This book provides the first book at the intersection of visualization and X-ray computed tomography. Visualization in industrial X-ray computed tomography (XCT) has become highly attractive for research endeavors in various domains. While XCT as a non-destructive imaging technique generates detailed information of the specimens of interest regarding their structures and characteristics, the visualization of respective data facilitates novel, previously impossible insights. An in-depth understanding of complex phenomena at multiple scales, in different dimensions, or using different modalities may be facilitated. Major challenges are found in the huge variety of scanned objects and dataset sizes that almost routinely reach the terabyte range; all this requires for a careful rethinking of methods on data handling and visualization. This book, concepted at the intersection of the two almost orthogonal domains of visualization and industrial XCT, compiles the state of the art and integrates some of the latest advancements in variety of fields. It presents contributions from mathematical concepts to visual metaphors as well as respective algorithms and data structures. It discusses current applications as well as upcoming research streams in different aspects of the visualization pipeline. The structure of the book builds upon in three main parts:  Fundamental Concepts: XCT requires the discussion of definitions, concepts, and theory. In this part XCT modalities are discussed from attenuation to phase contrast, dark field imaging, and beyond as well as related thereto the current state in XCT technology. Regarding data the definition of "Rich XCT data" is introduced resembling the complexity of primary and secondary derived data, as well as ensembles thereof for the analysis of temporal evolutions. The Visualization Pipeline: Following an introduction of XCT data visualization, specific XCT visualization topics are addressed in the second part of the book. These cover multivariate data analysis, visual analysis of high dimensional data, imaging and tracking dynamic phenomena, topology-driven approaches, immersive analytics, visualization of quantitative data as well as uncertainty quantification and visualization. Furthermore, specific aspects of preprocessing are covered from reconstruction of XCT data, global and local (de-)compression as well as segmentation of relevant features. Applications: The third part demonstrates industrial applications in outstanding case studies of XCT visualization. These case studies cover quantitative 3D-4D materials science for aeronautic applications, quantifying tomographic images of fiber-reinforced composites or visual computing at large scientific facilities.

Christoph Heinzl received his Ph.D. degree in computer science from TU Wien, where he was also awarded the habilitation (venia docendi) in 2022. He is currently a professor for cognitive sensor systems at University of Passau and leading the research group for knowledge-based image processing and visualization at Fraunhofer IIS Development Center for X-ray Technology. His current research covers visualization and analysis of “rich” XCT data, a research domain, in which he published >100 papers, >36 of them peer-reviewed, four book chapters and a patent. He acquired various applied and basic research grants on national and European level. His research interests are focused but not limited to scientific visualization, visual analytics, visual parameter space analysis, visual analysis of spatio-temporal data, visual analysis of ensemble data, comparative visualization, multi modal data analysis and visualization, immersive analytics, cross-virtuality analytics, virtual and augmented reality in visualization, machine learning, uncertainty visualization.

Tomas Sauer earned a Ph.D. and habilitation in Mathematics at the University of Erlangen in 1993 and 1998, respectively. After being a professor (C3) for Applied Mathematics and Scientific Computing at the University of Giessen from 2000 to 2012, he holds the Chair for Mathematical Image Processing at the University of Passau since 2012 and is also the director of the research institute FORWISS there. In 2017 he founded the Fraunhofer research group for Knowledge-Based Image Processing in Passau which he directed until 2022, when he became chief scientist for the Fraunhofer IIS Development Center for X-ray Technology. His current research interests include sparse recovery and sparse representation especially of volumetric data sets as well as the mathematical aspects of computed tomography, but also the mathematical background of machine learning methods and its applications.

Norman Uhlmann received his Ph.D. degree in experimental physics from the University of Erlangen-Nürnberg in 2005 for his research in detector development for a Compton camera system. He continued his research as head of the research group “X-ray detectors and Monte Carlo simulation” at the Fraunhofer Development Center for X-ray Technology EZRT, at which he became head of the department “Application-specific Methods and Systems” in 2010. Since 2020, he is the division director of  Fraunhofer IIS Development Center for X-ray Technology. His current focus lies on the administration of the research at the EZRT, research of x-ray imaging, optical inspection and MR as well as being the main contact for industrial customers.