Name: quantification of strain in a porcine model of skin expansion using multi-view stereo and isogeometric kinematics
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Description: Watch this Scientific Journal Video about Quantification of Strain in a Porcine Model of Skin Expansion Using Multi-View Stereo and Isogeometric Kinematics at JoVE.com

This work was supported by NIH grant 1R21EB021590-01A1 to Arun Gosain and Ellen Kuhl.

This protocol involves animal experiments. The protocol was approved by the IRB of Ann and Robert H. Lurie Children&#39;s Hospital of Chicago Research Center Animal Care and Use Committee to guarantee humane treatment of animals. The results for two expansion studies using this protocol have been published elsewhere 16,31.
Execution of this protocol requires a team with complementary expertise. The first part of the protocol describes the surgical procedure on the animal model, requiring personnel with the appropriate medical training. The subsequent analysis, particularly sections 4 and 5, involve basic computer programming skills in C++ and Python, and use of a command line shell.
1. Surgical Procedure for Expander Placement
NOTE: Personnel involved in the operation must be scrubbed and gowned in a sterile fashion. Sterile towels and drapes are applied around the surgical field to maintain sterility. All instruments, sutures, and tissue expanders are received in sterile packaging and handled only by sterile personnel. Sterility of the operative site must not be violated until the procedure is complete.
<ol>
	<li>Acclimate one-month-old male Yucatan mini pigs to standard housing for one week, and feed ad libitum.</li>
	<li>On the day of surgery, anesthetize the animal using ketamine/acepromazine for induction (4 - 6 mg/kg), then isoflurane for maintenance. Evaluate depth of anesthesia by monitoring the palpebral reflex. Also, monitor vital signs (heart rate, body temperature, respiratory rate, and/or response to pinch by tissue forceps). Apply ophthalmic ointment to the eyes to protect against corneal abrasions.</li>
	<li>Administer pre-procedural antibiotics and clean the dorsal skin with chlorhexidine-based surgical soap. Transfer four 10 x 10 cm2 grids, two on each side of the animal, with 1 cm line markings to the pig skin using tattoo transfer medium. The grids correspond to the following four regions: left rostral, right rostral, left caudal, and right caudal. Use a template with a midline reference to ensure symmetric placement of the grid patterns.
	<ol>
		<li>Create the grids on paper by tracing the grid outlines heavily with a ballpoint pen. Wash the area on the animal where the grid is to be placed with isopropyl rubbing alcohol.</li>
		<li>Apply the grid (pen-ink side down) directly onto the skin. The alcohol serves to leech some of the ink off of the paper, transferring the grid to the animal&#39;s skin.</li>
	</ol>
	</li>
	<li>Inject local anesthetic (1% lidocaine with 1:100,000 epinephrine) subcutaneously at the site of each planned incision.</li>
	<li>Make an incision on either side of the animal in the midpoint between the two grids. 
	NOTE: The incisions are placed on the left and right side of the animal between the 2 grids on that side. There is a left sided incision and a right sided incision</li>
	<li>Use a hemostat to develop a subcutaneous tunnel beneath the grid of interest. After developing a tunnel, insert the expander beneath the grid. 
	NOTE: Tunnels are placed under any grid that will have a tissue expander.</li>
	<li>Place the port for expander inflation remotely through a subcutaneous tunnel developed in a similar fashion along the dorsal midline of the animal. Repair wounds by suturing.</li>
	<li>Postoperatively, treat the animal with prophylactic antibiotics (Ceftiofur 5 mg/kg IM once) as well as analgesics (Buprenorphine 0.05 - 0.1 mg/kg) via intramuscular injection every 12 h for 4 doses, with additional doses available for evidence of animal distress.</li>
	<li>Observe animals continuously for 2 h postoperatively, including routine measurement of vital signs until they have resumed ambulation and are able to maintain normothermia. House the animal in a separate cage and monitor until it is able to walk independently on all 4 legs before transferring it back to its normal housing area and leaving it unattended.</li>
	<li>Following the immediate post anesthesia recovery period, check animals daily to evaluate wound healing. Remove the sutures 14 days postoperatively. These incisions do not require dressings. Leave the incisions to heal for 3 - 4 weeks before beginning expansion</li>
</ol>
2. Inflation Protocol
NOTE: The timing of the inflations and amount of solution used in each expander depends on the specific question being studied. To characterize the effect of different expander geometries, a suitable protocol is to perform five inflation steps at 0, 2, 7, 10, and 15 days to achieve filling volumes of 50, 75, 105, 165, and 225 cc respectively.
<ol>
	<li>Before each inflation step, sedate the animal administering ketamine (4 - 6 mg/kg) and dexmedetomidine at 20 - 80 &#181;g/kg. 
	NOTE: Dexmedetomidine is an alpha-adrenergic agonist that can be reversed with atipamezole (1:1 volume:volume) to facilitate faster recovery; however, this level of sedation may not be adequate for the animal to tolerate expansion without undue risk of harm to the animal or handlers. If this is the case, administer general anesthesia by delivering isoflurane via mask ventilation following ketamine/acepromazine induction.</li>
	<li>Attach two plastic flexible tape measures to the skin of the animal using surgical tape. Place the tape measures between the grids on the left and right sides.</li>
	<li>Place the animal on one side and acquire 30 photographs of the scene from as many different angles as possible. 
	NOTE: The objective is to capture the geometry of the two grids visible when the animal is laying down on one side.
	<ol>
		<li>First, position the camera above the animal and leaning towards the caudal side, to capture a shot where the tattooed grids are fully visible and fill the frame.</li>
		<li>Move in a circular pattern around the animal in an arch from the caudal to the rostral direction, taking photographs along the way, ensuring that, for every photograph, the tattooed grids that are visible appear entirely in the frame.
		<ol>
			<li>At the same time, try to maximize the space that the grids occupy in the frame. An ideal shot would capture the back of the animal with the tattooed grids and only small regions of background.</li>
		</ol>
		</li>
		<li>Next, position the camera towards the ventral side to capture a shot angle that is approximately parallel to the ground and take photographs in an arch from the ventral to the dorsal region. 
		NOTE: The amount of photographs is not a fixed value. For a good reconstruction, every point on the tattooed grid should be in at least 3 photographs; 30 photographs in total is an adequate amount for successful geometry reconstruction.</li>
	</ol>
	</li>
	<li>Place the animal on the opposite side and take 30 photographs of the two remaining grids following the same steps outlined above.</li>
	<li>Perform the inflation step by finding the remote filling port and injecting the required amount of saline solution corresponding to the expansion protocol of interest. Use sterile 0.9% injectable saline.
	<ol>
		<li>Locate the ports and prep over the skin of the animal with isopropyl alcohol wipes. Access the port with a sterile 25-gauge butterfly needle attached to a syringe filled with sterile injectable saline. 
		NOTE: As described above, the ports are tunneled subcutaneously to a position on the anterior midline dorsum during expander placement.</li>
		<li>Inject the desired amount of saline solution. Please refer to the note at the beginning of this section for the inflation volumes injected at each step of the expansion process.</li>
	</ol>
	</li>
	<li>Repeat the photo acquisition steps after inflation.</li>
	<li>Once the inflation protocol is complete, euthanize the animals.
	<ol>
		<li>Administer general anesthesia by delivering isoflurane via mask ventilation following ketamine/acepromazine induction. Evaluate depth of anesthesia by monitoring the palpebral reflex. Also, monitor vital signs (heart rate, body temperature, respiratory rate, and/or response to pinch with tissue forceps).</li>
		<li>Euthanize the animal by intravenous overdose of pentobarbital 90 - 100 mg/kg. Following pentobarbital overdose for euthanasia, confirm death by the absence of detectable heartbeat using a pulse oximeter and pulse palpation as well as the absence of spontaneous respirations.</li>
