X-ray Computed Tomography (XRCT) serves as a vital tool across numerous research disciplines for comprehending the internal structures of objects. Its utility has expanded to encompass the analysis of temporal changes, facilitated through repeated XRCT scans or in-situ experiments. Synchrotron facilities offer the advantage of conducting scans with exceptionally high spatial and temporal resolution, resulting in the rapid accumulation of vast datasets. In this thesis, we delved into methodologies geared towards expediting insights into extensive time-dependent XRCT datasets, without necessitating the time consuming process of full volume reconstruction. We introduce the temporal difference map, a novel approach providing a comprehensive overview of entire time-dependent XRCT datasets. Additionally, we propose a automated change detection method aimed at discerning pertinent data during in-situ experiments. To facilitate the exploration of our proposed methods, we developed an interactive data exploration tool that seamlessly integrates multiple change visualizations. Our evaluation encompasses multiple case studies, including porous media research at synchrotron facilities, laboratory-based porous media research, and the study of deforming objects. Furthermore, we generated an artificial dataset to assess the performance and limitations of our change detection method. Our findings underscore the efficacy of the temporal difference map in offering a coherent overview of developments within various XRCT datasets. Additionally our automatic change detection method performed well across our diverse case studies without requiring manual parameter adjustments.