One of the biggest challenges in using ultrasound (US) is learning to create a spatial mental model of the interior of the scanned object based on the US image and the probe position. As intraoperative ultrasound (IOUS) cannot be easily trained on patients, we present LiVRSono, an immersive VR application to train this skill. The immersive environment, including an US simulation with patientspecific data as well as haptics to support hand-eye coordination, provides a realistic setting. Four clinically relevant training scenarios were identified based on the described learning goal and the workflow of IOUS for liver. The realism of the setting and the training scenarios were evaluated with eleven physicians, of which six participants are experts in IOUS for liver and five participants are potential users of the training system. The setting, handling of the US probe, and US image were considered realistic enough for the learning goal. Regarding the haptic feedback, a limitation is the restricted workspace of the input device. Three of the four training scenarios were rated as meaningful and effective. A pilot study regarding learning outcome shows positive results, especially with respect to confidence and perceived competence. Besides the drawbacks of the input device, our training system provides a realistic learning environment with meaningful scenarios to train the creation of a mental 3D model when performing IOUS. We also identified important improvements to the training scenarios to further enhance the training experience.

Abstract Simulations and measurements of blood and airflow inside the human circulatory and respiratory system play an increasingly important role in personalized medicine for prevention, diagnosis and treatment of diseases. This survey focuses on three main application areas. (1) Computational fluid dynamics (CFD) simulations of blood flow in cerebral aneurysms assist in predicting the outcome of this pathologic process and of therapeutic interventions. (2) CFD simulations of nasal airflow allow for investigating the effects of obstructions and deformities and provide therapy decision support. (3) 4D phase‐contrast (4D PC) magnetic resonance imaging of aortic haemodynamics supports the diagnosis of various vascular and valve pathologies as well as their treatment. An investigation of the complex and often dynamic simulation and measurement data requires the coupling of sophisticated visualization, interaction and data analysis techniques. In this paper, we survey the large body of work that has been conducted within this realm. We extend previous surveys by incorporating nasal airflow, addressing the joint investigation of blood flow and vessel wall properties and providing a more fine‐granular taxonomy of the existing techniques. From the survey, we extract major research trends and identify open problems and future challenges. The survey is intended for researchers interested in medical flow but also more general, in the combined visualization of physiology and anatomy, the extraction of features from flow field data and feature‐based visualization, the visual comparison of different simulation results and the interactive visual analysis of the flow field and derived characteristics.

Intracranial aneurysms are pathologic dilations of the vessel wall, which bear the risk of rupture and of fatal consequences for the patient. Since treatment may be accompanied by severe complications as well, rupture risk assessment and thus rupture risk prediction plays an important role in clinical research. In this work, we investigate the potential of morphological features for rupture risk status classification in 100 intracranial aneurysms. We propose a pipeline for morphological feature extraction and rupture status classification with subsequent feature ranking and inspection. Our classification setup involves training separate models for each aneurysm type (sidewall or bifurcation) with multiple learning algorithms. We report on the classification performance of our pipeline and examine the predictive power of each morphological parameter towards rupture status classification. Further, we identify the most important features for the best models and study their marginal prediction.

Abstract Rupture risk assessment is a key to devise patient‐specific treatment plans of cerebral aneurysms. To understand and predict the development of aneurysms and other vascular diseases over time, both hemodynamic flow patterns and their effect on the vessel surface need to be analyzed. Flow structures close to the vessel wall often correlate directly with local changes in surface parameters, such as pressure or wall shear stress. Yet, in many existing applications, the analyses of flow and surface features are either somewhat detached from one another or only globally available. Especially for the identification of specific blood flow characteristics that cause local startling parameters on the vessel surface, like elevated pressure values, an interactive analysis tool is missing. The explorative visualization of flow data is challenging due to the complexity of the underlying data. In order to find meaningful structures in the entirety of the flow, the data has to be filtered based on the respective explorative aim. In this paper, we present a combination of visualization, filtering and interaction techniques for explorative analysis of blood flow with a focus on the relation of local surface parameters and underlying flow structures. Coherent bundles of pathlines can be interactively selected based on their relation to features of the vessel wall and further refined based on their own hemodynamic features. This allows the user to interactively select and explore flow structures locally affecting a certain region on the vessel wall and therefore to understand the cause and effect relationship between these entities. Additionally, multiple selected flow structures can be compared with respect to their quantitative parameters, such as flow speed. We confirmed the usefulness of our approach by conducting an informal interview with two expert neuroradiologists and an expert in flow simulation. In addition, we recorded several insights the neuroradiologists were able to gain with the help of our tool.

