The comparison of many members of an ensemble is difficult, tedious, and error-prone, which is aggravated by often just subtle differences. In this paper, we introduce Dynamic Volume Lines for the interactive visual analysis and comparison of sets of 3D volumes. Each volume is linearized along a Hilbert space-filling curve into a 1D Hilbert line plot, which depicts the intensities over the Hilbert indices. We present a nonlinear scaling of these 1D Hilbert line plots based on the intensity variations in the ensemble of 3D volumes, which enables a more effective use of the available screen space. The nonlinear scaling builds the basis for our interactive visualization techniques. An interactive histogram heatmap of the intensity frequencies serves as overview visualization. When zooming in, the frequencies are replaced by detailed 1D Hilbert line plots and optional functional boxplots. To focus on important regions of the volume ensemble, nonlinear scaling is incorporated into the plots. An interactive scaling widget depicts the local ensemble variations. Our brushing and linking interface reveals, for example, regions with a high ensemble variation by showing the affected voxels in a 3D spatial view. We show the applicability of our concepts using two case studies on ensembles of 3D volumes resulting from tomographic reconstruction. In the first case study, we evaluate an artificial specimen from simulated industrial 3D X-ray computed tomography (XCT). In the second case study, a real-world XCT foam specimen is investigated. Our results show that Dynamic Volume Lines can identify regions with high local intensity variations, allowing the user to draw conclusions, for example, about the choice of reconstruction parameters. Furthermore, it is possible to detect ring artifacts in reconstructions volumes.

We present GEMSe, an interactive tool for exploring and analyzing the parameter space of multi-channel segmentation algorithms. Our targeted user group are domain experts who are not necessarily segmentation specialists. GEMSe allows the exploration of the space of possible parameter combinations for a segmentation framework and its ensemble of results. Users start with sampling the parameter space and computing the corresponding segmentations. A hierarchically clustered image tree provides an overview of variations in the resulting space of label images. Details are provided through exemplary images from the selected cluster and histograms visualizing the parameters and the derived output in the selected cluster. The correlation between parameters and derived output as well as the effect of parameter changes can be explored through interactive filtering and scatter plots. We evaluate the usefulness of GEMSe through expert reviews and case studies based on three different kinds of datasets: A synthetic dataset emulating the combination of 3D X-ray computed tomography with data from K-Edge spectroscopy, a three-channel scan of a rock crystal acquired by a Talbot-Lau grating interferometer X-ray computed tomography device, as well as a hyperspectral image.

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Cross‐virtuality analytics (XVA) is a novel field of research within immersive analytics and visual analytics. A broad range of heterogeneous devices across the reality–virtuality continuum, along with respective visual metaphors and analysis techniques, are currently becoming available. The goal of XVA is to enable visual analytics that use transitional and collaborative interfaces to seamlessly integrate different devices and support multiple users. In this work, we take a closer look at XVA and analyse the existing body of work for an overview of its current state. We classify the related literature regarding ways of establishing cross‐virtuality by interconnecting different stages in the reality–virtuality continuum, as well as techniques for transitioning and collaborating between the different stages. We provide insights into visualization and interaction techniques employed in current XVA systems. We report on ways of evaluating such systems, and analyse the domains where such systems are becoming available. Finally, we discuss open challenges in XVA, giving directions for future research.

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This paper addresses the increasing demand in industry for methods to analyze and visualize multimodal data involving a spectral modality. Two data modalities are used: high‐resolution X‐ray computed tomography (XCT) for structural characterization and low‐resolution X‐ray fluorescence (XRF) spectral data for elemental decomposition. We present InSpectr, an integrated tool for the interactive exploration and visual analysis of multimodal, multiscalar data. The tool has been designed around a set of tasks identified by domain experts in the fields of XCT and XRF. It supports registered single scalar and spectral datasets optionally coupled with element maps and reference spectra. InSpectr is instantiating various linked views for the integration of spatial and non‐spatial information to provide insight into an industrial component's structural and material composition: views with volume renderings of composite and individual 3D element maps visualize global material composition; transfer functions defined directly on the spectral data and overlaid pie‐chart glyphs show elemental composition in 2D slice‐views; a representative aggregated spectrum and spectra density histograms are introduced to provide a global overview in the spectral view. Spectral magic lenses, spectrum probing and elemental composition probing of points using a pie‐chart view and a periodic table view aid the local material composition analysis. Two datasets are investigated to outline the usefulness of the presented techniques: a 3D virtually created phantom with a brass metal alloy and a real‐world 2D water phantom with insertions of gold, barium, and gadolinium. Additionally a detailed user evaluation of the results is provided.

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X-ray computed tomography (XCT) is one of the most powerful imaging techniques in non-destructive testing (NDT) for detecting, analysing and visualising defects such as pores, fibres, cracks etc. in industrial specimens. Detecting defects in X-ray images, however, is still a challenging problem, as it strongly depends on the quality of the XCT images. Numerical XCT simulation proved to be valuable in order to increase both image quality and detection performance. In this work, we thus analyse the differences between traditional segmentation techniques (i.e., k-means, watershed, Otsu thresholding) and deep learning-based methods (i.e., U-Net, V-Net, modified 3D U-Net) in terms of their defect detection capacity using virtual XCT images. For this purpose, we apply the probability of defect detection (POD) approach on simulated X-ray computed tomography data from aluminium cylinder heads. The XCT simulation tool SimCT was used to generate X-ray radiographs and respective reconstructions from a specimen series which features different well-defined defects with varying sizes, shapes and locations. To generate POD curves and to specify detection limits, the segmentation algorithms are used in predefined regions for defect detection via a hit/miss approach. A comparison and visualisation of six different types of defects is illustrated in 2D and 3D images, together with their POD curves and detection limits.