2022.01.03 Our new CAVE-type display, an ActiveCube by Virtalis, is now fully installed and working in the Appenzeller Visualization Laboratory



Incoporating Uncertainy in the Visualization Process

Various different sources of uncertainty exist in the data capture and visualization process. These can result from the capture technology or noise during the data capture process. Within the data processing and visualization pipeline these uncertainties are carried forward and sometimes amplified. Hence, methods for properly handling these uncertainties and conveying uncertainties properly and accurately to the user within the visualization. The goal of this project is to develop proper techniques for handling these uncertainties and devising visualization approaches that allow the user to easily recognize any uncertainties present in the data. J21 J22 J23 J26 J27 P26 P34 P42

Analysis of Molecular Structures

In order to better understand molecular structures, better visualization and analysis tools are needed. When capturing the geometric properties of molecular structures, a variety of uncertainties are inherent in these geometric models. These uncertainties stem from the capturing methods but also physical properties of the individual atoms involved. Visualization methods that incorporate these levels of uncertainties are imperative for a proper analysis. J27

Enhanced Reconstruction Methods for 3D Scanning Technologies

3D capture technologies commonly used in the medical domain, such as CT or MRI, can sometimes suffer from inaccuracies resulting from the tissue they are designed to capture. For example, calcifications are known to be overestimated in size and mass in CT scans. More advanced techniques are needed to more accurately reconstruct the 3D data from the orginal signal. J25

Enhanced Nursing Training using Augmented Reality

Augmented and virtual reality can be a great tool to support various training methods as they can provide more insight and thus enhance the learning process resulting in faster learning and more in depth understanding. In this application, animated models of the lungs and heart are componied by the ribs and digestive tract. Different sounds can further enhance the augmented reality experience. Perliminary data has already shown that there is an improvement in the learning process when comparing students using the augmented reality versus students trainged using the traditional approach. P43 C42 I9

Augmented Reality support for Surgery

Augmented reality can be used to support surgeons to increase precision and thereby reduce the invasiveness of the surgery by using smaller incisions. This requires a direct view of internal structures, such as organs or bones, through the scin. Using augmented reality devices, such as the Microsoft Hololens or Magic Leap One, such a view can be provided while not restricting the surgeon and leaving boths hands free due to the fact that these devices are head-mounted and can be controlled by gestures and voice. The models used in the overlay are patient-specific based on a CT scan of that patient. Preliminary cadaver testing has shown very high accuracy in the overlay with a high degree of alignment discorverd between rib structures and the overlaid image after making the incision. C42 C43

Efficient Learning Through Virtual and Augmented Reality Techniques

Virtual environments are well-known for improving the learning experience in certain applications. If done properly, retention rate can be increased and the learning can become more effective by reducing the time it takes for someone to learn specific topics. At the same time, virtual and augmented reality technologies can provide a safer learning environment. Participating areas include Engineering, Computer Science, Nursing, Medical, Architecure, and Manufacturing. C41 P43

Interactive Analysis of Design Prototypes Using Virtual Reality

Using a virtual environment for testing different designs can provide various different advantages, such as reproducibility and repeatability. This project utilizes one of our VR environments to study different designs and compare them to each other by exposing different study participants to these designs. J20

Using Containter Technology for Data Anlytics and Visualization in HPC environments

High-performance computing systems can pose different challenges for data analysis based on the environment they are run in. For example, restricted environments with enhanced data security may not allow users to install any type of software themselves. A workaround can be to use container technologies. This project studies different container technologies with respect to their suitability for data analytics and visualization under those security restrictions.

Uncertainty-aware Medical Image Segmentation and Processing

Processing medical imagery can be a challenging task. Different artifacts typically present in volumetric scans, such as CT or MRI, can make selecting specific regions within the image difficult even for an expert. This project's goals are to develop image segmentation and processing algorithms that are aware of uncertainties present in the raw data and provide more intuitive algorithms to enable the domain experts to interactively process their data. P25, P26, J23 .

Visual Flow Tracking in Networks

To assist network analysts in their tasks, as part of this project an interactive visualization system to review simulated Software-defined networking/network (SDN) data. overview of the SDN hierarchy and the flow of its packets is presented. The system provides a visually guided flow tracking of selected packets through the SDN. Through a brushing and linking approach, the system forms an interactive analysis tool that is successfully applied to a simulated SDN dataset. P28

Large-scale Visualization For HPC Platforms

This project aims at implementing visualization algorithms directly on the HPC platform to better assist the researchers performaning large-scale simulations on those computing systems. To avoid download of these large data sets, which can be prohibitively costly, the visualizations are generated directly on the HPC system. The implementation takes advantage of the parallel compute capabilities available on the HPC platform to achieve better run-time performance. Current implementations include parallel coordinate plots but could be extended to other algorithms as well. J18

