The authors would like to thank the Materials and Structural Testing Core at Mayo Clinic for their technical support in performing the fracture testing. In addition we would like to thank Ramesh Raghupathy and Ian Gerstel for their assistance in developing the DIC scripts and specific details of the DIC protocol during their tenure at Mayo Clinic, and the Victor Barocas Research Group, University of Minnesota for the underlying open source software that performs the core of the digital image correlation strain calculations11. This study was financially supported by the Grainger Innovation Fund from the Grainger Foundation.

All experiments were conducted with Institutional Review Board approval. The samples were obtained from anatomical research labs in collaboration.
1. Preparing Specimens for Testing
<ol>
	<li>Thaw the femora at RT for 24 h.</li>
	<li>When femur is in queue for testing, remove any wrap that was applied prior to freezing and wipe the femur with a dry towel to remove any remnant moisture, fatty deposits, or soft tissues. Pot the greater trochanter into a prefabricated aluminum cup with bone cement.</li>
	<li>Using a box to contain the particles as much as possible, spray the bone with white plastic primer to achieve a thin, uniform coating. Take care to cover the bone with one uniform layer of paint for optimal contrast and strong adhesion to the femur surface. 
	NOTE: The thickness was not measured.</li>
	<li>Let the paint dry for at least 5 min. This is important to avoid the unintended mixing with speckled droplets in Section 2.</li>
	<li>Wrap the bone with wet cloth to avoid tissue dryness.</li>
</ol>
2. Speckling Process
<ol>
	<li>Approximately, add 1 part of water to 2 parts of paint for best speckling on the bone. Add water gradually (for a better mixture) to the acrylic paint to make a black color.</li>
	<li>Dip a clean toothbrush in a palette of black paint to absorb color and flick the brush to make black speckles over the white coating.</li>
	<li>Let the paint dry for 5 min before proceeding.</li>
</ol>
3. Image Acquisition
<ol>
	<li>Mount the prepared speckled femur in the mechanical test machine by inserting the potted distal end into the fixture and tighten two screws to secure the specimen.</li>
	<li>Adjust the two high intensity discharge light reflectors such that the femur surface achieves the highest illumination possible while avoiding reflections in the camera image. Proceed quickly with the following steps in Section 3 to avoid unwanted heating of the specimen from light radiation prior to the test.</li>
	<li>Reduce the aperture of the front and back view high speed video camera lenses such that the entire region of interest of the femur in the field of view is in focus.</li>
	<li>Readjust the light reflectors to further improve illumination while reducing glare.</li>
	<li>Set the image acquisition software to capture at 6000 frames/s&#160;at a resolution of 1024 x 512 pixels. Set the total number of frames to be acquired after the trigger signal is received to 12288. Arm high speed video software for image acquisition when the trigger signal is received from the test system. When the testing is complete, the video resides in the camera&#39;s buffer memory.</li>
	<li>Using the image acquisition software, save the video to disk by specifying a desired path and file name and clicking &#34;Save&#34;. Be prepared to wait between 5 - 40 min for this process to complete depending on the number of frames to be saved.</li>
</ol>
4. Image Preparation
<ol>
	<li>Create separate working directories for front and back views of the femur.</li>
	<li>Use video analysis software to open the appropriate high speed video recording and note the key frame reference numbers at 1) the start of load frame actuator movement, and 2) the frame immediately after the fracture event.</li>
	<li>To downsample an uncompressed TIFF image sequence from the high speed video, open and run the &#34;mov_frames.m&#34; script in the working directory for the pertinent femur side.
	<ol>
		<li>In the resulting dialog box, enter the ending frame number identified in Step 4.2 with a step size of 25 - 40. Click &#34;Extract Frames&#34; and inspect the working directory to ensure the *.tiff files were extracted correctly.</li>
	</ol>
	</li>
</ol>
5. Finite Element Mesh Creation
<ol>
	<li>Use an external finite element meshing program to create the finite element mesh. Calculate 2D strains from the differential displacement vectors with the finite element method. Import the initial extracted *.tiff image into the finite element software pre-processor as a template for spline creation.</li>
	<li>Find two easy to identify fiduciary points in the image that are at opposite corners of the frame and record their X and Y coordinates (these will eventually be used in Step 6.1). These coordinates are arbitrary based on the convention the FEA software uses to import the *.tiff image. The coordinates of these points will be used to register the nodes of the finite element mesh with the corresponding pixels of the video images in Step 6.2.</li>
	<li>In an image editing software, open the same image that was imported into the finite element software preprocessor and record the X and Y direction values of the pixels associated with the points identified in step 5.2. These will eventually be used in Step 6.1.</li>
	<li>In the &#34;Sketch&#34; module of the finite element meshing program, use the spline tool to outline a closed section representing the region of interest. Verify the region is not too large such that the surface of the bone would move outside the region prior to fracture due to rotation.</li>
	<li>Prepare the closed section created in Step 5.4 for meshing by seeding the edges with a global mesh size of 1 mm under the menu &#34;Seed Part Instance&#34;.</li>
	<li>Under &#34;Assign mesh controls&#34;, set the element shape to quadrilateral.</li>
	<li>Mesh the closed section.</li>
	<li>Export the mesh to an ASCII file of the mesh database consisting of nodal coordinates and element definitions.</li>
	<li>With the resulting finite element input file open in a text editor, copy the node block containing node numbers and coordinates into a new text file and save as &#34;nodes.txt&#34;. Repeat for the element block and save the new text file as &#34;elements.txt&#34;.</li>
</ol>
6. Register the FE Mesh with the High Speed Video Images and Conduct Digital Image Correlation Analysis
<ol>
	<li>Inside a new session, create 2-element row vectors called ab1 and ab2 with the values identified in Step 5.2., and px1 and px2 with the values identified in Steps 5.3 by typing those vector names on the command line. Save the Workspace as &#34;points.mat&#34;.</li>