	</ol>
	</li>
</ol>
3. Multi-view Stereo Reconstruction
<ol>
	<li>Use commercially available software to upload the image files and reconstruct the geometric models.
	<ol>
		<li>Launch the MVS software on the browser and log in.</li>
		<li>Select Photo to 3D on the top left corner.</li>
		<li>Click add photos, browse to the location of the images and manually select the 30 photographs corresponding to a single model.</li>
		<li>Name the model and click create</li>
		<li>Wait for the model to be created. This can take several minutes. Click dashboard on the right to go back to the original landing page of the software. 
		NOTE: The dashboard shows representative images of the geometric models that have been created by the user.</li>
		<li>Place the cursor on the model that has just been created. Place the cursor on the bottom right corner of the model image. Click downloads and select obj.</li>
	</ol>
	</li>
</ol>
4. Spline Surface Fit
<ol>
	<li>Use open source software to process the geometric models.</li>
	<li>Click File-&#62;Import-&#62;obj to import the file generated from the MVS software. On the bottom of the 3D View click on Viewport Shading and select Texture. Look for a tab on the right of the 3D View with the submenus: Transform, Grease Pencil, View, 3D Pencil, etc. Click on Shading and select Shadeless.</li>
	<li>Right click on the geometry to select it. On the bottom of the 3D View select Edit Mode to visualize the triangular mesh.</li>
	<li>Select one by one the nodes on the 1 cm markings of the tape measure.
	<ol>
		<li>To select a point, right click on it, and highlight the point. Coordinates for the point appear on the tab on the right-hand side of the 3D View. Select and copy the coordinates of the selected point to a text file.</li>
		<li>Repeat this operation for all the points on the 1 cm markings of the tape measure.</li>
		<li>Do this for both tape measures. Examples of coordinate text files are provided: tape1.txt, tape2.txt. 
		NOTE: If there are no nodes of the mesh on the point of interest, subdivide the mesh until there is a node on the point of interest. To subdivide the mesh select the three vertices of a triangle by pressing Shift key and right-clicking on the vertices. Then click on the button Subdivide on the tab appearing on the left-hand side of the 3D View. This operation adds three more nodes inside the selected triangle.</li>
	</ol>
	</li>
	<li>Select the 11 x 11 points of the grid and save the coordinates of the 121 points to a text file in the pattern shown in Figure 1.
	<ol>
		<li>Analogously to what was done for the tape measures, to select a point of the grid, right click on it, the point will be highlighted. Coordinates for the point will appear on the tab on the right-hand side of the 3D View. Select and copy the coordinates of the selected point to a text file 
		NOTE: The numbering of the grid points is always caudal to rostral and from the dorsal midline towards the ventral region. This ordering guarantees that the parameter space is consistent for any two patches. As an example, the file gridReference.txt which contains the coordinates of 121 points of a skin patch is provided.</li>
	</ol>
	</li>
	<li>Download, compile and install C++ spline libraries. The file splineLibraryInstallation.txt contains the link to the source code of the spline libraries and instructions for installation.</li>
	<li>Compile the source code generateCurve.cpp to generate the executable generateCurve 
	NOTE: The program generateCurve only needs to be compiled once. To compile this C++ source code and generate an executable follow the instructions at the top of the source code file generateCurve.cpp.</li>
	<li>Use the program generateCurve to fit splines to the tape measures and to the grid points. To run the executable in a Bash shell, type 
	directory$ ./generateCurve
	<ol>
		<li>Upon running the program, it will ask the user to type in the path to the file containing the coordinates of the tape measure. Then the program will ask for a name for the output file. Add the termination .g2 to the filename. 
		NOTE: The termination .g2 stands for go tools, and is associated to the spline libraries. Two examples of spline files corresponding to the tape measures are available with this protocol (tape1.g2, tape2.g2).</li>
	</ol>
	</li>
	<li>Use the Python script scalePoints.py to scale the grid points. Run the program in a Bash shell prompt with three arguments: the filename of the grid points and the file names of the splines corresponding to the tape measures 
	directory$ python scalePoints.py gridReference.txt tape1.g2 tape2.g2 
	NOTE: The script scalePoints.py imports the scripts B_spline.py and NURBS_Curve.py, therefore all three scripts must be in the same folder.</li>
	<li>Compile the source code generateSurface.cpp to generate the executable generateSurface. 
	NOTE: This step only needs to be done once. More detailed instructions are available at the beginning of the source code file generateSurface.cpp.</li>
	<li>Use the program generateSurface to fit a spline surface to the grid points. Run the executable generateSurface on the Bash shell 
	directory$ ./generateSurface
	<ol>
		<li>Running the program in a shell will ask for the filename containing the scaled points. Then it will ask for the name of the output file. Add the termination .g2 to the output filename. 
		NOTE: The termination .g2 is suggested by the spline libraries and stands for go tools. The files gridReference.g2 and gridDeformed.g2 are provided as examples.</li>
	</ol>
	</li>
</ol>
5. Quantification of Expansion-induced Deformation
<ol>
	<li>Start Python in the Bash shell prompt 
	directory$ python 
	NOTE: Python initializes the interpreter, which is an interface similar to the shell that will show a new command line environment &#62;&#62;&#62;</li>
	<li>Import the script expansionIGA.py which contains a function called evaluateMembraneIGA 
	&#62;&#62;&#62; from expansionIGA import evaluateMembraneIGA</li>
	<li>Call the function evaluateMembraneIGA to calculate the deformation maps. 
	NOTE: This function takes as arguments: 
	Filename of the reference surface 
	Filename of the deformed surface 
	Resolution of the evaluation (how many points are evaluated in each direction) 
	Minimum value of area stretch used to scale the contour plot 
	Maximum value of area stretch used to scale the contour plot 
	Minimum value of stretch in longitudinal direction used to scale the contours 
	Maximum value of stretch in longitudinal direction used to scale the contours 
	Minimum value of stretch in transverse direction used to scale the contours 
	Maximum value of stretch in transverse direction used to scale the contours 
	Spacing between grid lines in the contour plot 
	Output filename
	<ol>
		<li>For example, run 
		&#62;&#62;&#62; evaluateMembraneIGA(&#39;gridReference.g2&#39;, &#39;gridDeformed.g2&#39;, 250, 3, 0.5, 2, 0.5, 2, 0.5, 25, &#39;deformation&#39;) 
		NOTE: This command will generate and save six output files. Note that the last argument in the example above is the output filename deformation, thus, the files that will be generated are: 
		deformation_theta.png: contour plot of the area stretch 
		deformation_theta.txt: table of values corresponding to the contour plot of area stretch 
		deformation_G1.png: contour plot of the stretch along the longitudinal axis of the animal 
		deformation_G1.txt: table of values corresponding to the contour plot of stretches along the longitudinal axis of the animal 
		deformation_G2.png: contour plot of the stretch component in the transverse axis of the animal 
		deformation_G2.txt: table of values corresponding to the contour plot of the component of the stretch in the transverse axis of the animal 
		NOTE: Do not confuse the termination of the spline files, .g2, with the vector G2. The spline files have ending .g2 following the naming conventions of the spline library. On the other hand, the vectors G1 and G2 denote the longitudinal and transverse directions with respect to the animal. 
		NOTE: The contour files are generated with distinct features at the four corners to facilitate interpretation of the parameter space: Black pixel: most caudal, most dorsal point; Red pixel corner: most rostral, most dorsal point; Green pixel corner: most caudal, most ventral point; Blue pixel corner: most rostral, most ventral point.</li>
	</ol>
	</li>
</ol>

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SThe authors have nothing to disclose.

