove the sample from the Petri dish. Spray tissue marking dye on the intimal surface of the sample for approximately 5 sec under a spraying pressure of 100 psi. Repeat three times to ensure that the speckle pattern uniformly covers the sample surface.</li>
</ol>
3. Performance of Experiments
<ol>
	<li>Attach each end of the sample to a plastic strip (1 cm width x 1 cm length x 0.5 cm thickness) using a tissue adhesive. Place the sample on a tissue cutting board. Position the sample such that it lies flat and measure its dimensions using a digital caliper.</li>
	<li>Initiate system controls for mechanical testing. On the system controls home screen, select &#34;Waveform&#34; on the taskbar located on the &#34;Setup&#34; tab.</li>
	<li>Adjust the position of the upper grip of the mechanical tester to -4 mm (4 mm extension relative to the designated initial position in the system). Gently secure one plastic strip (attached to the specimen in 3.1) into the upper grip of the mechanical tester and allow the sample to hang freely. Use the digital caliper to ensure that the distance between the sample and the upper grip is less than 2 mm.</li>
	<li>Manually adjust the position of the lower grip so that the free end of the sample can be secured without extension. Gently secure the plastic strip attached to the free end of the sample into the lower grip of the mechanical tester.
	<ol>
		<li>Use the digital caliper to ensure that the distance between the sample and the lower grip is less than 2 mm. Zero the system load cell. Measure the length of the sample and use this as the reference length for calculation of global circumferential strains.</li>
	</ol>
	</li>
	<li>Enter the mechanical testing protocol. The protocol used in this demonstration entails 4 uniaxial displacement cycles which extend the sample length by 18% at a displacement rate of 0.01 mm/sec.</li>
	<li>Intermittently spray PBS on the sample throughout the remaining testing protocol to ensure that it remains hydrated.</li>
	<li>Mount the camera (5 mega pixel camera, 100 mm lens, pixel size of 3.49 &#181;m) onto a tripod which is positioned 1.5 m from the loading frame. Ensure the camera and sample surface are perpendicular by setting the camera to the lowest available field of depth and manipulating its alignment such that the entire field of view is in focus.</li>
	<li>Open the image capture software.</li>
	<li>Select &#34;PGR-2&#34; in the option &#34;Select system&#34;.</li>
	<li>Select the project path to save the images to be analyzed.</li>
	<li>Click the &#34;Time Square&#34; icon and specify the acquisition interval as 5 sec.</li>
	<li>Adjust the exposure, numerical aperture, and focus of the lens so as to get a clear view of the sample.</li>
	<li>Adjust the position of LED to provide adequate lighting on the sample.</li>
	<li>Click the &#34;Start&#34; icon in the image capture software to obtain an image of the specimen surface.</li>
	<li>Open the image analysis software.</li>
	<li>Import the image obtained. Zoom in on an individual speckle, and then count number of pixels within this individual speckle. 
	Note: Identify a representative black speckle. Define the speckle size as the linear distance between pixels on both sides of the speckle that have similar high values. For an acceptable speckle size, the number of pixels across the width of a typical speckle should be greater than 3 pixels. To improve spatial resolution in the measurements, most speckles should have no more than 5-7 pixels across the width of the speckle, whenever possible. Thus, a typical speckle for this case would range between 10 &#956;m at the smallest and 23 &#956;m at the largest in linear dimension. To determine an appropriate subset size, a typical subset should have at least 3 white and 3 black speckles across its width. If a typical speckle is 5 pixels in linear dimension, then each 31x31 subset would be at least 105 &#956;m in linear dimension. The spacing between subset centers should be at least 1/6 of the linear dimension. Thus, for a 31x31 subset size, the distance is 5 pixels which represents 18 &#956;m in linear distance.</li>
	<li>After verifying the quality of speckle pattern, simultaneously click the &#34;Run&#34; icon in the system and &#34;Start&#34; icon in the image capture software to start the test.</li>
	<li>Capture a series of images throughout testing using the camera and image capture software.</li>
</ol>
4. Clean-up Procedures after the Experiment
<ol>
	<li>Place the discarded sample in biohazard bag and close the bag. Call the Department of Environmental Health and Safety (EHS) at the University of South Carolina for proper disposal.</li>
	<li>Prepare a phosphate-free disinfectant solution with a 1:64 dilution ratio of detergent disinfectant to distilled water. Soak the surgical tools in this solution for 20 min.</li>
	<li>Thoroughly rinse the items described in 4.2 with distilled water. Dry the tools using a paper towel and then spray them with a 70% ethanol solution. Once again dry the surgical tools using a paper towel and put them back in the surgical toolbox.</li>
</ol>
5. Image Analysis to Measure the Local Strain Field
<ol>
	<li>Open the image analysis software.</li>
	<li>Click the &#34;Speckle images&#34; tab, select all images which need to be analyzed.</li>
	<li>Click on rectangle tool and select the area of interest in the first image.</li>
	<li>Enter subset size 41&#215;41 pixels and step size 5 pixels.</li>
	<li>Click start analysis tab in the software, select the interpolation as optimized 8-trap; select the criterion as zero-normalized squared differences and subset weights option as Gaussian.</li>
	<li>Set the threshold options as default in the software.</li>
	<li>Click on post-processing sub tab in start analysis tab. Click option strain computation and leave filter size and type of filter as default in software. Select tensor type Lagrange.</li>
	<li>Select data tab and then select any analyzed image for visualization of the surface strain field.</li>
</ol>