	<li>Run the script &#34;convert_imagesize.m&#34; to register the points from the finite element mesh with the extracted high speed video image.</li>
	<li>Run the script &#34;rrImageTrackGui.m&#34;. Load the first image (&#34;p01.tif&#34;) and enter the number of the last *.tiff file that was extracted as the total number of images to process.</li>
	<li>Load the mesh created in Step 5.7 by making sure the mesh option is set to &#34;Read from file&#34; and click &#34;Accept&#34;. The finite element mesh should appear over the bone image.</li>
	<li>Specify the tracking values based on the following guidelines for Tracking parameters and click &#34;Proceed&#34; (keeping in mind that the parameter values are image size, texture and amount of deformation taking place, and need to be tested carefully on a case-by-case basis).
	<ol>
		<li>Use a starting Kernel size of 21. The Kernel size, n, is the size of an n x n window (where n is an odd number) of pixels that is used for the cross-correlation and determination of the deformation vector for that area that will be used for strain calculations.</li>
		<li>Use a starting Subpixel size of 4. The Subpixel size, m, is the size of the (2m+1) x (2m+1) sub-window over which subpixel deformation is computed by assuming homogeneous strain in that sub-window.</li>
		<li>Use a starting Smoothness factor of 2. Smoothness factor is the amount of smoothing applied to the displacement field at tracked locations before computing strains.</li>
		<li>Use a starting maxMove factor of 10. maxMove factor is the maximum number of pixels that any node can be away from its trajectory relative to its neighbor&#39;s trajectory. This helps avoid badly tracking to deformation.</li>
		<li>Use a starting smoothGrid factor of 15. The smoothGrid factor is the size of the grid (slightly coarser than the mesh of the tracked nodes) that is used for the smoothing.</li>
	</ol>
	</li>
	<li>Select a guide point that has significant contrast around it while avoiding areas with any glare or blurriness. Check this point by clicking &#34;Check guide&#34; and verify the correlation peak is strong (at least twice the amplitude) compared to its neighbors. Click &#34;Accept&#34; and &#34;Perform Tracking&#34; when satisfied. This can be a lengthy computational process where differential displacement is computed for the temporal image sequence.</li>
	<li>After Step 6.6 has completed, click &#34;Animate&#34;. When animate has finished, click &#34;Write strains (post processing software)&#34;, enter *.exe, and then select writeStrainRR_simple.exe11. This will calculate the strains. Close the GUI.</li>
</ol>
7. Post-processing of Displacement and Strain Data
<ol>
	<li>To obtain strain Vs. frame number, run &#34;analyzeFailurePrecursor.m&#34; from the command line with an input argument of step size (choose 20 - 30). The peaks will signify bone damage, and the largest peak will correspond to the frame close to global bone failure.</li>
	<li>To create movie files of the strains, run &#34;makeMovies.m&#34; from the command window with the arguments (numVars, endstep, flag). 
	NOTE: The argument numVars is defined as 1 - 3 being displacements, 4 - 6 being xx, yy, and xy strain components, 7 &#38; 8 are the two principal and von Mises strain, and 9 is the strain energy. The argument endstop is the last frame to be included in the movie.
	<ol>
		<li>Set the optional argument flag to 1 to only create movies for the entity specified for the numVars argument and to 0 to create movies of all variables.</li>
	</ol>
	</li>
</ol>
8. Fine Tuning and Refinement of Results
<ol>
	<li>If DIC tracking gave poor results such as a discontinuous strain field that falls outside continuum mechanics assumptions, determine what is happening and why the tracking is failing. Repeat Section 6 paying particular attention to the adjustment of tracking parameters. A secondary option may be to return to the finite element software and create a more uniform and possibly finer mesh.</li>
	<li>If DIC tracking gave reasonable results, create a finer series of images for DIC. Using those key frame reference numbers from Step 4.2 and the corresponding frame rate of the video, identify the frame spacing for three different regimes of interest in the fracture test with the requirement in mind that the points should be moving no more than 6 pixels between frames. 
	NOTE: For the initial segment of the test when the strains are building up slowly in the femur, the frame spacing will be relatively large (for example, for the 100 mm/s displacement rate, the frame spacing for this portion is 3333 &#956;s). For the intermediate portion of the test closer to the fracture frame, strain is increasing more rapidly and smaller frame spacing is needed (1667 &#956;s for the 100 mm/s displacement rate). For the final portion right before fracture, the frame spacing is at its smallest (16.7 &#956;s at the 100 mm/s displacement rate).</li>
	<li>Optional for documentation purposes only: Using the information from Step 8.2, create formatted data entries in an ASCII file entitled &#34;steps.txt&#34; containing a row of data for each frame spacing. The format of each row will be the starting frame of that regime separated by the number of frames to skip (based on Step 8.2) separated by a colon then the final frame of that regime (i.e.&#160;a format of &#34;1:20:200&#34; would instruct the extraction software to extract frame 1 to frame 200 in steps of 20).
	<ol>
		<li>Immediately following that designation, insert a tab and designate the image extraction number range (for the &#34;1:20:200&#34; example, the complete row designation would be &#34;1:20:200 &#60;Press TAB&#62;1:11&#34; without the quotations). Repeat for the other two test regimes so there will be three rows of information in the &#34;steps.txt&#34; file. This file serves as a record for how the images were extracted from the original high speed video.</li>
	</ol>
	</li>
	<li>Run the mov_Frames.m code again this time specifying multiple frame spacing regimes in the dialog box. Enter the frame numbers and step sizes identified in Step 8.2 to determine the start, finish, and frame skip parameters expected in the tool&#39;s dialog box. Make sure to do this in a new directory otherwise the original images will be overwritten.</li>
	<li>Repeat Sections 6 and 7 and review the results for improvement. Each femur may require different additional iterations depending on the nature of the fracture event, speckle pattern, and lighting. When repeating Step 6.5, keep the settings the same except reduce maxMove to 6 (from 10).</li>
</ol>