Here we presented a protocol to characterize the deformations induced during a tissue expansion procedure in a porcine model using multi-view stereo (MVS) and isogeometric kinematics (IGA kinematics). During tissue expansion, skin undergoes large deformations going from a smooth and relatively flat surface to a dome-like 3D shape. Skin, like other biological membranes 34, responds to stretch by producing new material, increasing in area that can be then used for reconstructive purposes 35. Therefore, accurate determination of the stretch produced by an expander is crucial to understand the mechanisms that regulate the adaptation of skin. Planning an expansion procedure is challenging because tissue expanders come in different sizes and shapes, the stretch distribution is not uniform over the entire expanded area and it depends on the location and rate of inflation 11,36. Having a protocol to accurately estimate expansion-induced deformation and capable of resolving large strains, 3D shapes, and regional variations, opens new avenues to study the mechanical regulation of skin growth, and can eventually lead to quantitative preoperative planning tools. Towards that goal, we developed a non-invasive, affordable, and flexible methodology to measure deformation in a porcine model of skin expansion 32.
Critical Steps
Animal models for tissue expansion have been well characterized for more than two decades 37. Porcine skin shows comparable properties to human integument. Furthermore, skin expansion in pigs follows a similar procedure as it would be done in humans 38. The tissue expansion procedure is the cornerstone for the success of this protocol. Experienced surgeons, experts in tissue expansion, performed the technique in the animal model presented here.
Skin is conveniently exposed to the outside environment and it is a thin membrane, therefore its deformation can be characterized by tracking points on its surface 17. MVS offers a flexible and affordable technique to study 3D skin deformations in vivo over long periods of time. This algorithm takes as input a set of photographs from a static scene and uses feature matching across the photographs to extract 3D coordinates. MVS reconstruction and the subsequent kinematic analysis critically depend on the photo acquisition steps of this protocol.
Modifications and Troubleshooting
During tissue expansion, the device can migrate away from the grid due to animal movement and loosening of the pocket in which the expander was originally placed. If the expanded area moves outside of the grid, the expander should be deflated and removed. This problem has been encountered using the protocol in one out of eight grids 31,32. Expanders can also leak if they are defective or punctured during the inflation protocol. This also compromises the validity of the experiment and the safety of the animal, therefore the expander should be removed. This problem has been encountered using this protocol in one out of eight grids 31,32.
MVS reconstruction can be challenging for some sets of photographs due to lighting effects, lack of focus, and background noise 23. Even though the commercial tools for MVS are powerful, if the results are not accurate enough at first, the following troubleshooting steps have always corrected the problem in on our experience: manually remove the background in the photographs; select a subset of photographs with sharper focus and discard blurry images; manually select matching points across photographs in the commercial software interface.
Limitations of the Technique
As discussed above, porcine integument is similar to human 38, nevertheless, there are still differences. Therefore, a porcine model is not expected to be fully predictive of human tissue expansion protocols 37. Another limitation of the protocol is the lack of commercial tools or user-friendly software to analyze the geometric models. Currently, once the geometry is generated through MVS, the analysis is performed with in-house code which consists of C++ and Python scripts. While on the one hand, the proposed method is creative and offers an affordable, convenient way to study the mechanics of soft tissue over long periods of time, the data analysis is dependent on technologies which have only been popular for the past decade 27. To circumvent this limitation, we provide our implementation of spline subroutines with this submission. One more limitation is the restriction of a tattooed grid in order to track chronic deformations. The need for a tattooed grid hinders the translation of the protocol to clinical settings.
Significance of the Technique with Respect to Existing/Alternative Methods
Currently, physicians rely mostly on their experience during preoperative planning of tissue expansion procedures, which has led to a wide variety of arbitrary protocols that often differ greatly 13,14,15. The protocol presented here addresses existing knowledge gaps by quantifying expansion-induced deformation in a porcine animal model of tissue expansion. To the author&#39;s knowledge, this is the first protocol to quantify continuous deformation maps on sizable patches of skin tissue 31,32.
The protocol is innovative, non-invasive, affordable and flexible; it relies on recent developments in computer vision algorithms such as MVS, and numerical analysis such as IGA kinematics. MVS has advanced intensely in the past decade, reaching reconstruction errors as low as 2% 24. The rise in commercially available software as well as open source code showcases the high popularity of this method 41. MVS is affordable because it requires only a digital camera and photographs are taken without calibration of the camera position. In contrast, other techniques such as stereo reconstruction require additional hardware to control the location of the camera 17. MVS is flexible because it can be performed in a variety of scenarios as long as photographs can be taken from different angles. This is a feature that becomes more relevant when considering a potential clinical application. In contrast, other techniques such as motion tracking require a specific setup and cannot be performed in an arbitrary location 18. One more feature of MVS is the production of 3D geometries. Other techniques, such as digital image correlation (DIC), are preferred for 2D motion tracking 39. The results presented here showcased the ability of commercial algorithms to faithfully reconstruct the 3D shapes induced during tissue expansion.
From the 3D geometries, deformations have to be calculated. This protocol relies on the use of spline surface IGA kinematics. Splines are useful because a few control points parameterize smooth geometries with high continuity which are needed for analysis of thin membranes 40. The greatest advantage of splines in this application is the notion of a parametric space. Other techniques, such as finite elements, lack a global parameter domain. While this is convenient for certain problems such as simulation of irregular patches (for example patches with holes), having an explicit parameterization allows determination of stretches between any two configurations in a straightforward manner. For instance, two different analyses were shown here: chronic and acute deformations. To calculate the strains in the grids with this protocol it is enough to provide the splines of the two surfaces of interest since all the surfaces have the same parameter domain.
During tissue expansion, skin responds to the applied deformation by growing in surface area, producing new integument that can then be employed for reconstructive surgery. Characterizing clinically relevant deformations of skin over long periods of time can improve our understanding of the mechanobiology of this organ as well as enable the development of quantitative preoperative tools. The protocol described here specifically addresses the need of an experimental design with potential translation to the clinical setting.
Future Applications or Directions after Mastering this Technique
The source code that is used in this protocol could easily be adjusted to other applications and could be incorporated into more user-friendly implementations. Provided with this paper are routines to evaluate spline basis functions, parameterize continuous fields over spline surfaces, integrate those continuous fields, and calculate deformation gradients, membrane and bending strains. We expect that this source code will continue to evolve towards a tool that can be eventually used in real clinical applications of tissue expansion as well as enable other applications. Another future area of work is the refinement of this protocol to take into consideration mechanical properties and stresses in the tissue and not only kinematics.
From a clinically relevant perspective, this protocol is able to quantify regional variations of tissue deformation, as well as differences between different expander shapes and inflation rates 31,32. Further work is needed to continue to evaluate the effect of different expansion parameters on the tissue response. Moreover, further refinement of the porcine model with emphasis on the biological mechanisms of adaptation can help elucidate fundamental mechanisms regulating skin adaptation to overstretch. The ultimate goal is to validate the protocol in a porcine model in order to translate it to the clinical setting.