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</ol>

The authors have no potential conflicts of interest.

Although previous studies have used a wide-range of dye-tracking video methods to assess sample strain 18,20,21,23,24, our present aim is to provide a comprehensive methodology to couple uniaxial tensile testing with 2D-DIC for assessment of surface strains on vascular tissue samples. With a high resolution camera and in-house image analysis software, the strain field can be measured within a predetermined surface region as the sample undergoes uniaxial loading. Of particular relevance to mechanical testing of...

A rich history of research spanning over 50 years has been focused on quantifying the mechanical properties of vascular tissues. These studies allow us to better understand both the physiological and pathological behavior of blood vessels, provide a basis for evaluating the efficacy/compatibility of endovascular devices, and aid in the design and fabrication of engineered vascular constructs1-6. Accurate measurement of the mechanical response of soft tissues and constitutive modeling of their mechanical properties is inherently challenging due to the mechanical heterogeneity, anisotropy, and nonlinearity exhibited by most tissue types. Moreover, experimental measurements are often confounded by local complexities introduced at sample-grip interfaces in the course of mechanical testing (i.e., bending, friction, stress concentrations, tearing) and the inevitable transition of mechanical properties once tissue is excised from the living animal.
A uniaxial tensile experiment is among the simplest mechanical tests that can be performed on a specimen made of a solid material, and is often used to assess the mechanical response of vascular tissue. Results from these experiments provide useful preliminary information for both native and engineered tissue sources, and can be used to compare the effects of certain treatments, disease states, or pharmacological compounds on the mechanical behavior of the vascular wall 7-11.
Uniaxial mechanical testing of soft tissues is typically performed on samples with relatively uniform geometries, which are most commonly dog-bone or ring shaped 7,8,12-14. However, significant departure from these idealized geometries can occur due to challenges associated with tissue dissection, isolation, and clamping within the testing system. Any non-uniformity in geometry will ultimately give rise to heterogeneous stress and strain fields when the sample is subjected to uniaxial extension, with the degree of heterogeneity dependent on actual sample shape, as well as sample size (relative to the grips) and the mechanical properties of the material 9,15,16. When field heterogeneities are significant, sample strain calculations based on the relative grip positions are inaccurate and thus an insufficient basis for assessing mechanical behavior.
Video analysis systems have been widely used for strain measurements of soft tissues, often using high contrast dye markers applied to the specimen surface 17,18. Digital image correlation, an optical metrological technique which measures full-field surface strain by comparing gray level intensity values on the specimen&#39;s surface before and after deformation, has been used in conjunction with video analyses of soft tissues 19-21. There are several advantages of digital image correlation compared to interferometric methods that may be employed for measurements. First, as a non-contacting measurement technique, it minimizes the confounding effects of modifying material properties due to the way in which the measurement system affects the specimen. Second, it requires a much less stringent measurement environment and has a wider range of sensitivity and resolution than other methods. Third, endowed with the capability of capturing a full field of view, this technique can characterize both the average and the local mechanical responses. For detailed explanation of the method, readers are encouraged to see the book by Sutton 22.
To obtain strain fields on the specimen surface, a two-dimensional digital image correlation technique (2D-DIC) can be used. In short, images of the specimen are captured at unloaded and various loaded states. The first image is divided into small squares called subsets (M&#215;M pixels) which form a mesh for subsequent calculation of 2D strain fields. The position of each square in the deformed specimen is obtained using an image matching algorithm. The motion of each square is then tracked, image-by-image, yielding displacement fields which can then be used to derive deformation gradients and strains via a variety of methods, including polynomial fitting or finite element interpolation. In the present manuscript, we provide a detailed methodology for assessment of the surface strain fields on native vascular tissues via integration of uniaxial tensile testing and 2D-DIC.

