Digital Image Correlation Tendon 2021 Open Access

Digital Image Correlation (DIC) has revolutionized experimental mechanics, providing a non-contact and full-field strain measurement technique that has found widespread applications across various engineering disciplines. Its adoption in the study of tendons, particularly in understanding their complex biomechanical behavior, has surged in recent years. This exploration focuses on the open access advancements in DIC for tendon research, specifically highlighting key developments and applications documented up to 2021. By making research freely available, the open access movement has accelerated innovation and collaboration within the biomechanics community, leading to a deeper understanding of tendon mechanics and pathology.

Digital Image Correlation: A Primer

Before diving into the specifics of DIC applications in tendon research, it's crucial to understand the underlying principles of this powerful technique. DIC is an optical method that tracks the deformation of a material's surface by comparing digital images captured before and after deformation. Here's a breakdown of the process:

• Surface Preparation: The surface of the specimen is typically prepared by applying a random speckle pattern. This pattern can be created using spray paint, airbrushing, or even applying toner powder. The key is to have a high-contrast, random pattern that is stable throughout the experiment.
• Image Acquisition: A series of digital images are captured during the experiment using one or more cameras. The images must be of high quality and resolution to accurately track the speckle pattern.
• Image Processing: The DIC software divides the images into small subsets or facets. The software then searches for the corresponding subset in the deformed image. This is done using correlation algorithms that minimize the difference between the subsets.
• Displacement and Strain Calculation: Once the displacement of each subset is determined, the strain field can be calculated. This provides a full-field map of the strain distribution on the surface of the specimen.

DIC offers several advantages over traditional strain measurement techniques, such as strain gauges:

• Non-Contact: DIC is a non-contact technique, meaning it does not affect the mechanical behavior of the specimen. This is particularly important for delicate tissues like tendons.
• Full-Field Measurement: DIC provides a full-field map of the strain distribution, unlike strain gauges that only measure strain at a single point.
• Versatility: DIC can be used to measure both small and large deformations, and it can be applied to a wide range of materials and geometries.

Tendon Biomechanics: The Need for Advanced Measurement Techniques

Tendons are fibrous connective tissues that transmit forces from muscles to bones, enabling movement. They are primarily composed of collagen fibers arranged in a hierarchical structure. Understanding the biomechanical behavior of tendons is crucial for:

• Preventing Injuries: Tendon injuries, such as tendinitis and ruptures, are common in athletes and the general population. Understanding the mechanisms of injury can help develop strategies for prevention.
• Improving Treatment: Knowledge of tendon mechanics is essential for designing effective treatments for tendon injuries. This includes surgical repairs, rehabilitation protocols, and regenerative medicine approaches.
• Designing Biomaterials: Tendon tissue engineering aims to create functional tendon replacements. Understanding the biomechanical properties of native tendons is crucial for designing biomaterials that can mimic their behavior.

Traditional methods for studying tendon mechanics, such as tensile testing with extensometers, provide valuable information but have limitations. They typically only measure the average strain over a relatively large gauge length and do not capture the complex strain distributions within the tendon. This is where DIC comes in.

DIC Applications in Tendon Research: Open Access Advancements up to 2021

The open access movement has significantly contributed to the advancement of DIC applications in tendon research by making research findings readily available to a wider audience. This has fostered collaboration and accelerated the pace of innovation. Here are some key areas where open access DIC research has made a significant impact up to 2021:

1. Characterizing Tendon Heterogeneity

One of the key advantages of DIC is its ability to capture the heterogeneous strain distribution within tendons. Tendons are not uniform structures; they exhibit variations in collagen fiber alignment, cross-linking, and hydration. These variations can lead to localized stress concentrations and potentially contribute to injury.

Open access studies using DIC have revealed significant heterogeneity in tendon strain fields. For example, researchers have used DIC to show that strain is higher in regions with less organized collagen fibers or in areas with pre-existing microdamage. This information is critical for understanding how tendons respond to loading and how injuries initiate and propagate.

Furthermore, DIC has been used to investigate the interfascicular matrix, the material that surrounds and connects the collagen fiber bundles within the tendon. Studies have shown that the interfascicular matrix plays a crucial role in transmitting load between the fiber bundles and that its properties can significantly influence the overall tendon behavior. Open access DIC research has provided valuable insights into the mechanics of the interfascicular matrix and its contribution to tendon heterogeneity.

2. Investigating the Effects of Aging and Disease

Aging and disease can significantly alter the biomechanical properties of tendons, making them more susceptible to injury. Open access DIC studies have been instrumental in characterizing these changes.

For instance, researchers have used DIC to investigate the effects of aging on tendon strain distribution. These studies have shown that aged tendons tend to exhibit higher strain concentrations and reduced ability to distribute load evenly. This may be due to age-related changes in collagen cross-linking, hydration, or fiber alignment.