<ol><li>Peters, W., Ranson, W. <a target="_blank" href="http://opticalengineering.spiedigitallibrary.org/article.aspx?articleid=1222314">Digital imaging techniques in experimental stress analysis.</a> Opt Eng. 21 (3), 213427-213427 (1982).</li>
<li>Kwon, O., Hanna, R. <a target="_blank" href="http://onlinelibrary.wiley.com/doi/10.1111/j.1475-1305.2008.00455.x/abstract">The Enhanced Digital Image Correlation Technique for Feature Tracking During Drying of Wood.</a> Strain. 46 (6), 566-580 (2010).</li>
<li>Sutton, M. A., Orteu, J. J., Schreier, H. W. <a target="_blank" href="https://link.springer.com/book/10.1007%2F978-0-387-78747-3">Image Correlation for Shape, Motion and Deformation Measurements.</a> Adv of Opt Methods in Exp Mech. 3, (2009).</li>
<li>Grassi, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((How+accurately+can+subject-specific+finite+element+models+predict+strains+and+strength+of+human+femora%3F+Investigation+using+full-field+measurements%5BTitle%5D)+AND+%22J+Biomech%22%5BJournal%5D)">How accurately can subject-specific finite element models predict strains and strength of human femora? Investigation using full-field measurements.</a> J Biomech. 49 (5), 802-806 (2016).</li>
<li>Den Buijs, J. O., Dragomir-Daescu, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Validated+finite+element+models+of+the+proximal+femur+using+two-dimensional+projected+geometry+and+bone+density%5BTitle%5D)+AND+%22Comput+Methods+Programs+Biomed%22%5BJournal%5D)">Validated finite element models of the proximal femur using two-dimensional projected geometry and bone density.</a> Comput Methods Programs Biomed. 104 (2), 168-174 (2011).</li>
<li>Keyak, J. H., Rossi, S. A., Jones, K. A., Skinner, H. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Prediction+of+femoral+fracture+load+using+automated+finite+element+modeling%5BTitle%5D)+AND+%22J+Biomech%22%5BJournal%5D)">Prediction of femoral fracture load using automated finite element modeling.</a> J Biomech. 31 (2), 125-133 (1998).</li>
<li>Lotz, J. C., Cheal, E. J., Hayes, W. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Fracture+Prediction+for+the+Proximal+Femur+Using+Finite-Element+Models+.+1Linear-Analysis%5BTitle%5D)+AND+%22J+Biomech+Eng-T+Asme%22%5BJournal%5D)">Fracture Prediction for the Proximal Femur Using Finite-Element Models . 1Linear-Analysis.</a> J Biomech Eng-T Asme. 113 (4), 353-360 (1991).</li>
<li>Cody, D. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Femoral+strength+is+better+predicted+by+finite+element+models+than+QCT+and+DXA%5BTitle%5D)+AND+%22J+Biomech%22%5BJournal%5D)">Femoral strength is better predicted by finite element models than QCT and DXA.</a> J Biomech. 32 (10), 1013-1020 (1999).</li>
<li>Dragomir-Daescu, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Robust+QCT%2FFEA+models+of+proximal+femur+stiffness+and+fracture+load+during+a+sideways+fall+on+the+hip%5BTitle%5D)+AND+%22Ann+Biomed+Eng%22%5BJournal%5D)">Robust QCT/FEA models of proximal femur stiffness and fracture load during a sideways fall on the hip.</a> Ann Biomed Eng. 39 (2), 742-755 (2011).</li>
<li>Bettamer, A., Hambli, R., Allaoui, S., Almhdie-Imjabber, A. <a target="_blank" href="http://www.tandfonline.com/doi/abs/10.1080/21681163.2015.1079505">Using visual image measurements to validate a novel finite element model of crack propagation and fracture patterns of proximal femur.</a> Comput Methods Biomech Biomed Eng Imaging Vis. , 1-12 (2015).</li>
<li>Raghupathy, R., Barocas, V. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aRaghupathy%2C+R+%22Robust+Image+Correlation+Based+Strain+Calculator+for+Tissue+Systems%22">Robust Image Correlation Based Strain Calculator for Tissue Systems</a>. , 
 <a target="_blank" href="http://license.umn.edu/technologies/20130022_robust-image-correlation-based-strain-calculator-for-tissue-systems">http://license.umn.edu/technologies/20130022_robust-image-correlation-based-strain-calculator-for-tissue-systems</a> (2016).</li>
<li>Taddei, F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Subject-specific+finite+element+models+of+long+bones%3A+An+in+vitro+evaluation+of+the+overall+accuracy%5BTitle%5D)+AND+%22J+Biomech%22%5BJournal%5D)">Subject-specific finite element models of long bones: An in vitro evaluation of the overall accuracy.</a> J Biomech. 39 (13), 2457-2467 (2006).</li>
<li>Grassi, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Accuracy+of+finite+element+predictions+in+sideways+load+configurations+for+the+proximal+human+femur%5BTitle%5D)+AND+%22J+Biomech%22%5BJournal%5D)">Accuracy of finite element predictions in sideways load configurations for the proximal human femur.</a> J Biomech. 45 (2), 394-399 (2012).</li>
<li>Gerstel, I., Raghupathy, R., Dragomir-Daescu, D. <a target="_blank" href="http://www.ors.org/Transactions/58/0138.pdf">Digital Image Correlation Identifies Quantitative Characteristics in Proximal Femur Fracture Crack.</a> ORS Annual Mtg. , (2012).</li>
</ol>