<table><tbody><tr><td>Uniaxial tensile mechanical tester</td><td>Enduratec</td><td>3230 AT/HR</td><td></td></tr><tr><td>Blue tissue marking dye</td><td>http://www.ebay.com/itm/Tissue-Marking-Dye-in-Bottles-2oz-Bottle-1-ea-/201193551510?pt=LH_DefaultDomain_0&amp;amp;hash=item2ed811f696</td><td></td><td></td></tr><tr><td>Sprayer</td><td>Anest-iwata</td><td>CM-B</td><td>Custom Micron B</td></tr><tr><td>Camera</td><td>Point Grey</td><td>GS2-GE-50S5M-C</td><td></td></tr><tr><td>Lens</td><td>Tokina</td><td>AT-X M100</td><td></td></tr><tr><td>Vascular tissue</td><td>Caughman Inc</td><td></td><td></td></tr><tr><td>0.9% Sodium Chloride Injection PBS</td><td>BAXTER HEALTHCARE CORP.</td><td></td><td></td></tr><tr><td>Vic_snap</td><td>Correlated Solutions</td><td></td><td></td></tr><tr><td>Vic_2D</td><td>Correlated Solutions</td><td></td><td></td></tr><tr><td>Wintest 4.1</td><td>Bose ElectroForce</td><td></td><td></td></tr><tr><td>Tissue adhesive</td><td>&amp;nbsp;3M Vetbond&amp;nbsp;</td><td>1469SB</td><td></td></tr><tr><td>Disinfectant&amp;nbsp;</td><td>Fisher Scientific</td><td>04-355-13</td><td>Decon BDD Bacdown Detergent Disinfectant</td></tr></tbody></table>

using digital image correlation to characterize local strains on vascular tissue specimens

Characterization of the mechanical behavior of biological and engineered soft tissues is a central component of fundamental biomedical research and product development. Stress-strain relationships are typically obtained from mechanical testing data to enable comparative assessment among samples and in some cases identification of constitutive mechanical properties. However, errors may be introduced through the use of average strain measures, as significant heterogeneity in the strain field may result from geometrical non-uniformity of the sample and stress concentrations induced by mounting/gripping of soft tissues within the test system. When strain field heterogeneity is significant, accurate assessment of the sample mechanical response requires measurement of local strains. This study demonstrates a novel biomechanical testing protocol for calculating local surface strains using a mechanical testing device coupled with a high resolution camera and a digital image correlation technique. A series of sample surface images are acquired and then analyzed to quantify the local surface strain of a vascular tissue specimen subjected to ramped uniaxial loading. This approach can improve accuracy in experimental vascular biomechanics and has potential for broader use among other native soft tissues, engineered soft tissues, and soft hydrogel/polymeric materials. In the video, we demonstrate how to set up the system components and perform a complete experiment on native vascular tissue.

