Optimising digital volume correlation across materials: a practical framework for accuracy and spatial resolution

Digital volume correlation (DVC) provides full-field three-dimensional measurements of internal deformation, but its accuracy and effective spatial resolution depend on image-processing and analysis choices. Here, we use fibrous, cartilaginous and mineralised tissues within a rat intervertebral disc (IVD) as a controlled multi-material case study, combining virtual compression with experimentally loaded synchrotron computed tomography images. We systematically evaluate phase retrieval and image filtering, bit-depth conversion, point-cloud design, subvolume size and strain-field smoothing. Stronger phase retrieval reduced correlation residuals while increasing displacement and strain errors, showing that residual minimisation alone can select poorer parameters. Image filtering sensitivity was greatest in fibrous tissue, which had the smallest characteristic image feature size, whereas inappropriate intensity mapping during 16-bit to 8-bit conversion preferentially degraded low-contrast cartilage. Increasing subvolume size, point spacing or strain-window size improved measurement robustness but progressively smoothed local strain heterogeneity. We demonstrate that the spatial resolution of strain measurement must be matched across tissue types in order to compare strain magnitude; in the IVD, matching DVC spatial resolution changed the apparent ratio of compressive strain among fibrous, cartilaginous and mineralised tissues from 3.1:1.9:1 to 14:7.7:1. These findings establish a sequential, deformation-based optimisation framework in which image characteristics guide processing, known deformations validate accuracy and strain fields are compared at matched measurement scales. The framework supports more reproducible and mechanically interpretable DVC analyses in heterogeneous biological and engineered materials.