Tissue expansion is a common technique in plastic and reconstructive surgery that grows skin in vivo for the correction of large cutaneous defects 1. Neumann, in 1957, was the first surgeon to document this procedure. He implanted a balloon below the skin of a patient and inflated it gradually over a period of several weeks to grow new tissue and resurface an ear 2. Skin, like most biological tissues, adapts to applied forces and deformations in order to reach mechanical homeostasis. When stretched beyond the physiological regime, skin grows 3,4. One of the central advantages of tissue expansion is the production of skin with proper vascularization and the same hair bearing, mechanical properties, color, and texture as the surrounding tissue 5.
After its introduction six decades ago, skin expansion has been widely adopted by plastic and reconstructive surgeons and is presently used to correct burns, large congenital defects, and for breast reconstruction after mastectomy 6,7. Yet, despite its widespread use, skin expansion procedures can lead to complications 8. This is partly due to the lack of sufficient quantitative evidence needed to understand the fundamental mechanobiology of the procedure and to guide the surgeon during preoperative planning 9,10. Key parameters in this technique are the filling rate, filling volume per inflation, the selection of the shape and size of the expander, and the placement of the device 11,12. Current preoperative planning relies largely on the physician&#39;s experience, resulting in a wide variety of arbitrary protocols that often differ greatly 13,14,15.
To address the current knowledge gaps, we present an experimental protocol to quantify expansion-induced deformation in a porcine animal model of tissue expansion. The protocol relies on the use of multi-view stereo (MVS) to reconstruct three-dimensional (3D) geometries out of sequences of two-dimensional (2D) images with unknown camera positions. Employing splines, representation of smooth surfaces leads to the calculation of the corresponding deformation maps by means of an isogeometric (IGA) description. The analysis of the geometry is based on the theoretical framework of continuum mechanics of membranes having an explicit parameterization 16.
Characterizing physiologically relevant deformations of living materials over long periods of time still remains a challenging problem. Common strategies for imaging of biological tissues include stereoscopic digital image correlation, commercial motion capture systems with reflective markers, and biplane video fluoroscopy 17,18,19. However, these techniques require a restrictive experimental setup, are generally expensive, and have been primarily used for ex vivo or acute in vivo settings. Skin has the advantage of being a thin structure. Even though it consists of several layers, the dermis is largely responsible for the mechanical properties of the tissue and thus the surface deformation is of primary importance 20; reasonable kinematical assumptions can be made regarding the out of plane deformation 21,22. Moreover, skin is already exposed to the outside environment, making it possible to use conventional imaging tools to capture its geometry. Here we propose the use of MVS as an affordable and flexible approach to monitor in vivo deformations of skin over several weeks without interfering majorly with a tissue expansion protocol. MVS is a technique that extracts 3D representations of objects or scenes from a collection of 2D images with unknown camera angles 23. Only in the last three years, several commercial codes have appeared (see list of materials for examples). The high accuracy of the model reconstruction with MVS, with errors as low as 2% 24, makes this approach suitable for the kinematic characterization of skin in vivo over long periods of time.
To obtain the corresponding deformation maps of skin during tissue expansion, points between any two geometric configurations are matched. Conventionally, researchers in computational biomechanics have used finite element meshes and inverse analysis to retrieve the deformation map 25,26. The IGA approach employed here uses spline basis functions which offer several advantages for the analysis of thin membranes 27,28. Namely, the availability of high degree polynomials facilitates representations of smooth geometries even with very coarse meshes 29,30. Additionally, it is possible to fit the same underlying parameterization to all the surface patches, which circumvents the need for an inverse problem to account for non-matching discretizations.
The method described here opens new avenues to study skin mechanics in relevant in vivo settings over long periods of time. In addition, we are hopeful that our methodology is an enabling step towards the ultimate goal of developing computational tools for personalized treatment planning in the clinical setting.

<table><tbody><tr><td>Yucatan miniature swine</td><td>Sinclair Bioresources, Windham, ME</td><td>N/A</td><td></td></tr><tr><td>Antibiotics</td><td>Santa Cruz Animal Health, Paso Robles, CA</td><td>sc-362931Rx</td><td>Ceftiofur, dosage 5 mg/kg intramuscular</td></tr><tr><td>Chlorhexidine-based surgical soap</td><td>Cardinal Health, Dublin, OH</td><td>AS-4CHGL(4-32)</td><td>4% chlorhexidine gluconate surgical hand scrub</td></tr><tr><td>Tattoo transfer medium&amp;nbsp;</td><td>Hildbrandt Tattoo Supply, Point Roberts, WA</td><td>TRANSF</td><td>Stencil thermal tattoo transfer paper</td></tr><tr><td>Lidocaine with epinephrine</td><td>ACE Surgical Supply Co, Brockton, MA</td><td>001-1423</td><td>Lidocaine Hcl 1% (Xylocaine) - Epinephrine 1:100,000, 20 mL</td></tr><tr><td>Buprenorphine</td><td>ZooPharm, Windsor, CO</td><td></td><td>1 mg/mL sustained release, dosage 0.01 mg/kg intramuscular</td></tr><tr><td>Digital camera</td><td>Sony</td><td>Alpha33</td><td>Standard digital camera with 18 - 35&amp;nbsp;mm lens, 3.5 - 5.6 aperture. Used in automatic mode, no flash</td></tr><tr><td>Tape measure</td><td>Medline, Mundelein, Illinois</td><td>NON171330</td><td>Retractable tape measure, cloth, plastic case, 72 inches</td></tr><tr><td>Tissue expanders</td><td>PMT, Chanhassen, MN</td><td>03610-06-02</td><td>4 cm x 6 cm, rectangular, 120 cc, 3610 series 2 stage tissue expander with standard port</td></tr><tr><td>ReCap360</td><td>Autodesk</td><td>N/A</td><td>MVS Software, Web application: recap360.autodesk.com</td></tr><tr><td>Blender</td><td>Blender Foundation</td><td>N/A</td><td>Computer Graphics Software, open source: blender.org</td></tr><tr><td>SISL</td><td>SINTEF</td><td>N/A</td><td>C++ spline libraries, open source: https://www.sintef.no/projectweb/geometry-toolkits/sisl/</td></tr></tbody></table>

quantification of strain in a porcine model of skin expansion using multi-view stereo and isogeometric kinematics

Tissue expansion is a popular technique in plastic and reconstructive surgery that grows skin in vivo for correction of large defects such as burns and giant congenital nevi. Despite its widespread use, planning and executing an expansion protocol is challenging due to the difficulty in measuring the deformation imposed at each inflation step and over the length of the procedure. Quantifying the deformation fields is crucial, as the distribution of stretch over time determines the rate and amount of skin grown at the end of the treatment. In this manuscript, we present a method to study tissue expansion in order to gain quantitative knowledge of the deformations induced during an expansion process. This experimental protocol incorporates multi-view stereo and isogeometric kinematic analysis in a porcine model of tissue expansion. Multi-view stereo allows three-dimensional geometric reconstruction from uncalibrated sequences of images. The isogeometric kinematic analysis uses splines to describe the regional deformations between smooth surfaces with few mesh points. Our protocol has the potential to bridge the gap between basic scientific inquiry regarding the mechanics of skin expansion and the clinical setting. Eventually, we expect that the knowledge gained with our methodology will enable treatment planning using computational simulations of skin deformation in a personalized manner.