The mechanical data acquired from a ramped uniaxial extension test on vascular tissue consists of load versus applied sample displacement relationships at a given displacement rate. In this study, 2D-DIC in conjunction with uniaxial mechanical testing is used to measure the surface strain fields of the specimen in orthogonal directions at various deformed states. The viscoelastic nature of vascular tissue is manifested by the notable degree of hysteresis in the load-displacement curves pr...

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Using Digital Image Correlation to Characterize Local Strains on Vascular Tissue Specimens

We describe the use of digital image correlation to characterize the local surface strain field on vascular tissue samples subjected to uniaxial tensile testing. These measurements facilitate precise quantification of the sample mechanical response and the generation of constitutive stress-strain relations.

Characterization of the mechanical behavior of biological and engineered soft tissues is a central component of fundamental biomedical research and ...

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Research

JoVE Journal

Biology

9.4K Views.  University of South Carolina. We describe the use of digital image correlation to characterize the local surface strain field on vascular tissue samples subjected to uniaxial tensile testing. These measurements facilitate precise quantification of the sample mechanical response and the generation of constitutive stress-strain relations.

Video: Using Digital Image Correlation to Characterize Local Strains on Vascular Tissue Specimens

Optical Frequency Domain Imaging of Ex vivo Pulmonary Resection Specimens: Obtaining One to One Image to Histopathology Correlation

Lung cancer is the leading cause of cancer-related deaths1. Squamous cell and small cell cancers typically arise in association with the conducting airways, whereas adenocarcinomas are typically more peripheral in location. Lung malignancy detection early in the disease process may be difficult due to several limitations: radiological resolution, bronchoscopic limitations in evaluating tissue underlying the airway mucosa and identifying early pathologic changes, and small sample size and/or incomplete sampling in histology biopsies. High resolution imaging modalities, such as optical frequency domain imaging (OFDI), provide non-destructive, large area 3-dimensional views of tissue microstructure to depths approaching 2 mm in real time (Figure 1)2-6. OFDI has been utilized in a variety of applications, including evaluation of coronary artery atherosclerosis6,7 and esophageal intestinal metaplasia and dysplasia6,8-10.
Bronchoscopic OCT/OFDI has been demonstrated as a safe in vivo imaging tool for evaluating the pulmonary airways11-23 (Animation). OCT has been assessed in pulmonary airways16,23 and parenchyma17,22 of animal models and in vivo human airway14,15. OCT imaging of normal airway has demonstrated visualization of airway layering and alveolar attachments, and evaluation of dysplastic lesions has been found useful in distinguishing grades of dysplasia in the bronchial mucosa11,12,20,21. OFDI imaging of bronchial mucosa has been demonstrated in a short bronchial segment (0.8 cm)18. Additionally, volumetric OFDI spanning multiple airway generations in swine and human pulmonary airways in vivo has been described19. Endobronchial OCT/OFDI is typically performed using thin, flexible catheters, which are compatible with standard bronchoscopic access ports. Additionally, OCT and OFDI needle-based probes have recently been developed, which may be used to image regions of the lung beyond the airway wall or pleural surface17.
While OCT/OFDI has been utilized and demonstrated as feasible for in vivo pulmonary imaging, no studies with precisely matched one-to-one OFDI:histology have been performed. Therefore, specific imaging criteria for various pulmonary pathologies have yet to be developed. Histopathological counterparts obtained in vivo consist of only small biopsy fragments, which are difficult to correlate with large OFDI datasets. Additionally, they do not provide the comprehensive histology needed for registration with large volume OFDI. As a result, specific imaging features of pulmonary pathology cannot be developed in the in vivo setting. Precisely matched, one-to-one OFDI and histology correlation is vital to accurately evaluate features seen in OFDI against histology as a gold standard in order to derive specific image interpretation criteria for pulmonary neoplasms and other pulmonary pathologies. Once specific imaging criteria have been developed and validated ex vivo with matched one-to-one histology, the criteria may then be applied to in vivo imaging studies. Here, we present a method for precise, one to one correlation between high resolution optical imaging and histology in ex vivo lung resection specimens. Throughout this manuscript, we describe the techniques used to match OFDI images to histology. However, this method is not specific to OFDI and can be used to obtain histology-registered images for any optical imaging technique. We performed airway centered OFDI with a specialized custom built bronchoscopic 2.4 French (0.8 mm diameter) catheter. Tissue samples were marked with tissue dye, visible in both OFDI and histology. Careful orientation procedures were used to precisely correlate imaging and histological sampling locations. The techniques outlined in this manuscript were used to conduct the first demonstration of volumetric OFDI with precise correlation to tissue-based diagnosis for evaluating pulmonary pathology24. This straightforward, effective technique may be extended to other tissue types to provide precise imaging to histology correlation needed to determine fine imaging features of both normal and diseased tissues.