The authors have no relevant disclosures.

We introduced a protocol to consistently prepare femoral samples for high contrast imaging during fracture testing which were then used to estimate full field strain distributions with DIC. This protocol ensured appropriate contrast texture of black tracking speckles against a solid white background on the bone surface. Following this protocol, we successfully replicated the estimation of strains using DIC analysis for eighty-nine femora.
DIC is an optical method which involves placing a mesh ...

Digital Image Correlation (DIC) is an image post-processing method that is used in the current protocol to estimate the full field surface strain of cadaveric femoral test specimens from time-sequence images obtained during mechanical fracture tests. The technique was first developed and applied in experimental stress analysis in the 1980&#39;s and has experienced a rapid increase in use in recent years1,2,3. It has several key advantages over more traditional approaches of mounting strain gauges on a structure including increased spatial distribution of the strain field, finer gauge lengths through increased camera resolution, and avoiding issues with strain gauge glue adhesion or compliance. A major advantage of DIC for biological tissues, such as bone, is that it can be applied to irregular geometries comprising of highly heterogeneous material properties4,5. Its primary drawback over traditional strain acquisition methods is that it requires expensive high speed video cameras of sufficient resolution for the measurement of the region of interest to achieve sufficient spatial and temporal sampling to accurately estimate strain fields.
The primary application of the temporal strain fields obtained from bone fracture DIC analysis is to validate the strain estimations in QCT/FEA models of femoral strength5. Such validation is the focus of many orthopedic research groups which predominantly utilize remote measurements of force and displacement from load cells and displacement transducers6,7,8. In addition, post-fracture image analysis of the fracture pattern has been combined with these remote measurements as further means of model validation9. More recently, the DIC method was applied to validate an FEA model of fracture and crack propagation in the proximal femur10. By utilizing strain correlation between models and experiments, even more confidence in the validity of computational models of proximal femora will be obtained and further advance the QCT/FEA diagnostic method closer to clinical use.
This work explains a detailed protocol to incorporate the necessary steps for DIC analysis in fracture testing of proximal femora. The procedure included the bone preparation steps of spraying a white paint on the bone surface and then speckling black spots on the dried white surface of the bone, methods of obtaining images with sufficient spatial and temporal resolution using high speed video cameras, and the process and tools we used for computing strain fields from these images. We also explained several caveats that may affect the quality of the measurements.