This methodology has been successfully employed to study the deformation induced by different expander geometries: rectangle, sphere and crescent expanders 31,32. The results corresponding to the sphere and crescent expanders are discussed next. Figure 2 illustrates the three steps of MVS model reconstruction. The starting point is a collection of photographs from a static scene. The animal with the tattooed grids and the tape measures was lying still as the photographs were taken from different angles. The MVS algorithm matched features between the photographs to extract 3D coordinates. As a result, a geometric model consisting of a triangular mesh with texture was generated.
The protocol described here can be used to investigate different aspects of the tissue expansion process. The variations in regional strains induced by sphere and crescent expanders is one important aspect of the expansion process since it leads to regional variations in the amount of skin grown. Both devices were filled to the same volume at every time point. Five inflation steps were performed at 0, 2, 7, 10, and 15 days to generate filling volumes of 50, 75, 105, 165, and 225 cc. Figure 3 shows photographs of the expanded skin grids at the end of each inflation step. The expanders stretched the skin and the deformation was apparent by the distortion of the grid over time.
For every configuration of the grid a spline surface was generated as described in the Protocol section. Deformations were calculated by choosing a reference and a deformed grid as illustrated in Figure 1. The results of two different types of analyses are discussed here. To study the chronic deformation, the pig at day 0 was selected as the reference configuration and compared to all other time points. Comparing the end of every inflation step to the reference configuration results in the contour plots shown in Figure 4. The methodology presented here extracts three measures of deformation. The area change is denoted &#952;, the stretch in the longitudinal direction is termed &#955;G1, and &#955;G2 is the stretch in the transverse direction, as shown in Figure 1. The progression of the area changes and stretches in the two orthogonal directions for sphere and crescent expanders are depicted in Figure 4. Spline surfaces are generally smooth and therefore the corresponding contour plots were smooth. Nonetheless, the coarseness of the mesh was evidenced by the contours which showed spot features. A finer grid would increase the fidelity of the deformation maps. Nevertheless, the differences between different expander geometries was immediately apparent and quantifiable. Even though both expanders were filled to the same volume, the spherical expander induced a larger deformation. The spatial variation of the contour plots revealed that skin was stretched more at the center of the expander compared to the periphery of the grid. Results are summarized in Table 1.
A second analysis consisted of determining the acute deformation at every inflation step. In this case, the reference configuration was the grid just prior to expansion, and the deformed grid was that immediately after the inflation step. The deformations induced at every inflation step were remarkably similar on average between the different time points. The summary is contained in Table 2. On average, the deformation was close to 1 (where 1 would be the absence of deformation). Inspection of the contour maps shown in Figure 5 showcased evident spatial variations. Even though there was almost no deformation on average, some zones of the grid were stretched while others were shrunk with respect to the reference. Similar to the analysis of the chronic deformation, the center regions were the ones being stretched the most.
In both the acute and chronic cases, longitudinal and transverse stretches showed a clear trend indicative of anisotropy. Skin, as most collagenous tissues, shows a preferred fiber orientation contributing to an anisotropic mechanical response 25. In the case of skin in the back of a pig, fibers are thought to be aligned transversely 33. Our experiments showed that during skin expansion, the stretches in the longitudinal directions were always greater than those along the transverse direction. This was true for both the sphere and the crescent expanders, at all time points, and for the acute and chronic deformation contours. This result supports the hypothesis that skin anisotropy can impact the deformations induced during a tissue expansion procedure.
<img alt="Figure 1" src="/files/ftp_upload/55052/55052fig1.jpg" /> 
Figure 1: Grid Configurations and Parameter Space. Grids are tattooed on the back of the animals and photographed with tape measures in place in order to scale the geometric models (top). Deformation between a reference and a deformed configuration is characterized by three variables: area change &#952;, longitudinal stretch &#955;G1, and transverse stretch &#955;G2 (top). The grid is consistently parameterized by numbering the points always from caudal to rostral and from dorsal to ventral directions (bottom left). The output of the analysis is a contour plot over the parameter space. The contours are marked at the corners with one pixel which takes the color black, red, green, and blue, to facilitate identification of caudal, rostral, dorsal, and ventral sides (bottom right). <a href="https://cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/55052/55052fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" src="/files/ftp_upload/55052/55052fig2.jpg" /> 
Figure 2: Multi-view Stereo Reconstruction of an Expansion Process. MVS is an algorithm from computer vision that takes as input photographs from different angles with unknown camera positions (left). The algorithm matches features across the images to find 3D coordinates (center). The output of the algorithm is a triangular mesh with the texture overlaid (right). (Figure adapted with permission from 31) <a href="https://cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/55052/55052fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" src="/files/ftp_upload/55052/55052fig3.jpg" /> 
Figure 3: Expansion of Sphere and Crescent Expanders. Sphere (top row) and crescent (bottom row) expanders were placed below the tattooed skin on the back of a pig and inflated at days 0, 2, 7, 10, and 15 days to generate filling volumes of 50, 75, 105, 165, and 225 cc. (Figure adapted with permission from 31). <a href="https://cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/55052/55052fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" src="/files/ftp_upload/55052/55052fig4.jpg" /> 
Figure 4: Chronic Deformation Induced by Sphere and Crescent Expanders. The tattooed grids were converted to spline surfaces for analysis (rows 1 and 2). Taking the reference to be the grid at day 0, three measures of deformation were calculated. Area change showed progressively higher values over time, with higher deformation in the center region of the expander, and higher deformation in the sphere compared to the crescent (rows 3 and 4). Longitudinal stretches (rows 5 and 6) resembled area stretches while transverse stretches (rows 7 and 8) showed bands of deformation and less stretch compared to the longitudinal direction. (Figure adapted with permission from 31) <a href="https://cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/55052/55052fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" src="/files/ftp_upload/55052/55052fig5.jpg" /> 