Optical Frequency Domain Imaging of Ex vivo Pulmonary Resection Specimens: Obtaining One to One Image to Histopathology Correlation

A method to image ex vivo pulmonary resection specimens with optical frequency domain imaging (OFDI) and obtain precise correlation to histology is described, which is essential to developing specific OFDI interpretation criteria for pulmonary pathology. This method is applicable to other tissue types and imaging techniques to obtain precise imaging to histology correlation for accurate image interpretation and assessment. Imaging criteria established with this technique would then be applicable to image assessment in future in vivo studies.

Lung cancer is the leading cause of cancer-related deaths1. Squamous cell and small cell cancers typically arise in association with the conducting ...

Bioengineering

Monitoring the Wall Mechanics During Stent Deployment in a Vessel

Clinical trials have reported different restenosis rates for various stent designs1. It is speculated that stent-induced strain concentrations on the arterial wall lead to tissue injury, which initiates restenosis2-7. This hypothesis needs further investigations including better quantifications of non-uniform strain distribution on the artery following stent implantation. A non-contact surface strain measurement method for the stented artery is presented in this work. ARAMIS stereo optical surface strain measurement system uses two optical high speed cameras to capture the motion of each reference point, and resolve three dimensional strains over the deforming surface8,9. As a mesh stent is deployed into a latex vessel with a random contrasting pattern sprayed or drawn on its outer surface, the surface strain is recorded at every instant of the deformation. The calculated strain distributions can then be used to understand the local lesion response, validate the computational models, and formulate hypotheses for further in vivo study.

Stent-induced arterial strain distributions are characterized using an optical surface strain measurement system. This visualization technique is used to gain insights into the impact of stent implantation on the host vessel.

Clinical trials have reported different restenosis rates for various stent designs1. It is speculated that stent-induced strain concentrations on the ...

Observing and Quantifying Fibroblast-mediated Fibrin Gel Compaction

Cells embedded in collagen and fibrin gels attach and exert traction forces on the fibers of the gel. These forces can lead to local and global reorganization and realignment of the gel microstructure. This process proceeds in a complex manner that is dependent in part on the interplay between the location of the cells, the geometry of the gel, and the mechanical constraints on the gel. To better understand how these variables produce global fiber alignment patterns, we use time-lapse differential interference contrast (DIC) microscopy coupled with an environmentally controlled bioreactor to observe the compaction process between geometrically spaced explants (clusters of fibroblasts). The images are then analyzed with a custom image processing algorithm to obtain maps of the strain. The information obtained from this technique can be used to probe the mechanobiology of various cell-matrix interactions, which has important implications for understanding processes in wound healing, disease development, and tissue engineering applications.