<table><tbody><tr><td>Krylon plastic primer white</td><td>Krylon, Peoria, AZ, USA</td><td>N/A</td><td>Used as a base coat for a smooth white finish on bone surface</td></tr><tr><td>Water-based acrylic white and black paint&amp;nbsp;</td><td>Plaid Enterprises (Ceramcoat), Norcross, GA, USA</td><td>N/A</td><td>Paint source for white and black colors</td></tr><tr><td>Mixing bowl</td><td>Not specific (generic)</td><td>N/A</td><td>Used to mix and prepare paint</td></tr><tr><td>Foam brush</td><td>Linzer Products, Wyandanch, NY, USA</td><td>N/A</td><td>Used to apply paint on bone surface</td></tr><tr><td>Toothbrush</td><td>Colgate-Palmolive, New York, NY, USA</td><td>Firm bristle</td><td>Used to apply appropriate size and distribution of speckling pattern</td></tr><tr><td>Hygenic Orthodontic Resin (PMMA)</td><td>Patterson Dental, St Paul, MN, USA</td><td>H02252</td><td>Controlled substance and can be purchased with proper approval</td></tr><tr><td>Kenmore Freezer</td><td>Sears Holdings, Hoffman Estates, IL, USA</td><td>N/A</td><td>Used to maintain a -20&lt;sup&gt;o&lt;/sup&gt;C storage enviroment for bone specimens</td></tr><tr><td>Physiologic Saline (0.9% Sodium Chloride)</td><td>Baxter Healthcare, Deerfield, IL, USA</td><td>NDC 0338-0048-04</td><td>Used for keeping specimens hydrated</td></tr><tr><td>Scalpels and scrapers</td><td>Aspen Surgical (Bard-Parker), Caledonia, MI, USA&amp;nbsp;</td><td>N/A</td><td>Used to remove soft tissue from bone specimens</td></tr><tr><td>Fume Hood</td><td>Hamilton Laboratory Solutions, Manitowoc, WI, USA</td><td>70532</td><td>Used for ventilation when preparing PMMA for potting of specimens</td></tr><tr><td>Lighting units</td><td>ARRI, Munich, Germany</td><td>N/A</td><td>Needed for illumination of target for image capture</td></tr><tr><td>High-speed video camera</td><td>Photron Inc., San Diego, CA, USA</td><td>Photron Fastcam APX-RS&amp;nbsp;</td><td>Used to capture the high speed video recordings of the fracture events</td></tr><tr><td>Photron FASTCAM Imager and Viewer</td><td>Photron Inc., San Diego, CA, USA</td><td>Ver.3392(x64)</td><td>Used to record and view the high speed video recordings</td></tr><tr><td>Camera lens</td><td>Zeiss, Oberkochen, Germany</td><td>Zeiss Planar L4/50 ZF Lens</td><td>Needed for appropriate image resolution</td></tr><tr><td>ABAQUS CAE</td><td>Dassault System&amp;egrave;s, Waltham, MA, USA</td><td>Versions 6.13-4</td><td>Used for defining region of interest and creating finite element mesh</td></tr><tr><td>MATLAB</td><td>Mathworks, Natick, MA, USA</td><td>Version 2015b</td><td>Used for image processing and DIC analysis</td></tr><tr><td>TecPlot</td><td>TecPlot Inc., Bellevue, WA</td><td></td><td>Used for post processing of strain fields</td></tr><tr><td>Strain Calculator Software</td><td>Victor Barocas Research Group, University of Minnesota, Minneapolis, MN, USA</td><td>http://license.umn.edu/technologies/20130022_robust-image-correlation-based-strain-calculator-for-tissue-systems</td><td>Used to calculate strain field</td></tr><tr><td>mov_frames.m</td><td>Matlab script, Mayo Clinic, Rochester, MN,USA</td><td>N/A</td><td>Used to downsample uncompressed images from high speed video files</td></tr><tr><td>convert_imagesize.m</td><td>Matlab script, Mayo Clinic, Rochester, MN,USA</td><td>N/A</td><td>Used to register image pixel coordinates with mesh coordinates</td></tr><tr><td>rrImageTrackGui.m</td><td>Matlab script, Mayo Clinic, Rochester, MN,USA</td><td>N/A</td><td>Used to perform the image cross-correlation to obtain deformations and run Strain Calculator</td></tr><tr><td>analyzeFailurePrecursor.m</td><td>Matlab script, Mayo Clinic, Rochester, MN,USA</td><td>N/A</td><td>Used to track the peak strain components temporally</td></tr><tr><td>makeMovies.m</td><td>Matlab script, Mayo Clinic, Rochester, MN,USA</td><td>N/A</td><td>Used to create portable *.avi movies of the deformation components, strain components, principal strains, von Mises strain, and strain energy</td></tr></tbody></table>

