Digital Image Correlation Cartilage 2021 Open Access

Digital Image Correlation (DIC) has emerged as a powerful tool in biomechanics, particularly for analyzing the mechanical behavior of cartilage. Its non-contact, full-field measurement capabilities offer unique insights into cartilage deformation under various loading conditions. This article delves into the application of DIC in cartilage research up to 2021, highlighting its advantages, methodologies, challenges, and the significant open-access resources available in this field.

Understanding Digital Image Correlation

DIC is an optical technique used to measure the displacement and strain fields on the surface of an object. It works by tracking the movement of small subsets (or facets) of pixels within a series of digital images as the object deforms. The basic principle involves:

• Speckle Pattern Application: A random speckle pattern is applied to the surface of the sample. This pattern can be created using various methods, such as spraying paint or using a fine-tipped marker.
• Image Acquisition: A series of digital images is captured using one or more cameras while the object is subjected to a mechanical load or deformation.
• Image Processing: Specialized DIC software analyzes the images, identifying and tracking the movement of the speckle pattern. By comparing the position of the subsets in the deformed images relative to the reference image, the software calculates the displacement field.
• Strain Calculation: From the displacement field, the strain field can be calculated using numerical differentiation techniques.

Why DIC is Important for Cartilage Research

Cartilage is a complex tissue that provides a smooth, low-friction surface for joint movement. Understanding its mechanical behavior is crucial for developing effective treatments for cartilage injuries and diseases like osteoarthritis. DIC offers several advantages over traditional methods for characterizing cartilage mechanics:

• Full-Field Measurement: Unlike traditional strain gauges or extensometers, DIC provides full-field measurements of displacement and strain, revealing local variations in mechanical behavior.
• Non-Contact Technique: DIC is a non-contact method, meaning it does not interfere with the mechanical behavior of the cartilage. This is particularly important for soft tissues like cartilage, which can be easily affected by contact-based measurement techniques.
• Versatile Application: DIC can be applied to various cartilage samples, including in vitro explants, ex vivo joints, and even in situ measurements during surgical procedures.
• High Resolution: DIC can achieve high spatial resolution, allowing for detailed analysis of cartilage deformation at the microstructural level.
• Open Access Resources: The increasing availability of open-access DIC data and software promotes collaboration and accelerates research progress.

Methodologies for Applying DIC to Cartilage

Applying DIC to cartilage requires careful consideration of several factors, including sample preparation, speckle pattern application, imaging setup, and data analysis. Here's a breakdown of the key methodologies:

1. Sample Preparation

• Tissue Source: Cartilage samples can be obtained from various sources, including cadaveric joints, animal models, or surgical waste.
• Sample Geometry: The geometry of the cartilage sample depends on the specific research question. Common geometries include cylindrical plugs, rectangular sections, and intact joint surfaces.
• Hydration: Cartilage is highly hydrated, and maintaining its hydration level is crucial for accurate mechanical testing. Samples should be kept immersed in a physiological solution (e.g., phosphate-buffered saline) during preparation and testing.

2. Speckle Pattern Application

• Contrast: The speckle pattern must provide sufficient contrast for accurate image correlation. The size and density of the speckles should be optimized for the resolution of the imaging system.
• Methods:
  • Spraying: Airbrushing or spray painting with a fine nozzle is a common method for applying a speckle pattern. The paint should be biocompatible and non-toxic.
  • Micro-Particles: Applying micro-particles (e.g., titanium dioxide) suspended in a liquid carrier can create a fine, uniform speckle pattern.
  • Stamping/Printing: Micro-contact printing or stamping can be used to create precise and repeatable speckle patterns.

3. Imaging Setup

• Camera System: The choice of camera system depends on the desired resolution, field of view, and frame rate. High-resolution cameras are essential for capturing fine details of cartilage deformation.
• Lighting: Uniform and consistent lighting is crucial for obtaining high-quality images. Diffuse lighting can minimize shadows and reflections.
• Lens Selection: The lens should be chosen to provide the desired magnification and depth of field.
• Stereo vs. 2D DIC:
  • 2D DIC: Uses a single camera to measure in-plane displacements. Suitable for planar samples and when out-of-plane motion is negligible.
  • Stereo DIC: Uses two cameras to measure both in-plane and out-of-plane displacements. Essential for non-planar samples and when out-of-plane motion is significant.

4. Mechanical Loading

• Loading Type: The type of mechanical loading should be relevant to the physiological conditions experienced by cartilage in the joint. Common loading types include compression, tension, shear, and indentation.
• Loading Apparatus: A mechanical testing machine is used to apply controlled loads or displacements to the cartilage sample.
• Data Acquisition: Load and displacement data should be recorded simultaneously with the DIC images.

5. Data Analysis

• DIC Software: Several commercial and open-source DIC software packages are available. These software packages provide tools for image correlation, displacement calculation, and strain analysis.
• Subset Size and Step Size: The subset size and step size are important parameters that affect the accuracy and resolution of the DIC results.
• Strain Calculation: Strain can be calculated from the displacement field using various numerical differentiation techniques.
• Data Validation: The DIC results should be validated by comparing them to analytical solutions, finite element simulations, or other experimental measurements.