Time-lapse microscopy and image processing techniques were used to observe and analyze fibroblast-mediated gel compaction and fibrin fiber realignment in an environmentally controlled bioreactor over a 48 hr&#160;period.

Cells embedded in collagen and fibrin gels attach and exert traction forces on the fibers of the gel. These forces can lead to local and global ...

Intermediate Strain Rate Material Characterization with Digital Image Correlation

The mechanical response of a material under dynamic load is typically different than its behavior under static conditions; therefore, the common quasistatic equipment and procedures used for material characterization are not applicable for materials under dynamic loads. The dynamic response of a material depends on its deformation rate and is broadly categorized into high (i.e., greater than 200/s), intermediate (i.e., 10&#8722;200/s) and low strain rate regimes (i.e., below 10/s). Each of these regimes calls for specific facilities and testing protocols to ensure the reliability of the acquired data. Due to the limited access to high-speed servo-hydraulic facilities and validated testing protocols, there is a noticeable gap in the results at the intermediate strain rate. The current manuscript presents a validated protocol for the characterization of different materials at these intermediate strain rates. Strain gauge instrumentation and digital image correlation protocols are also included as complimentary modules to extract the utmost level of detailed data from every single test. Examples of raw data, obtained from a variety of materials and test setups (e.g., tensile and shear) is presented and the analysis procedure used to process the output data is described. Finally, the challenges of dynamic characterization using the current protocol, along with the limitations of the facility and methods of overcoming potential problems are discussed.

Here we present a methodology for the dynamic characterization of tensile specimens at intermediate strain rates using a high-speed servo-hydraulic load frame. Procedures for strain gauge instrumentation and analysis, as well as for digital image correlation strain measurements on the specimens, are also defined.

The mechanical response of a material under dynamic load is typically different than its behavior under static conditions; therefore, the common ...

Engineering

A Method to Estimate Cadaveric Femur Cortical Strains During Fracture Testing Using Digital Image Correlation

This protocol describes the method using digital image correlation to estimate cortical strain from high speed video images of the cadaveric femoral surface obtained from mechanical testing. This optical method requires a texture of many contrasting fiduciary marks on a solid white background for accurate tracking of surface deformation as loading is applied to the specimen. Immediately prior to testing, the surface of interest in the camera view is painted with a water-based white primer and allowed to dry for several minutes. Then, a black paint is speckled carefully over the white background with special consideration for the even size and shape of the droplets. Illumination is carefully designed and set such that there is optimal contrast of these marks while minimizing reflections through the use of filters. Images were obtained through high speed video capture at up to 12,000 frames/s. The key images prior to and including the fracture event are extracted and deformations are estimated between successive frames in carefully sized interrogation windows over a specified region of interest. These deformations are then used to compute surface strain temporally during the fracture test. The strain data is very useful for identifying fracture initiation within the femur, and for eventual validation of proximal femur fracture strength models derived from Quantitative Computed Tomography-based Finite Element Analysis (QCT/FEA).

In this protocol, the femur surface strains are estimated during fracture testing using the digital image correlation technique. The novelty of the method involves application of a high-contrast stochastic speckle pattern on the femur surface, carefully specified illumination, high speed video capture, and digital image correlation analysis for strain calculations.

This protocol describes the method using digital image correlation to estimate cortical strain from high speed video images of the cadaveric femoral ...

Measuring Local Tissue Strains in Tendons via Open-Source Digital Image Correlation