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).

Before the speckling process, the femur is cleaned from excess fat and soft tissues, and the greater trochanter is potted in an aluminum cup. During solidification of polymethylmethacrylate (PMMA), the bone is wrapped in a saline soaked cloth to avoid tissue dryness. Once PMMA is solidified, the bone is cleaned again right before spraying (Figure 1). Then, the bone surface is sprayed or brushed with a water-based plastic white color. Once dri...

Watch this Scientific Journal Video about A Method to Estimate Cadaveric Femur Cortical Strains During Fracture Testing Using Digital Image Correlation at JoVE.com

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

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 ...

a-method-to-estimate-cadaveric-femur-cortical-strains-during-fracture

Research

JoVE Journal

Bioengineering

7.4K Views.  Mayo Clinic. 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. 

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

Full-field Strain Measurements for Microstructurally Small Fatigue Crack Propagation Using Digital Image Correlation Method

A novel measurement approach is used to reveal the cumulative deformation field at a sub-grain level and to study the influence of microstructure on the growth of microstructurally small fatigue cracks. The proposed strain field analysis methodology is based on the use of a unique pattering technique with a characteristic speckle size of approximately 10 &#181;m. The developed methodology is applied to study the small fatigue crack behavior in body centered cubic (bcc) Fe-Cr ferritic stainless steel with a relatively large grain size allowing a high spatial measurement accuracy at the sub-grain level. This methodology allows the measurement of small fatigue crack growth retardation events and associated intermittent shear strain localization zones ahead of the crack tip. In addition, this can be correlated with the grain orientation and size. Thus, the developed methodology can provide a deeper fundamental understanding of the small fatigue crack growth behavior, required for the development of robust theoretical models for the small fatigue crack&#160;propagation in polycrystalline materials.

Microstructurally small fatigue crack growth behavior is investigated using a novel methodological approach combining crack growth rate measurement and strain-field analysis to reveal the cumulative deformation field at sub-grain level.

A novel measurement approach is used to reveal the cumulative deformation field at a sub-grain level and to study the influence of microstructure on the ...

Engineering

Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation

A procedure using digital image correlation (DIC) to detect cracks on welded specimens during fatigue tests on resonance testing machines is presented. It is intended as a practical and reproducible procedure to identify macroscopic cracks at an early stage and monitor crack propagation during fatigue tests. It consists of strain field measurements at the weld using DIC. Images are taken at fixed load cycle intervals. Cracks become visible in the computed strain field as elevated strains. This way, the whole width of a small-scale specimen can be monitored to detect where and when a crack initiates. Subsequently, it is possible to monitor the development of the crack length. Because the resulting images are saved, the results are verifiable and comparable. The procedure is limited to cracks initiating at the surface and is intended for fatigue tests under laboratory conditions. By visualizing the crack, the presented procedure allows direct observation of macrocracks from their formation until rupture of the specimen.

Digital image correlation is used in fatigue tests on a resonance testing machine to detect macroscopic cracks and monitor crack propagation in welded specimens. Cracks on the specimen surface become visible as increased strains.

A procedure using digital image correlation (DIC) to detect cracks on welded specimens during fatigue tests on resonance testing machines is presented. It ...