Visualization of Large-Scale Multidimensional Data

Multidimensional data can be challenging in terms of identifying a comprehensible, easy-to-interpret visualization. Data sources, such as general recognition theory, can generate millions of these multidimensional data sets that need to be visualized all at the same time. The sample image shows a parallel coordinate plot of such a data set. P19, C27

Virtual Environments

Virtual environments for presenting a specific model, such as an architectural design, or for repetative testing in which subjects need to be exposed to a specific scenario can be a valuable tool. In the latter case it is of upmost importance that the scenario is exactly identical for every subject. The different displays in the Appenzeller Visualization laboratory combined with the available software provide the perfect basis for these environments. C25

Reconstruction of Dragonfly Take-off

In order to reconstruct and study the flight characteristics of a dragonfly during take-off, the dragonfly can be captured using high-speed cameras from different angles to reconstruct the geometry of the body and wings. Using a flow simulation with this geometry as boundary condition the air flow around the wings can be computed and a suitable visualization reveals the properties that allow the dragonfly at take off. J12

Diffuse Coronary Artery Disease Detection

The general objective of this project is to develop a novel rationale for diagnosis of diffuse coronary artery disease (DCAD) using clinical non-invasive imaging of the coronary arteries. The indices of diagnosis will be validated in studies of an atherosclerotic porcine model with DCAD. Our unique algorithms for accurately extracting morphometric data from computerized tomography angiography (CTA) images of normal and disease patients along with our quantative approach uniquely position us to undertake this research. J5,J6

Early Lung Disease Detection Alliance

The Cleveland Clinic Foundation and its partners, Riverain Medical, Wright State University and University Hospitals Health System, have joined together to form the Early Lung Disease Detection Alliance (ELDDA), a multidisciplinary research and commercialization program that will develop, test (through clinical trials), and bring to market new image-analysis systems that permit the early detection of lung cancer and other lung diseases. This computer-aided detection (CAD) system will be applied to the most widely available and used imaging exam, the chest x-ray. The fight against lung cancer is waged on three major fronts: prevention, detection and treatment. The goal of this collaboration is to detect disease at an early stage (i.e. stage I for lung cancer), a necessary step to improve the treatment and survival of lung cancer patients and those at risk for lung cancer throughout Ohio. J11

Visualization of vascular structures

Cardiovascular diseases, such as atherosclerosis and coronary artery disease, are high risk factors for cardiac pain and death. We implemented a visualization software that enables interactive 3-D visualization of the cardiac vasculature retrieved using CT scanning technology, and an interactive flight through the vessel. Bifurcation angles and radii of the vessels can be measured while exploring the tree. Areas of high risk that could cause potential problems can be identified by this method. The project is conducted in collaboration with Dr. Ghassan Kassab's lab at the Department of Biomedical Engineering at the Indiana University Purdue University, who provided the data set. P11

Large-scale visualization of arterial trees

Current CT scanner allow the retrieval of vessel only up to a certain point due to the limited resolution. Recent techniques developed by Benjamin Kaimovitz et al. allow the extension of such scans down to the vessels at the capillary level, resulting in a model of the entire arterial vasculature. Of course, such a model is enormous in size challenging the visualization. We implemented a visualization software that is capable of handling a model with several GBs in size, exceeding the main memory of desktop computers. The software is highly optimized for tree shaped geometrical objects to achieve the best rendering performance possible. J3

3D Computer Games

Computer games are in a sense an example of virtual environments. In order to facilitate a fully immersive experience, we developed computer games that support quad-buffered stereo. Combined with, for example, 3D-capable displays and active shutter glasses, these games provide a truely 3D experience. Similarly, existing games and game engines can be ported to support such 3D capabilities, such as Cube 2. With Cube 2 being open source, we adapted its game engine to support 3D stereo. The adapted version can be downloaded, which includes Windows and Linux binaries, as well as the source code. C22

Tensor field visualization

The analysis and visualization of tensor fields is an advancing area in scientific visualization. Topology based methods that investigate the eigenvector fields of second order tensor fields have gained increasing interest in recent years. To complete the topological analysis, we developed an algorithm for detecting closed hyper-streamlines as an important topological feature. BC5

Vector field visualization (FAnToM)

FAnToM (Field Analysis using Topological Methods) is a software system that allows a user to explore vector fields by applying different analysis and visualization algorithms. Among other algorithms, it is capable of analyzing the topology of a 2-D or 3-D vector field, including complex structures, such as closed streamlines. This greatly helps a user to comprehend the structure of complex vector fields which could not be achieved by traditional visualization methods. P6