There is considerable scientific interest in understanding the strains that tendon cells experience in situ and how these strains influence tissue remodeling. Based on this interest, several analytical techniques have been developed to measure local tissue strains within tendon explants during loading. However, in several cases, the accuracy and sensitivity of these techniques have not been reported, and none of the algorithms are publicly available. This has made it difficult for the more widespread measurement of local tissue strains in tendon explants. Therefore, the objective of this paper was to create a validated analysis tool for measuring local tissue strains in tendon explants that is readily available and easy to use. Specifically, a publicly available augmented-Lagrangian digital image correlation (ALDIC) algorithm was adapted for measuring 2D strains by tracking the displacements of cell nuclei within mouse Achilles tendons under uniaxial tension. Additionally, the accuracy of the calculated strains was validated by analyzing digitally transformed images, as well as by comparing the strains with values determined from an independent technique (i.e., photobleached lines). Finally, a technique was incorporated into the algorithm to reconstruct the reference image using the calculated displacement field, which can be used to assess the accuracy of the algorithm in the absence of known strain values or a secondary measurement technique. The algorithm is capable of measuring strains up to 0.1 with an accuracy of 0.00015. The technique for comparing a reconstructed reference image with the actual reference image successfully identified samples that had erroneous data and indicated that, in samples with good data, approximately 85% of the displacement field was accurate. Finally, the strains measured in mouse Achilles tendons were consistent with the prior literature. Therefore, this algorithm is a highly useful and adaptable tool for accurately measuring local tissue strains in tendons.

Measuring Local Tissue Strains in Tendons via Open-Source Digital Image Correlation

This paper describes an open-source digital image correlation algorithm for measuring local 2D tissue strains within tendon explants. The accuracy of the technique has been validated using multiple techniques, and it is available for public use.

There is considerable scientific interest in understanding the strains that tendon cells experience in situ and how these strains influence tissue ...

Controlled Strain of 3D Hydrogels under Live Microscopy Imaging

External forces are an important factor in tissue formation, development, and maintenance. The effects of these forces are often studied using specialized in vitro stretching methods. Various available systems use 2D substrate-based stretchers, while the accessibility of 3D techniques to strain soft hydrogels, is more restricted. Here, we describe a method that allows external stretching of soft hydrogels from their circumference, using an elastic silicone strip as the sample carrier. The stretching system utilized in this protocol is constructed from 3D-printed parts and low-cost electronics, making it simple and easy to replicate in other labs. The experimental process begins with polymerizing thick (&#62;100 &#956;m) soft fibrin hydrogels (Elastic Modulus of ~100 Pa) in a cut-out at the center of a silicone strip. Silicone-gel constructs are then attached to the printed-stretching device and placed on the confocal microscope stage. Under live microscopy the stretching device is activated, and the gels are imaged at various stretch magnitudes. Image processing is then used to quantify the resulting gel deformations, demonstrating relatively homogenous strains and fiber alignment throughout the gel&#8217;s 3D thickness (Z-axis). Advantages of this method include the ability to strain extremely soft hydrogels in 3D while executing in situ microscopy, and the freedom to manipulate the geometry and size of the sample according to the user&#8217;s needs. Additionally, with proper adaptation, this method can be used to stretch other types of hydrogels (e.g., collagen, polyacrylamide or polyethylene glycol) and can allow for analysis of cells and tissue response to external forces under more biomimetic 3D conditions.

The presented method involves uniaxial stretching of 3D soft hydrogels embedded in silicone rubber while allowing live confocal microscopy. Characterization of the external and internal hydrogel strains as well as fiber alignment are demonstrated. The device and protocol developed can assess the response of cells to various strain regimes.

External forces are an important factor in tissue formation, development, and maintenance. The effects of these forces are often studied using specialized ...

A Method to Study the Correlation Between Local Collagen Structure and Mechanical Properties of Atherosclerotic Plaque Fibrous Tissue

The rupture of atherosclerotic plaques in coronary and carotid arteries is the primary cause of fatal cardiovascular events. However, the rupture mechanics of the heterogeneous, highly collagenous plaque tissue, and how this is related to the tissue's fibrous structure, are not known yet. Existing pipelines to study plaque mechanics are limited to obtaining only gross mechanical characteristics of the plaque tissue, based on the assumption of structural homogeneity of the tissue. However, fibrous plaque tissue is structurally heterogeneous, arguably mainly due to local variation in the collagen fiber architecture.
The mechano-imaging pipeline described here has been developed to study the heterogeneous structural and mechanical plaque properties. In this pipeline, the tissue's local collagen architecture is characterized using multiphoton microscopy (MPM) with second-harmonic generation (SHG), and the tissue's failure behavior is characterized under uniaxial tensile testing conditions using digital image correlation (DIC) analysis. This experimental pipeline enables correlation of the local predominant angle and dispersion of collagen fiber orientation, the rupture behavior, and the strain fingerprints of the fibrous plaque tissue. The obtained knowledge is key to better understand, predict, and prevent atherosclerotic plaque rupture events.