Abstract Medical visualizations are highly adapted to a specific medical application scenario. Therefore, many researchers conduct qualitative evaluations with a low number of physicians or medical experts to assess the benefits of their visualization technique. Although this type of research has advantages, it is difficult to reproduce and can be subjectively biased. This makes it problematic to quantify the benefits of a new visualization technique. Quantitative evaluation can objectify research and help bringing new visualization techniques into clinical practice. To support researchers, we present guidelines to quantitatively evaluate medical visualizations, considering specific characteristics and difficulties. We demonstrate the adaptation of these guidelines on the example of comparative aneurysm surface visualizations. We developed three visualization techniques to compare aneurysm volumes. The visualization techniques depict two similar, but not identical aneurysm surface meshes. In a user study with 34 participants and five aneurysm data sets, we assessed objective measures (accuracy and required time) and subjective ratings (suitability and likeability). The provided guidelines and presentation of different stages of the evaluation allow for an easy adaptation to other application areas of medical visualization.

Abstract Intravascular imaging provides new insights into the condition of vessel walls. This is crucial for cerebrovascular diseases including stroke and cerebral aneurysms, where it may present an important factor for indication of therapy. In this work, we provide new information of cerebral artery walls by combining ex vivo optical coherence tomography (OCT) imaging with histology data sets. To overcome the obstacles of deflated and collapsed vessels due to the missing blood pressure, the lack of co‐alignment as well as the geometrical shape deformations due to catheter probing, we developed the new image processing method virtual inflation. We locally sample the vessel wall thickness based on the (deflated) vessel lumen border instead of the vessel's centerline. Our method is embedded in a multi‐view framework where correspondences between OCT and histology can be highlighted via brushing and linking yielding OCT signal characteristics of the cerebral artery wall and its pathologies. Finally, we enrich the data views with a hierarchical clustering representation which is linked via virtual inflation and further supports the deduction of vessel wall pathologies.

Epidemiological studies comprise heterogeneous data about a subject group to define disease-specific risk factors. These data contain information (features) about a subject's lifestyle, medical status as well as medical image data. Statistical regression analysis is used to evaluate these features and to identify feature combinations indicating a disease (the target feature). We propose an analysis approach of epidemiological data sets by incorporating all features in an exhaustive regression-based analysis. This approach combines all independent features w.r.t. a target feature. It provides a visualization that reveals insights into the data by highlighting relationships. The 3D Regression Heat Map, a novel 3D visual encoding, acts as an overview of the whole data set. It shows all combinations of two to three independent features with a specific target disease. Slicing through the 3D Regression Heat Map allows for the detailed analysis of the underlying relationships. Expert knowledge about disease-specific hypotheses can be included into the analysis by adjusting the regression model formulas. Furthermore, the influences of features can be assessed using a difference view comparing different calculation results. We applied our 3D Regression Heat Map method to a hepatic steatosis data set to reproduce results from a data mining-driven analysis. A qualitative analysis was conducted on a breast density data set. We were able to derive new hypotheses about relations between breast density and breast lesions with breast cancer. With the 3D Regression Heat Map, we present a visual overview of epidemiological data that allows for the first time an interactive regression-based analysis of large feature sets with respect to a disease.