Figure 5: Acute Deformation Induced by Sphere and Crescent Expanders. Taking as reference the configuration just prior to an inflation step, and as deformed the configuration immediately after injection of solution into an expander, acute deformations were calculated. Deformation maps were smooth, however, some edge effects were noticeable and the coarseness of the discretization was reflected in spot-like patterns of deformation. Area changes (rows 1 and 2) showed regional variation, with higher stretch in the region corresponding to the expander. Stretches were similar across the different time points. The same trend could be seen for longitudinal stretches (rows 3 and 4). Transverse stretches (rows 5 and 6) showed more uniform distributions and lower values compared to the longitudinal case. (Figure adapted with permission from 31) <a href="https://cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/55052/55052fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table fo:keep-together.within-page="1">
	<tbody>
		<tr>
			<td rowspan="2">Time [days]</td>
			<td rowspan="2">Expander</td>
			<td rowspan="2">Volume [cc]</td>
			<td colspan="3">Area change &#952;</td>
			<td colspan="3">Longitudinal stretch &#955;G1</td>
			<td colspan="3">Transverse stretch &#955;G2</td>
		</tr>
		<tr>
			<td>max</td>
			<td>min</td>
			<td>avg</td>
			<td>max</td>
			<td>min</td>
			<td>avg</td>
			<td>max</td>
			<td>min</td>
			<td>avg</td>
		</tr>
		<tr>
			<td>0</td>
			<td>sphere</td>
			<td>50</td>
			<td>1.44</td>
			<td>0.71</td>
			<td>0.98</td>
			<td>1.37</td>
			<td>0.76</td>
			<td>1</td>
			<td>1.17</td>
			<td>0.84</td>
			<td>0.97</td>
		</tr>
		<tr>
			<td>0</td>
			<td>crescent</td>
			<td>50</td>
			<td>1.46</td>
			<td>0.76</td>
			<td>0.98</td>
			<td>1.24</td>
			<td>0.79</td>
			<td>1</td>
			<td>1.17</td>
			<td>0.84</td>
			<td>0.98</td>
		</tr>
		<tr>
			<td>2</td>
			<td>sphere</td>
			<td>75</td>
			<td>1.74</td>
			<td>0.68</td>
			<td>1.08</td>
			<td>1.51</td>
			<td>0.73</td>
			<td>1.08</td>
			<td>1.19</td>
			<td>0.75</td>
			<td>1</td>
		</tr>
		<tr>
			<td>2</td>
			<td>crescent</td>
			<td>75</td>
			<td>1.43</td>
			<td>0.66</td>
			<td>1</td>
			<td>1.31</td>
			<td>0.65</td>
			<td>1</td>
			<td>1.26</td>
			<td>0.77</td>
			<td>1</td>
		</tr>
		<tr>
			<td>7</td>
			<td>sphere</td>
			<td>105</td>
			<td>0.01</td>
			<td>0.69</td>
			<td>1.21</td>
			<td>1.7</td>
			<td>0.75</td>
			<td>1.13</td>
			<td>1.32</td>
			<td>0.84</td>
			<td>1.07</td>
		</tr>
		<tr>
			<td>7</td>
			<td>crescent</td>
			<td>105</td>
			<td>1.66</td>
			<td>0.83</td>
			<td>1.15</td>
			<td>1.4</td>
			<td>0.87</td>
			<td>1.11</td>
			<td>1.33</td>
			<td>0.86</td>
			<td>1.03</td>
		</tr>
		<tr>
			<td>10</td>
			<td>sphere</td>
			<td>165</td>
			<td>2.26</td>
			<td>0.74</td>
			<td>1.36</td>
			<td>1.76</td>
			<td>0.77</td>
			<td>1.21</td>
			<td>1.39</td>
			<td>0.83</td>
			<td>1.11</td>
		</tr>
		<tr>
			<td>10</td>
			<td>crescent</td>
			<td>165</td>
			<td>1.86</td>
			<td>0.87</td>
			<td>1.26</td>
			<td>1.58</td>
			<td>0.8</td>
			<td>1.15</td>
			<td>1.45</td>
			<td>0.83</td>
			<td>1.09</td>
		</tr>
		<tr>
			<td>15</td>
			<td>sphere</td>
			<td>225</td>
			<td>2.77</td>
			<td>0.72</td>
			<td>1.52</td>
			<td>2.01</td>
			<td>0.69</td>
			<td>1.29</td>
			<td>1.47</td>
			<td>0.89</td>
			<td>1.18</td>
		</tr>
		<tr>
			<td>15</td>
			<td>crescent</td>
			<td>225</td>
			<td>1.87</td>
			<td>0.83</td>
			<td>1.32</td>
			<td>1.46</td>
			<td>0.84</td>
			<td>1.17</td>
			<td>1.44</td>
			<td>0.92</td>
			<td>1.14</td>
		</tr>
		<tr>
			<td>21</td>
			<td>sphere</td>
			<td>225</td>
			<td>3.09</td>
			<td>0.93</td>
			<td>1.7</td>
			<td>2.13</td>
			<td>0.9</td>
			<td>1.33</td>
			<td>1.62</td>
			<td>0.98</td>
			<td>1.27</td>
		</tr>
		<tr>
			<td>21</td>
			<td>crescent</td>
			<td>225</td>
			<td>2.25</td>
			<td>0.87</td>
			<td>1.49</td>
			<td>1.66</td>
			<td>0.85</td>
			<td>1.25</td>
			<td>1.67</td>
			<td>0.96</td>
			<td>1.2</td>
		</tr>
	</tbody>
</table>
Table 1: Summary of Chronic Deformation. Strains were calculated taking the initial configuration as the reference and comparing the patches at the end of each inflation step with respect to it. The average of the deformation attributed to the sphere expander reached 1.70 at day 21 while the crescent expander deformed 1.49 in area by the end of expansion. There was significant spatial variation and maximum and minimum values varied with respect to the average. The longitudinal stretches reached 1.33 and 1.25 for the sphere and crescent expanders respectively, while transverse stretches were lower, with values of 1.27 and 1.20. (Table adapted with permission from 31)
<table fo:keep-together.within-page="1">
	<tbody>
		<tr>
			<td rowspan="2">Time [days]</td>
			<td rowspan="2">Expander</td>
			<td rowspan="2">Volume [cc]</td>
			<td colspan="3">Area change &#952;</td>
			<td colspan="3">Longitudinal stretch &#955;G1</td>
			<td colspan="3">Transverse stretch &#955;G2</td>
		</tr>
		<tr>
			<td>max</td>
			<td>min</td>
			<td>avg</td>
			<td>max</td>
			<td>min</td>
			<td>avg</td>
			<td>max</td>
			<td>min</td>
			<td>avg</td>
		</tr>
		<tr>
			<td>0</td>
			<td>sphere</td>
			<td>50</td>
			<td>1.32</td>
			<td>0.72</td>
			<td>0.98</td>
			<td>1.44</td>
			<td>0.75</td>
			<td>1</td>
			<td>1.23</td>
			<td>0.83</td>
			<td>0.97</td>
		</tr>
		<tr>
			<td>0</td>
			<td>crescent</td>
			<td>50</td>
			<td>1.5</td>
			<td>0.71</td>
			<td>0.98</td>
			<td>1.3</td>
			<td>0.8</td>
			<td>1</td>
			<td>1.21</td>
			<td>0.84</td>
			<td>0.98</td>
		</tr>
		<tr>
			<td>2</td>
			<td>sphere</td>
			<td>75</td>
			<td>1.36</td>
			<td>0.69</td>
			<td>0.98</td>
			<td>1.26</td>
			<td>0.66</td>
			<td>1</td>
			<td>1.2</td>
			<td>0.8</td>
			<td>0.98</td>
		</tr>
		<tr>
			<td>2</td>
			<td>crescent</td>
			<td>75</td>
			<td>1.31</td>
			<td>0.61</td>
			<td>0.98</td>
			<td>1.24</td>
			<td>0.8</td>
			<td>1.01</td>
			<td>1.34</td>
			<td>0.68</td>
			<td>0.97</td>
		</tr>
		<tr>
			<td>7</td>
			<td>sphere</td>
			<td>105</td>
			<td>1.4</td>
			<td>0.79</td>
			<td>0.98</td>
			<td>1.3</td>
			<td>0.57</td>
			<td>1</td>
			<td>1.2</td>
			<td>0.77</td>
			<td>0.98</td>
		</tr>
		<tr>
			<td>7</td>
			<td>crescent</td>
			<td>105</td>
			<td>1.37</td>
			<td>0.59</td>
			<td>1</td>
			<td>1.6</td>
			<td>0.83</td>
			<td>1.02</td>
			<td>1.16</td>
			<td>0.77</td>
			<td>0.98</td>
		</tr>
		<tr>
			<td>10</td>
			<td>sphere</td>
			<td>165</td>
			<td>1.6</td>
			<td>0.73</td>
			<td>1.01</td>
			<td>1.35</td>
			<td>0.6</td>
			<td>1.02</td>
			<td>1.25</td>
			<td>0.75</td>
			<td>0.99</td>
		</tr>
		<tr>
			<td>10</td>
			<td>crescent</td>
			<td>165</td>
			<td>1.48</td>
			<td>0.58</td>
			<td>1.01</td>
			<td>1.42</td>
			<td>0.75</td>
			<td>1.02</td>
			<td>1.22</td>
			<td>0.77</td>
			<td>1</td>
		</tr>
		<tr>
			<td>15</td>
			<td>sphere</td>
			<td>225</td>
			<td>1.27</td>
			<td>0.73</td>
			<td>1.01</td>
			<td>1.35</td>
			<td>0.55</td>
			<td>1.02</td>
			<td>1.22</td>
			<td>0.79</td>
			<td>0.98</td>
		</tr>
		<tr>
			<td>15</td>
			<td>crescent</td>
			<td>225</td>
			<td>1.34</td>
			<td>0.54</td>
			<td>1.02</td>
			<td>1.37</td>
			<td>0.8</td>
			<td>1.02</td>
			<td>1.32</td>
			<td>0.81</td>
			<td>1</td>
		</tr>
	</tbody>
</table>
Table 2: Summary of Acute Deformation. Strains were calculated taking the configuration prior to expansion as the reference and the configuration immediately after the inflation step as the deformed grid. On average, both the sphere and crescent expanders showed similar trends, with values close to 1 which would indicate no deformation. However, due to spatial variations, we measured maximum area changes were as high as 1.60 for the sphere and 1.50 for the crescent. The stretches in the longitudinal and transverse directions were anisotropic, with the maximum values of longitudinal stretches almost always higher than the transverse stretches. (Table adapted with permission from 31)