We have developed a mechano-imaging pipeline to study the heterogeneous structural and mechanical atherosclerotic plaque properties. This pipeline enables correlation of the local predominant angle and dispersion of collagen fiber orientation, the rupture behavior, and the strain fingerprints of the fibrous plaque tissue.

The rupture of atherosclerotic plaques in coronary and carotid arteries is the primary cause of fatal cardiovascular events. However, the rupture ...

A Novel Approach to Monitoring Graft Neovascularization in the Human Gingiva

Laser speckle contrast imaging (LSCI) is a novel method for measuring superficial blood perfusion over large areas. Since it is non-invasive and avoids direct contact with the measured area, it is suitable for monitoring blood flow changes during wound healing in human patients. Vestibuloplasty is periodontal surgery to the oral vestibule, aiming to restore vestibular depth with simultaneous enlargement of the keratinized gingiva. In this special clinical case, a split thickness flap was elevated at the first upper premolar and a xenogenic collagen matrix was adapted to the resulting recipient bed. LSCI was used to monitor the re- and neovascularization of the graft and the surrounding mucosa for one year. A protocol is introduced for the correct adjustment of microcirculation measurement in the oral mucosa, highlighting difficulties and possible failures.
The clinical case study presented demonstrated that &#8212; following the appropriate protocol &#8212; LSCI is a suitable and reliable method for following up microcirculation in a healing wound in the human oral mucosa and gives useful information on graft integration.

This study introduces a protocol for measuring microcirculation in human oral mucosa by laser speckle contrast imaging. The monitoring of wound healing after vestibuloplasty combined with a xenogenic collagen graft is presented on a clinical case.

Laser speckle contrast imaging (LSCI) is a novel method for measuring superficial blood perfusion over large areas. Since it is non-invasive and avoids ...

Medicine

Quantification of Vascular Parameters in Whole Mount Retinas of Mice with Non-Proliferative and Proliferative Retinopathies

Retinopathies are a heterogeneous group of diseases that affect the neurosensory tissue of the eye. They are characterized by neurodegeneration, gliosis&#160;and a progressive change in vascular function and structure. Although the onset of the retinopathies is characterized by subtle disturbances in visual perception, the modifications in the vascular plexus are the first signs detected by clinicians. The absence or presence of neovascularization determines whether the retinopathy is classified as either non-proliferative (NPDR) or proliferative (PDR). In this sense, several animal models tried to mimic specific vascular features of each stage to determine the underlying mechanisms involved in endothelium alterations, neuronal death and other events taking place in the retina. In this article, we will provide a complete description of the procedures required for the measurement of retinal vascular parameters in adults and early birth mice at postnatal day (P)17. We will detail the protocols to carry out retinal vascular staining with Isolectin GSA-IB4 in whole mounts for later microscopic visualization. Key steps for image processing with Image J Fiji software are also provided, therefore, the readers will be able to measure vessel density, diameter and tortuosity, vascular branching, as well as avascular and neovascular areas. These tools are highly helpful to evaluate and quantify vascular alterations in both non-proliferative and proliferative retinopathies.

This article describes a well-established and reproducible lectin stain assay for the whole mount retinal preparations and the protocols required for the quantitative measurement of vascular parameters frequently altered in proliferative and non-proliferative retinopathies.

Using Digital Image Correlation to Characterize Local Strains on Vascular Tissue Specimens

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