This book is the first one to focus on the intersection of visualization and X-ray computed tomography (XCT). While XCT, as a non-destructive imaging technique, generates detailed information about structures and characteristics of the specimens, proper visualization of the resulting data facilitates novel and previously impossible insights and yields an in-depth understanding of complex phenomena at multiple scales, in different dimensions, or different modalities. Major challenges emerge from the huge variety of scanned objects and dataset sizes that almost routinely reach the terabyte range; all this asks for a careful rethinking of methods on data handling and visualization.

Concepted at the intersection of the two almost disjoint domains of visualization and industrial XCT, this book compiles the state of the art and integrates some of the latest advancements from mathematical concepts to visual metaphors as well as respective algorithms and data structures. It discusses current applications and upcoming research streams in different stages of the visualization pipeline.

The book is structured into three main parts.

Fundamental Concepts: Fundamental definitions, concepts, and theory of X-ray imaging including different XCT modalities are addressed together with the foundations of data handling. Finally, the notion of "Rich XCT data" is introduced as a conceptional basis for the visualization tasks to follow.

The Visualization Pipeline: Besides standard visualization tasks like rendering, more advanced concepts such as multivariate data analysis, visual analysis of high dimensional data, imaging and tracking of dynamic phenomena, topology-driven approaches, and immersive analytics are discussed. Further contributions consider the visualization of quantitative data as well as uncertainty quantification and visualization. In addition, fundamental aspects of data processing, from reconstruction of XCT data, global and local (de-)compression to segmentation of relevant features are treated in detail.

Applications: Industrial applications in outstanding case studies of XCT visualization are presented, covering quantitative 3D to 4D materials science for aeronautic applications and quantification of tomographic images of fiber-reinforced composites.

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.