Watch this Scientific Journal Video about Quantification of Strain in a Porcine Model of Skin Expansion Using Multi-View Stereo and Isogeometric Kinematics at JoVE.com

Quantification of Strain in a Porcine Model of Skin Expansion Using Multi-View Stereo and Isogeometric Kinematics

This protocol uses multi-view stereo to generate three-dimensional (3D) models out of uncalibrated sequences of photographs, making it affordable and adjustable to a surgical setting. Strain maps between the 3D models are quantified with spline-based isogeometric kinematics, which facilitate representation of smooth surfaces over coarse meshes sharing the same parameterization.

Tissue expansion is a popular technique in plastic and reconstructive surgery that grows skin in vivo for correction of large defects such as burns and ...

quantification-strain-porcine-model-skin-expansion-using-multi-view

Nanyang Technological University

Research

JoVE Journal

Bioengineering

11.5K Views.  Purdue University. This protocol uses multi-view stereo to generate three-dimensional (3D) models out of uncalibrated sequences of photographs, making it affordable and adjustable to a surgical setting. Strain maps between the 3D models are quantified with spline-based isogeometric kinematics, which facilitate representation of smooth surfaces over coarse meshes sharing the same parameterization.

Video: Quantification of Strain in a Porcine Model of Skin Expansion Using Multi-View Stereo and Isogeometric Kinematics

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 ...

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 ...

Engineering

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 ...

Measuring 3D In-vivo Shoulder Kinematics using Biplanar Videoradiography

The shoulder is one of the human body's most complex joint systems, with motion occurring through the coordinated actions of four individual joints, multiple ligaments, and approximately 20 muscles. Unfortunately, shoulder pathologies (e.g., rotator cuff tears, joint dislocations, arthritis) are common, resulting in substantial pain, disability, and decreased quality of life. The specific etiology for many of these pathologic conditions is not fully understood, but it is generally accepted that shoulder pathology is often associated with altered joint motion. Unfortunately, measuring shoulder motion with the necessary level of accuracy to investigate motion-based hypotheses is not trivial. However, radiographic-based motion measurement techniques have provided the advancement necessary to investigate motion-based hypotheses and provide a mechanistic understanding of shoulder function. Thus, the purpose of this article is to describe the approaches for measuring shoulder motion using a custom biplanar videoradiography system. The specific objectives of this article are to describe the protocols to acquire biplanar videoradiographic images of the shoulder complex, acquire CT scans, develop 3D bone models, locate anatomical landmarks, track the position and orientation of the humerus, scapula, and torso from the biplanar radiographic images, and calculate the kinematic outcome measures. In addition, the article will describe special considerations unique to the shoulder when measuring joint kinematics using this approach.

Biplane videoradiography can quantify shoulder kinematics with a high degree of accuracy. The protocol described herein was specifically designed to track the scapula, humerus, and the ribs during planar humeral elevation, and outlines the procedures for data collection, processing, and analysis. Unique considerations for data collection are also described.

The shoulder is one of the human body's most complex joint systems, with motion occurring through the coordinated actions of four individual joints, ...

Biomechanical Analysis of Peripheral Nerves Using DLT-Calibrated Stereo-Imaging

Direct Linear Transformation for the Measurement of In-Situ Peripheral Nerve Strain During Stretching

Peripheral nerves undergo physiological and non-physiological stretch during development, normal joint movement, injury, and more recently while undergoing surgical repair. Understanding the biomechanical response of peripheral nerves to stretch is critical to the understanding of their response to different loading conditions and thus, to optimizing treatment strategies and surgical interventions. This protocol describes in detail the calibration process of the stereo-imaging camera system via direct linear transformation and the tracking of the three-dimensional in-situ tissue displacement of peripheral nerves during stretch, obtained from three-dimensional coordinates of the video files captured by the calibrated stereo-imaging camera system.
From the obtained three-dimensional coordinates, the nerve length, change in the nerve length, and percent strain with respect to time can be calculated for a stretched peripheral nerve. Using a stereo-imaging camera system provides a non-invasive method for capturing three-dimensional displacements of peripheral nerves when stretched. Direct linear transformation enables three-dimensional reconstructions of peripheral nerve length during stretch to measure strain. Currently, no methodology exists to study the in-situ strain of stretched peripheral nerves using a stereo-imaging camera system calibrated via direct linear transformation. Capturing the in-situ strain of peripheral nerves when stretched can not only aid clinicians in understanding underlying injury mechanisms of nerve damage when overstretched but also help optimize treatment strategies that rely on stretch-induced interventions. The methodology described in the paper has the potential to enhance our understanding of peripheral nerve biomechanics in response to stretch to improve patient outcomes in the field of nerve injury management and rehabilitation.

Stereo-Imaging System DLT Calibration to Capture 3D In Situ Displacements of Stretched Peripheral Nerves

This protocol implements a stereo-imaging camera system calibrated using direct linear transformation to capture three-dimensional in-situ displacements of stretched peripheral nerves. By capturing these displacements, strain induced at varying degrees of stretch can be determined informing the stretch injury thresholds that can advance the science of stretch-dependent nerve repair.

Peripheral nerves undergo physiological and non-physiological stretch during development, normal joint movement, injury, and more recently while ...