We present the first visualization tool that combines pathlines from blood flow and wall thickness information. Our method uses illustrative techniques to provide occlusion-free visualization of the flow. We thus offer medical researchers an effective visual analysis tool for aneurysm treatment risk assessment. Such aneurysms bear a high risk of rupture and significant treatment-related risks. Therefore, to get a fully informed decision it is essential to both investigate the vessel morphology and the hemodynamic data. Ongoing research emphasizes the importance of analyzing the wall thickness in risk assessment. Our combination of blood flow visualization and wall thickness representation is a significant improvement for the exploration and analysis of aneurysms. As all presented information is spatially intertwined, occlusion problems occur. We solve these occlusion problems by dynamic cutaway surfaces. We combine this approach with a glyph-based blood flow representation and a visual mapping of wall thickness onto the vessel surface. We developed a GPU-based implementation of our visualizations which facilitates wall thickness analysis through real-time rendering and flexible interactive data exploration mechanisms. We designed our techniques in collaboration with domain experts, and we provide details about the evaluation of the technique and tool.

For an individual rupture risk assessment of aneurysms, the aneurysm's wall morphology and hemodynamics provide valuable information. Hemodynamic information is usually extracted via computational fluid dynamic (CFD) simulation on a previously extracted 3D aneurysm surface mesh or directly measured with 4D phase-contrast magnetic resonance imaging. In contrast, a noninvasive imaging technique that depicts the aneurysm wall in vivo is still not available. Our approach comprises an experiment, where intravascular ultrasound (IVUS) is employed to probe a dissected saccular aneurysm phantom, which we modeled from a porcine kidney artery. Then, we extracted a 3D surface mesh to gain the vessel wall thickness and hemodynamic information from a CFD simulation. Building on this, we developed a framework that depicts the inner and outer aneurysm wall with dedicated information about local thickness via distance ribbons. For both walls, a shading is adapted such that the inner wall as well as its distance to the outer wall is always perceivable. The exploration of the wall is further improved by combining it with hemodynamic information from the CFD simulation. Hence, the visual analysis comprises a brushing and linking concept for individual highlighting of pathologic areas. Also, a surface clustering is integrated to provide an automatic division of different aneurysm parts combined with a risk score depending on wall thickness and hemodynamic information. In general, our approach can be employed for vessel visualization purposes where an inner and outer wall has to be adequately represented.

We investigate the task of breast tumor classification based on dynamic contrast-enhanced magnetic resonance image data (DCE-MRI). Our objective is to study how the formation of regions of similar voxels contributes to distinguishing between benign and malignant tumors. First, we perform clustering on each tumor with different algorithms and parameter settings, and then combine the clustering results to identify the most suspect region of the tumor and derive features from it. With these features we train classifiers on a set of tumors that are difficult to classify, even for human experts. We show that the features of the most suspect region alone cannot distinguish between benign and malignant tumors, yet the properties of this region are indicative of tumor malignancy for the dataset we studied.

Abstract Cardiovascular imaging with current multislice spiral computed tomography (MSCT) technology enables a non‐invasive evaluation of the coronary arteries. Contrast‐enhanced MSCT angiography with high spatial resolution allows for a segmentation of the coronary artery tree. We present an automatically adapted transfer function (TF) specification to highlight pathologic changes of the vessel wall based on the segmentation result of the coronary artery tree. The TFs are combined with common visualization techniques, such as multiplanar reformation and direct volume rendering for the evaluation of coronary arteries in MSCT image data. The presented TF‐based mapping of CT values in Hounsfield Units (HU) to color and opacity leads to a different color coding for different plaque types. To account for varying HU values of the vessel lumen caused by the contrast medium, the TFs are adapted to each dataset by local histogram analysis. We describe an informal evaluation with three board‐certified radiologists which indicates that the represented visualizations guide the user's attention to pathologic changes of the vessel wall as well as provide an overview about spatial variations.