Applications of DIC in Cartilage Research (up to 2021)

DIC has been applied to address a wide range of research questions related to cartilage mechanics:

• Characterizing Cartilage Material Properties: DIC has been used to measure the elastic modulus, Poisson's ratio, and shear modulus of cartilage under various loading conditions.
• Investigating Cartilage Degeneration: DIC has revealed changes in cartilage mechanical behavior associated with osteoarthritis and other degenerative conditions.
• Evaluating Cartilage Repair Strategies: DIC has been used to assess the effectiveness of various cartilage repair techniques, such as microfracture, osteochondral grafting, and cell-based therapies.
• Analyzing Cartilage Contact Mechanics: DIC has provided insights into the contact stresses and strains in cartilage during joint loading.
• Developing Finite Element Models: DIC data can be used to validate and refine finite element models of cartilage, which can be used to predict cartilage behavior under complex loading conditions.
• Understanding the Influence of Microstructure: DIC is used to understand how collagen fiber orientation and other microstructural features influence the local mechanical response of cartilage.

Challenges and Limitations of DIC in Cartilage Research

While DIC is a powerful tool, it also has certain challenges and limitations:

• Speckle Pattern Application: Creating a uniform and durable speckle pattern on cartilage can be challenging, especially for in vivo applications.
• Image Quality: Poor image quality (e.g., due to low contrast, reflections, or motion blur) can affect the accuracy of the DIC results.
• Computational Cost: DIC analysis can be computationally intensive, especially for large image datasets.
• Out-of-Plane Motion: Out-of-plane motion can introduce errors in 2D DIC measurements. Stereo DIC is required to accurately measure displacements in three dimensions.
• Penetration Depth: DIC only measures surface deformation. It does not provide information about the mechanical behavior of the cartilage matrix below the surface.
• Data Interpretation: Interpreting DIC data can be complex, especially when dealing with heterogeneous materials like cartilage.

Open Access Resources for DIC in Cartilage Research (Up to 2021)

The open-access movement has significantly benefited the field of DIC in cartilage research, fostering collaboration and accelerating scientific discovery. Several valuable resources were available up to 2021:

1. Open-Source DIC Software

• Ncorr: A MATLAB-based 2D DIC software package developed by researchers at Brigham Young University. Ncorr is freely available and provides a user-friendly interface for performing DIC analysis.
• DICe: Developed by Sandia National Laboratories, DICe is an open-source, parallelizable DIC code. It is designed for high-performance computing and can handle large image datasets.
• OpenPIV: While primarily designed for particle image velocimetry, OpenPIV can also be adapted for DIC analysis.

2. Open-Access Publications

• PubMed Central: A free archive of biomedical and life sciences literature. Searching PubMed Central for "digital image correlation" and "cartilage" will yield a wealth of open-access research articles.
• Directory of Open Access Journals (DOAJ): A directory of open-access journals covering a wide range of subjects.
• University Repositories: Many universities maintain open-access repositories where researchers can deposit their publications and data.

3. Open Data Repositories

• Dryad: A curated, general-purpose repository that makes research data discoverable, freely reusable, and citable.
• Zenodo: A research data repository hosted by CERN.
• Figshare: A repository where users can share all of their research outputs, including figures, datasets, and code.

4. Online Forums and Communities

• ResearchGate: A social networking site for scientists and researchers. ResearchGate provides a platform for researchers to share their work, ask questions, and collaborate with colleagues.
• LinkedIn Groups: Several LinkedIn groups are dedicated to DIC and related topics. These groups provide a forum for researchers to discuss their work and share resources.

Future Directions

While DIC has already made significant contributions to cartilage research, several exciting avenues for future research exist:

• Integration with Other Imaging Modalities: Combining DIC with other imaging modalities, such as MRI, CT, and optical coherence tomography (OCT), can provide a more comprehensive understanding of cartilage structure and function.
• In Vivo DIC: Developing techniques for applying DIC in vivo would allow for real-time monitoring of cartilage mechanics during joint loading.
• High-Throughput DIC: Developing high-throughput DIC methods would enable the screening of large numbers of cartilage samples, accelerating the discovery of new treatments for cartilage injuries and diseases.
• Machine Learning: Applying machine learning techniques to DIC data could help to identify patterns and predict cartilage behavior under complex loading conditions.
• Standardization: Efforts to standardize DIC protocols and data analysis methods would improve the reproducibility and comparability of research findings.

Conclusion

Digital Image Correlation is a valuable tool for investigating the mechanical behavior of cartilage. Its non-contact, full-field measurement capabilities offer unique insights into cartilage deformation under various loading conditions. The increasing availability of open-access resources has significantly benefited the field, fostering collaboration and accelerating scientific discovery. Despite certain challenges and limitations, DIC holds great promise for advancing our understanding of cartilage mechanics and developing new treatments for cartilage injuries and diseases. As technology advances, we can expect to see even more innovative applications of DIC in cartilage research, leading to improved outcomes for patients with joint disorders.