Method and Instrumented Fixture for Femoral Fracture Testing in a Sideways Fall-on-the-Hip Position

Mechanical testing of femora brings valuable insights into understanding the contribution of clinically-measureable variables such as bone mineral density distribution and geometry on the femoral mechanical properties. Currently, there is no standard protocol for mechanical testing of such geometrically complex bones to measure strength, and stiffness. To address this gap we have developed a protocol to test cadaveric femora to fracture and to measure their biomechanical parameters. This protocol describes a set of adaptable fixtures to accommodate the various load magnitudes and directions accounting for possible bone orientations in a fall on the hip configuration, test speed, bone size, and left leg-right leg variations. The femora were prepared for testing by cleaning, cutting, scanning, and potting the distal end and greater trochanter contact surfaces in poly(methyl methacrylate) (PMMA) as presented in a different protocol. The prepared specimens were placed in the testing fixture in a position mimicking a sideways fall on the hip and loaded to fracture. During testing, two load cells measured vertical forces applied to the femoral head and greater trochanter, a six-axis load cell measured forces and moments at the distal femoral shaft, and a displacement sensor measured differential displacement between the femoral head and trochanter contact supports. High speed video cameras were used to synchronously record the sequence of fracture events during testing. The reduction of this data allowed us to characterize the strength, stiffness, and fracture energy for nearly 200 osteoporotic, osteopenic, and normal cadaveric femora for further development of engineering-based diagnostic tools for osteoporosis research.

In this manuscript, we present a protocol to fracture test cadaveric proximal femora in a sideways fall on the hip configuration using instrumented fixtures mounted on a standard servo hydraulic frame. Nine digitized signals comprising forces, moments, and displacement along with two high speed video streams are acquired during testing.

Mechanical testing of femora brings valuable insights into understanding the contribution of clinically-measureable variables such as bone mineral density ...

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 ...

Imaging of the Microstructural Failure Mechanism in the Human Hip

Imaging the bone microstructure under progressively increasing loads allows for observing the microstructural failure behavior of bone. Here, we describe a protocol for obtaining a sequence of three-dimensional microstructural images of the entire proximal femur under progressively increasing deformation, causing clinically relevant fractures of the femoral neck. The protocol is demonstrated using four femora from female donors aged 66-80 years at the lower end of bone mineral density in the population (T-score range = &#8722;2.09 to &#8722;4.75). A radio-transparent compressive stage was designed for loading the specimens replicating a one-leg stance, while recording the applied load during&#160;micro-computed tomography (micro-CT) imaging. The field of view was 146 mm wide and 132 mm high, and the isotropic pixel size was 0.03 mm. The force increment was based on finite-element predictions of the fracture load. The compressive stage was used to apply the displacement to the specimen and enact the prescribed force increments. Sub-capital fractures due to opening and shear of the femoral neck occurred after four to five load increments. The micro-CT&#160;images and the reaction force measurements were processed to study the bone strain and energy absorption capacity. Instability of the cortex appeared at the early loading steps. The subchondral bone in the femoral head displayed large deformations reaching 16% before&#160;fracture, and a progressive increase in the support capacity up to&#160;fracture. The deformation energy linearly increased with the displacement up to&#160;fracture, while the stiffness decreased to near-zero values immediately before&#160;fracture. Three-fourths of the fracture energy was taken by the specimen during the final 25% force increment. In conclusion, the protocol developed revealed a remarkable energy absorption capacity, or damage tolerance, and a synergic interaction between the cortical and trabecular bone at an advanced donor age.

The protocol enables the measurement of the deformation of the bone microstructure in the entire proximal human femur and its toughness by combining large-volume micro-CT scanning, a custom-made compressive stage, and advanced image processing tools.

Imaging the bone microstructure under progressively increasing loads allows for observing the microstructural failure behavior of bone. Here, we describe ...

Proximal Cadaveric Femur Preparation for Fracture Strength Testing and Quantitative CT-based Finite Element Analysis

Cadaveric fracture testing is routinely used to understand factors that affect proximal femur strength. Because ex vivo biological tissues are prone to lose their mechanical properties over time, specimen preparation for experimental testing must be performed carefully to obtain reliable results that represent in vivo conditions. For that reason, we designed a protocol and a set of fixtures to prepare the femoral specimens such that their mechanical properties experienced minimal changes. The femora were kept in a frozen state except during preparation steps and mechanical testing. The relevant clinical measures of total hip and femoral neck bone mineral density (BMD) were obtained with a clinical dual X-ray absorptiometry (DXA) bone densitometer, and the 3D geometry and distribution of bone mineral were obtained using CT with a calibration phantom for quantitative estimations based on the greyscale values. Any possible bone disease, fracture, or the presence of implants or artifacts affecting the bone structure, was ruled out with X-ray scans. For preparation, all bones were carefully cleaned of excess soft tissue, and were cut and potted at the internal rotation angle of interest. A cutting fixture allowed the distal end of the bone to be cut off leaving the proximal femur at a desired length. To allow positioning of the femoral neck at prescribed angles during later CT scanning and mechanical testing, the proximal femoral shafts were potted in polymethylmethacrylate (PMMA) using a fixture designed specifically for desired orientations. The data collected from our experiments were then used for validation of quantitative computed tomography (QCT)-based finite element analysis (FEA), as described in a different protocol. In this manuscript, we present the protocol for the precise bone preparation for mechanical testing and subsequent QCT/FEA modeling. The current protocol was successfully applied to prepare about 200 cadaveric femora over a 6-year time period.

