(1) Background: Pulmonary arterial hypertension (PAH) is a serious condition that is associated with many cardiopulmonary diseases. Invasive right heart catheterization (RHC) is currently the only method for the definitive diagnosis and follow-up of PAH. In this study, we sought a non-invasive hemodynamic biomarker for the diagnosis of PAH. (2) Methods: We applied prospectively respiratory and cardiac gated 4D-flow MRI at a 9.4T preclinical scanner on three different groups of Sprague Dawley rats: baseline (n = 11), moderate PAH (n = 8), and severe PAH (n = 8). The pressure gradients as well as the velocity values were analyzed from 4D-flow data and correlated with lung histology. (3) Results: The pressure gradient between the pulmonary artery and vein on the unilateral side as well as the time-averaged mean velocity values of the small pulmonary arteries were capable of distinguishing not only between baseline and severe PAH, but also between the moderate and severe stages of the disease. (4) Conclusions: The current preclinical study suggests the pulmonary arteriovenous pressure gradient and the time-averaged mean velocity as potential biomarkers to diagnose PAH.

Simulation ensembles such as the ones simulating deep water asteroid impacts have many facets. Their analysis in terms of detecting spatio-temporal patterns, comparing multiple runs, and analyzing the influence of simulation parameters requires aggregation at multiple levels. We propose respective visual encodings embedded in an interactive visual analysis tool.

We present an interactive visual analysis tool of time-varying cosmological simulations comprising particle as well as multi-field information. Coordinated views support the joint analysis of particle distributions and reconstructed field values at multiple levels. Overview visualizations allow for the identification of features, combined filtering interactions for their detailed analysis.

Sensitivity analyses of simulation ensembles determine how simulation parameters influence the simulation's outcome. Commonly, one global numerical sensitivity value is computed per simulation parameter. However, when considering 3D spatial simulations, the analysis of localized sensitivities in different spatial regions is of importance in many applications. For analyzing the spatial variation of parameter sensitivity, one needs to compute a spatial sensitivity scalar field per simulation parameter. Given $n$ simulation parameters, we obtain multi-field data consisting of $n$ scalar fields when considering all simulation parameters. We propose an interactive visual analytics solution to analyze the multi-field sensitivity data. It supports the investigation of how strongly and in what way individual parameters influence the simulation outcome, in which spatial regions this is happening, and what the interplay of the simulation parameters is. Its central component is an overview visualization of all sensitivity fields that avoids 3D occlusions by linearizing the data using an adapted scheme of data-driven space-filling curves. The spatial sensitivity values are visualized in a combination of a Horizon Graph and a line chart. We validate our approach by applying it to synthetic and real-world ensemble data.

The analysis of multi-run oceanographic simulation data imposes various challenges ranging from visualizing multi-field spatio-temporal data over properly identifying and depicting vortices to visually representing uncertainties. We present an integrated interactive visual analysis tool that enables us to overcome these challenges by employing multiple coordinated views of different facets of the data at different levels of aggregation.

Hemodynamic analysis of blood flow is critical for diagnosing cardiovascular diseases and investigating cardiovascular parameters, such as aneurysms and wall shear stress. For subject-specific analyses, the anatomy and blood flow of the subject can be captured non-invasively using structural and 4D Magnetic Resonance Imaging (MRI), respectively. Computational fluid dynamics (CFD), on the other hand, can be used to generate blood flow simulations. To generate and analyze subject-specific blood flow simulations, MRI and CFD have to be brought together. We present an interactive, customizable, and user-oriented visual analysis tool that integrates measured data and CFD simulations. Thus, our open-source tool supports both medical and numerical analysis workflows. It enables the creation of simulation ensembles with a high variety of parameters. Furthermore, it allows for visual and analytical examination of simulations and measurements through 2D embeddings. To demonstrate the effectiveness of our tool, we applied it to three real-world use cases, showcasing its ability to configure simulation ensembles and analyze blood flow. We evaluated our example cases together with MRI and CFD experts. By combining the strengths of both CFD and MRI, our tool provides a comprehensive understanding of hemodynamic parameters, facilitating accurate analysis of hemodynamic biomarkers.