In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy

Several hip pathologies have been attributed to abnormal morphology with an underlying assumption of aberrant biomechanics. However, structure-function relationships at the joint level remain challenging to quantify due to difficulties in accurately measuring dynamic joint motion. The soft tissue artifact errors inherent in optical skin marker motion capture are exacerbated by the depth of the hip joint within the body and the large mass of soft tissue surrounding the joint. Thus, the complex relationship between bone shape and hip joint kinematics is more difficult to study accurately than in other joints. Herein, a protocol incorporating computed tomography (CT) arthrography, three-dimensional (3D) reconstruction of volumetric images, dual fluoroscopy, and optical motion capture to accurately measure the dynamic motion of the hip joint is presented. The technical and clinical studies that have applied dual fluoroscopy to study form-function relationships of the hip using this protocol are summarized, and the specific steps and future considerations for data acquisition, processing, and analysis are described.

In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy

Dual fluoroscopy accurately captures in vivo dynamic motion of human joints, which can be visualized relative to reconstructed anatomy (e.g., arthrokinematics). Herein, a detailed protocol to quantify hip arthrokinematics during weight-bearing activities of daily living is presented, including the integration of dual fluoroscopy with traditional skin marker motion capture.

Several hip pathologies have been attributed to abnormal morphology with an underlying assumption of aberrant biomechanics. However, structure-function ...

Medicine

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.

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 ...

Biology

Ascending Aortic Banding for Studying Pressure-Overload Induced Heart Failure

A Minimally Invasive Model of Aortic Stenosis in Swine

Large animal models of heart failure play an essential role in the development of new therapeutic interventions due to their size and physiological similarities to humans. Efforts have been dedicated to creating a model of pressure-overload induced heart failure, and ascending aortic banding while still supra-coronary and not a perfect mimic of aortic stenosis in humans, closely resembling the human condition.
The purpose of this study is to demonstrate a minimally invasive approach to induce left ventricular pressure overload by placing an aortic band, precisely calibrated with percutaneously introduced high-fidelity pressure sensors. This method represents a refinement of the surgical procedure (3Rs), resulting in homogenous trans-stenotic gradients and reduced intragroup variability. Additionally, it enables swift and uneventful animal recovery, leading to minimal mortality rates. Throughout the study, animals were followed for up to 2 months after surgery, employing transthoracic echocardiography and pressure-volume loop analysis. However, longer follow-up periods can be achieved if desired. This large animal model proves valuable for testing new drugs, particularly those targeting hypertrophy and the structural and functional alterations associated with left ventricular pressure overload.

Author Spotlight: Development of a Minimally Invasive Large-Animal Model for Reliable and Reproducible Cardiovascular Research 

This protocol describes a minimally invasive surgical procedure for ascending aortic banding in swine.

Large animal models of heart failure play an essential role in the development of new therapeutic interventions due to their size and physiological ...

3D Visualization of Bone Remodeling Under Tensile Load in Suture Expansion Mice

A 3-D Visualization Technique for Bone Remodeling in a Suture Expansion Mouse Model

Craniofacial sutures play a crucial role beyond being fibrous joints connecting craniofacial bones; they also serve as the primary niche for calvarial and facial bone growth, housing mesenchymal stem cells and osteoprogenitors. As most craniofacial bones develop through intramembranous ossification, the sutures' marginal regions act as initiation points. Due to this significance, these sutures have become intriguing targets in orthopedic therapies like spring-assisted cranial vault expansion, rapid maxillary expansion, and maxillary protraction. Under orthopedic tracing force, suture stem cells are rapidly activated, becoming a dynamic source for bone remodeling during expansion. Despite their importance, the physiological changes during bone remodeling periods remain poorly understood. Traditional sectioning methods, primarily in the sagittal direction, do not capture the comprehensive changes occurring throughout the entire suture. This study established a standard mouse model for sagittal suture expansion. To fully visualize bone remodeling changes post-suture expansion, the PEGASOS tissue clearing method was combined with whole-mount EdU staining and calcium chelating double labeling. This allowed the visualization of highly proliferating cells and new bone formation across the entire calvarial bones following expansion. This protocol offers a standardized suture expansion mouse model and a 3-D visualization method, shedding light on the mechanobiological changes in sutures and bone remodeling under tensile force loading.

Author Spotlight: PEGASOS Tissue Clearing Technique to Visualize Bone Remodeling 

This protocol presents a standardized suture expansion mouse model and a 3-D visualization method to study the mechanobiological changes of the suture and bone remodeling under tensile force loading.

Craniofacial sutures play a crucial role beyond being fibrous joints connecting craniofacial bones; they also serve as the primary niche for calvarial and ...

Quantitative Strain Mapping of an Abdominal Aortic Aneurysm

Source: Hannah L. Cebull1, Arvin H. Soepriatna1, John J. Boyle2 and <a href="https://www-jove-com.bdigitaluss.remotexs.co/author/Craig+J._Goergen">Craig J. Goergen</a>1 
1Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana
2Mechanical Engineering &#38; Materials Science, Washington University in St. Louis, St Louis, Missouri
The mechanical behavior of soft tissues, such as blood vessels, skin, tendons, and other organs, are strongly influenced by their composition of elastin and collagen, which provide elasticity and strength. The fiber orientation of these proteins depends on the type of soft tissue and can range from a single preferred direction to intricate meshed networks, which can become altered in diseased tissue. Therefore, soft tissues often behave anisotropically on the cellular and organ level, creating a need for three-dimensional characterization. Developing a method for reliably estimating strain fields within complex biological tissues or structures is important to mechanically characterize and understande disease. Strain represents how soft tissue relatively deforms over time, and it can be described mathematically through various estimations.
Acquiring image data over time allows deformation and strain to be estimated. However, all medical imaging modalities contain some amount of noise, which increases the difficulty of accurately estimating in vivo strain. The technique described here successfully overcomes these issues by using a direct deformation estimation (DDE) method to calculate spatially varying 3D strain fields from volumetric image data.
Current strain estimation methods include digital image correlation (DIC) and digital volume correlation. Unfortunately, DIC can only accurately estimate strain from a 2D plane, severely limiting the application of this method. While useful, 2D methods such as DIC have difficulty quantifying strain in regions that undergo 3D deformation. This is because out-of-plane motion creates deformation errors. Digital volume correlation is a more applicable method that divides the initial volume data into regions and finds the most similar region of the deformed volume, thereby reducing out-of-plane error. However, this method proves to be sensitive to noise and requires assumptions about the mechanical properties of the material.
The technique demonstrated here eliminates these issues by using a DDE method, thus making it very useful in the analysis of medical imaging data. Furthermore, it is robust to high or localized strain. Here we describe the acquisition of gated, volumetric 4D ultrasound data, its conversion into an analyzable format, and the use of a custom Matlab code to estimate 3D deformation and corresponding Green-Lagrange strains, a parameter that better describes large deformations. The Green-Lagrange strain tensor is implemented in many 3D strain estimation methods because it allows for F to be calculated from a Least Squares Fit (LSF) of the displacements. The equation below represents the Green-Lagrange strain tensor, E, where F and I represent the deformation gradient and second order identity tensor, respectively.
<img alt="Equation 1" src="/files/ftp_upload/10480/10480eq1v2.jpg" /> (1)

4-D Ultrasound &amp; Quantitative Strain Mapping in Abdominal Aortic Aneurysm

Source: Hannah L. Cebull1, Arvin H. Soepriatna1, John J. Boyle2 and Craig J. Goergen1 
1Weldon School of Biomedical Engineering, Purdue University, West ...

Quantification of Strain in a Porcine Model of Skin Expansion Using Multi-View Stereo and Isogeometric Kinematics

Summary

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