Multi-frame DVC for temporal image sequences - Manigrasso, Zaira; Goethals, Wannes; Goudarzi, Niloofar M.; Boone, Matthieu N.; Samaro, Aseel; Vervaet, Chris; Philips, Wilfried; Aelterman, Jan (Frontiers in Materials, 2022)
Digital Volume Correlation (DVC) is an image-based technique for displacement and strain computation. Traditionally, both (Digital Image Correlation) DIC and DVC are two methods based on 2 individual time frames: the estimation of the displacement and strain field is done using one reference and one moving image or volume as input. However, dynamic experiments generate more than two temporal frames. Therefore, with classical DVC techniques, only a subset of the available data is used. In this work, we propose a novel DVC method that can rely on more than two frames for the displacement and strain computation. The proposed method aims to be as general as possible: no constraint regarding the nature or the rate of the displacement has been imposed (e.g., cyclic or linear). The aim of this method is to impose a temporal regularization that improves the self-consistency of the algorithm. The multi-frame DVC improves the quality of the registration in challenging situations. As an example, we investigate the dissolution of a pharmaceutical tablet in water, which undergoes three processes: swelling, gel formation and material erosion. The accuracy of the registration - quantified by the sum of square differences (SSD) - has been improved by 23% on an average with respect to the classical two-frame method. Classical DVC methods fail in registering images with structures that change appearance trough time, such as the tablet that, in contact with water, reacts chemically changing phase and becoming gel. Moreover, we proved that multi-frame DVC is more robust in registering images with severe but realistic motion artefacts (De Schryver et al., 2018). As an example for this case, we apply the method to a series of μ-CT datasets of aluminum foam during compression experiment. As seen for the tablets, we are in a situation where the appearance of the structures in the images changes trough time, but in this case because of motion artefacts. Finally, the use of more than two frames makes the method more robust against noisy images, with an average improvement of 35% in registration accuracy obtained using 3-frame DVC method compared to the classical 2-frame DVC method.