We present a robust protocol on how to carefully preserve and prepare cadaveric femora for fracture testing and quantitative computed tomography imaging. The method provides precise control over input conditions for the purpose of determining relationships between bone mineral density, fracture strength, and defining finite element model geometry and properties.

Cadaveric fracture testing is routinely used to understand factors that affect proximal femur strength.  Because ex vivo biological tissues are prone to ...

Medicine

Cortical Bone Assessment Using the Ultrasonic BDAT Device

Cortical Bone Assessment Using Ultrasonic Guided Waves: A Reproducibility Study in a Healthy Population

Fragility fractures are still a worldwide health burden in the context of population aging. In particular, the global number of hip fractures is expected to double between 2020 and 2050. Therefore, it is essential to detect patients at risk of fragility fracture at the population scale. The current golden standard is dual X-ray absorptiometry (DXA), providing the areal bone mineral density (aBMD). Ultrasonic devices, usually more portable and cheaper than X-ray devices, represent interesting DXA alternatives as screening tools. However, operator dependency is usually recognized as their main drawback. In this study, the measurement protocol of the bi-directional axial transmission (BDAT) ultrasonic device is presented in detail. The dedicated ultrasonic probe is placed at the one-third distal radius of the non-dominant forearm using conventional coupling gel. The guided interface provides in quasi-real time (about 2 Hz) four parameters of interest: velocities of the first arriving signal (vFAS) and the A0 mode (vA0),&#160;cortical thickness (Ct.Th) and porosity (Ct.Po), as well as four quality parameters. The operator moves the probe slowly at the measurement site, carefully observing the feedback provided by the interface until finding a stable position and starting a series of 10 acquisitions. When at least four consistent series are obtained, the measurement ends, and an automatic report is generated. The measurement usually takes about 5 min to complete. To determine the robustness of this protocol, a reproducibility study was conducted among 3 operators (one expert and two novices) and 14 healthy participants (6 women, 8 men, 21-53 years old). The intraclass correlation coefficients (ICC) were found good for vA0 (0.76), Ct.Po (0.80) or excellent for Ct.Th (0.87) and vFAS (0.91). The standard deviations were found to be less than 10% of the total ranges in clinical practice.

Here, we present the measurement protocol of the bi-directional axial transmission (BDAT) ultrasonic device in detail and test it in a reproducibility study, considering 14 healthy participants and 3 operators. Reliability, measured with the intraclass correlation coefficients (ICC), was good to excellent for four parameters of interest.

Fragility fractures are still a worldwide health burden in the context of population aging. In particular, the global number of hip fractures is expected ...

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 ...

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

Assessment of Bone Fracture Healing Using Micro-Computed Tomography

Micro-computed tomography (&#181;CT) is the most common imaging modality to characterize the three-dimensional (3D) morphology of bone and newly formed bone during fracture healing in translational science investigations. Studies of long bone fracture healing in rodents typically involve secondary healing and the formation of a mineralized callus. The shape of the callus formed and the density of the newly formed bone may vary substantially between timepoints and treatments. Whereas standard methodologies for quantifying parameters of intact cortical and trabecular bone are widely used and embedded in commercially available software, there is a lack of consensus on procedures for analyzing the healing callus. The purpose of this work is to describe a standardized protocol that quantitates bone volume fraction and callus mineral density in the healing callus. The protocol describes different parameters that should be considered during imaging and analysis, including sample alignment during imaging, the size of the volume of interest, and the number of slices that are contoured to define the callus.

Micro-computed tomography (&#181;CT) is a non-destructive imaging tool that is instrumental in assessing bone structure in preclinical studies, however there is a lack of consensus on &#181;CT procedures for analyzing the bone healing callus. This study provides a step-by-step &#181;CT protocol that allows the monitoring of fracture healing.

Micro-computed tomography (&#181;CT) is the most common imaging modality to characterize the three-dimensional (3D) morphology of bone and newly formed ...

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

Summary

Explore More Videos

Full-field Strain Measurements for Microstructurally Small Fatigue Crack Propagation Using Digital Image Correlation Method

Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation

Method and Instrumented Fixture for Femoral Fracture Testing in a Sideways Fall-on-the-Hip Position

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

Imaging of the Microstructural Failure Mechanism in the Human Hip

Proximal Cadaveric Femur Preparation for Fracture Strength Testing and Quantitative CT-based Finite Element Analysis

Cortical Bone Assessment Using Ultrasonic Guided Waves: A Reproducibility Study in a Healthy Population

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

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

Assessment of Bone Fracture Healing Using Micro-Computed Tomography