Similarly, DIC has been used to study the effects of diseases such as diabetes and tendinopathy on tendon mechanics. Studies have shown that these conditions can lead to changes in tendon stiffness, viscosity, and strain distribution. Open access DIC research has provided valuable insights into the mechanisms by which aging and disease affect tendon biomechanics.

3. Evaluating Tendon Repair Techniques

Tendon repair is a challenging clinical problem, and many current repair techniques have limited success rates. DIC can be used to evaluate the effectiveness of different repair techniques by measuring the strain distribution at the repair site.

Open access studies have used DIC to compare the biomechanical performance of different suture configurations, graft materials, and tissue engineering approaches for tendon repair. These studies have shown that certain repair techniques can lead to more uniform strain distribution and improved mechanical strength.

Moreover, DIC can be used to assess the healing process after tendon repair. By tracking the strain distribution over time, researchers can monitor the formation of new tissue and the restoration of mechanical function. This information can be used to optimize rehabilitation protocols and improve the long-term outcomes of tendon repair.

4. Validating Computational Models

Computational models are increasingly used to simulate tendon biomechanics and predict the effects of different interventions. DIC can be used to validate these models by comparing the predicted strain fields with the experimentally measured strain fields.

Open access studies have used DIC data to validate finite element models of tendons. These studies have shown that the accuracy of the models depends on the material properties assigned to the different tendon components, such as collagen fibers and the interfascicular matrix.

By comparing the model predictions with the experimental data, researchers can refine the models and improve their predictive capabilities. This can lead to more accurate simulations of tendon behavior and more effective strategies for preventing and treating tendon injuries.

5. Exploring the Effects of Loading Rate and Fatigue

Tendons are subjected to a wide range of loading conditions during daily activities and sports. The loading rate and the number of loading cycles can significantly affect tendon behavior and potentially lead to fatigue damage.

Open access DIC studies have investigated the effects of loading rate and fatigue on tendon strain distribution. These studies have shown that higher loading rates can lead to higher strain concentrations and increased risk of injury. Similarly, repeated loading can cause microdamage to accumulate within the tendon, leading to changes in its mechanical properties and increased susceptibility to rupture.

DIC has been used to characterize the fatigue behavior of tendons under different loading conditions. This information is critical for understanding how tendons respond to prolonged exercise and for developing strategies to prevent overuse injuries.

Challenges and Future Directions

While DIC has proven to be a powerful tool for studying tendon biomechanics, there are still some challenges that need to be addressed. Some of these challenges include:

• Specimen Preparation: Creating a high-quality speckle pattern on soft tissues like tendons can be challenging. The speckle pattern must be stable, uniform, and have good contrast.
• Image Resolution: The accuracy of DIC measurements depends on the resolution of the images. High-resolution images are needed to capture the fine details of the speckle pattern and accurately track the deformation.
• Computational Cost: DIC analysis can be computationally intensive, especially for large datasets. Faster algorithms and more powerful computers are needed to process the data efficiently.
• Three-Dimensional DIC: Most DIC systems are limited to measuring surface strains. Three-dimensional DIC techniques are needed to capture the full strain field within the tendon.

Despite these challenges, the future of DIC in tendon research is bright. With continued advancements in imaging technology, computational algorithms, and open access initiatives, DIC will continue to play a crucial role in advancing our understanding of tendon biomechanics and improving the treatment of tendon injuries. Some potential future directions include:

• In Vivo DIC: Developing DIC techniques that can be used to measure tendon strains in vivo would provide valuable information about tendon behavior during real-world activities.
• Multi-Scale DIC: Combining DIC with other imaging modalities, such as microscopy and ultrasound, would allow researchers to study tendon mechanics at different length scales.
• Machine Learning: Applying machine learning algorithms to DIC data could help to identify patterns and relationships that are not apparent using traditional analysis methods.

Conclusion

Digital Image Correlation has emerged as a vital tool for investigating the biomechanics of tendons, providing detailed, full-field strain measurements that traditional methods cannot offer. The open access movement has been instrumental in accelerating the adoption and development of DIC in tendon research, fostering collaboration and innovation within the biomechanics community. As research up to 2021 demonstrates, DIC has been crucial in characterizing tendon heterogeneity, understanding the effects of aging and disease, evaluating repair techniques, validating computational models, and exploring the effects of loading rate and fatigue. Addressing current challenges and exploring future directions will further enhance the capabilities of DIC, leading to a deeper understanding of tendon biomechanics and improved strategies for preventing and treating tendon injuries. The continued commitment to open access principles will undoubtedly play a critical role in these advancements, ensuring that research findings are widely available and that the pace of innovation continues to accelerate.