An overview of reliable and representative DVC measurements for musculoskeletal tissues.

Abstract

Musculoskeletal tissues present complex hierarchical structures and mechanical heterogeneity across multiple length scales, making them difficult to characterise accurately. Digital volume correlation (DVC) is a non-destructive imaging technique that enables quantification of internal 3D strain fields under realistic loading conditions, offering a unique tool to investigate the biomechanics of biological tissues and biomaterials. This review highlights recent advancements in DVC, focusing on its applications across scales ranging from organ- to tissue-level mechanics in both mineralised and soft tissues. Instead of a traditional systematic review, we identify key technical challenges including the treatment of tissue interfaces, border effects, and the quantification of uncertainty in DVC outputs. Strategies for improving measurement accuracy and reliability are discussed. We also report on the increasing use of DVC in in vivo applications, its coupling with computational modelling to inform and validate biomechanical simulations, and its recent integration with data-driven methods such as deep learning to directly predict displacement and strain fields. Additionally, we examine its application in tissue engineering and implant-tissue interface assessment. By addressing such areas, we outline current limitations and emerging opportunities for future research. These include advancing precision, enabling clinical translation, and leveraging machine learning to create more robust, automated, and predictive DVC workflows for musculoskeletal health and tissue engineering.