In material sciences, the analysis of a material’s structure and its deformation behavior are essential. High-resolution X-ray tomography (μCT) and particularly the imaging of dynamic processes using 4D-μCT are powerful, non-destructive tools to perform this analysis. However, full analysis requires processing of the 3D data associated with the internal motion of a material sample through time, which is called digital volume correlation (DVC). Compared to 2D (surface material) analysis called DIC, DVC is lagging behind in capabilities because DVC is simultaneously a more challenging problem from an estimation accuracy point of view as well as computationally more challenging due to the dimensionality increase going from 2D to 3D datasets. The aim of this project is to close the gap between DIC (2D) and DVC (3D) capabilities by tackling the aforementioned combination of problems. For solving this combination of problems, we envision a hardware-software co-design solution, where we will leverage the capabilities of an innovative continuous-acquisition μCT system with novel DVC algorithms that incorporate robust estimators and multi-frame DVC estimation. Including these algorithms in a framework for CT reconstruction will significantly improve the potential of 4D-μCT. Both innovations will synergize to open up the possibility of performing high-accuracy DVC in the presence of challenging samples that exhibit complex motion behavior (e.g. due to sudden buckling or cracking).