Kai Lawonn

Adaptive Surface Visualization of Vessels with Animated Blood Flow

The investigation of hemodynamic information for the assessment of cardiovascular diseases (CVDs) gained importance in recent years. Improved flow measuring modalities and computational fluid dynamics (CFD) simulations yield in reliable blood flow information. For a visual exploration of the flow information, domain experts are used to investigate the flow information combined with its enclosed vessel anatomy. Since the flow is spatially embedded in the surrounding vessel surface, occlusion problems have to be resolved. A visual reduction of the vessel surface that still provides important anatomical features is required. We accomplish this by applying an adaptive surface visualization inspired by the suggestive contour measure. Furthermore, an illustration is employed to highlight the animated pathlines and to emphasize nearby surface regions. Our approach combines several visualization techniques to improve the perception of surface shape and depth. Thereby, we ensure appropriate visibility of the embedded flow information, which can be depicted with established or advanced flow visualization techniques. We apply our approach to cerebral aneurysms and aortas with simulated and measured blood flow. An informal user feedback with nine domain experts, we confirm the advantages of our approach compared with existing methods, e.g. semi‐transparent surface rendering. Additionally, we assessed the applicability and usefulness of the pathline animation with highlighting nearby surface regions.

Streamlines for illustrative real-time rendering

Line drawing techniques are important methods to illustrate shapes. Existing feature line methods, e.g., suggestive contours, apparent ridges, or photic extremum lines, solely determine salient regions and illustrate them with separate lines. Hatching methods convey the shape by drawing a wealth of lines on the whole surface. Both approaches are often not sufficient for a faithful visualization of organic surface models, e.g., in biology or medicine. In this paper, we present a novel object‐space line drawing algorithm that conveys the shape of such surface models in real‐time. Our approach employs contour‐ and feature‐based illustrative streamlines to convey surface shape (ConFIS). For every triangle, precise streamlines are calculated on the surface with a given curvature vector field. Salient regions are detected by determining maxima and minima of a scalar field. Compared with existing feature lines and hatching methods, ConFIS uses the advantages of both categories in an effective and flexible manner. We demonstrate this with different anatomical and artificial surface models. In addition, we conducted a qualitative evaluation of our technique to compare our results with exemplary feature line and hatching methods.

Occlusion-free Blood Flow Animation with Wall Thickness Visualization

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.

Glyph-Based Comparative Visualization for Diffusion Tensor Fields

Diffusion Tensor Imaging (DTI) is a magnetic resonance imaging modality that enables the in-vivo reconstruction and visualization of fibrous structures. To inspect the local and individual diffusion tensors, glyph-based visualizations are commonly used since they are able to effectively convey full aspects of the diffusion tensor. For several applications it is necessary to compare tensor fields, e.g., to study the effects of acquisition parameters, or to investigate the influence of pathologies on white matter structures. This comparison is commonly done by extracting scalar information out of the tensor fields and then comparing these scalar fields, which leads to a loss of information. If the glyph representation is kept, simple juxtaposition or superposition can be used. However, neither facilitates the identification and interpretation of the differences between the tensor fields. Inspired by the checkerboard style visualization and the superquadric tensor glyph, we design a new glyph to locally visualize differences between two diffusion tensors by combining juxtaposition and explicit encoding. Because tensor scale, anisotropy type, and orientation are related to anatomical information relevant for DTI applications, we focus on visualizing tensor differences in these three aspects. As demonstrated in a user study, our new glyph design allows users to efficiently and effectively identify the tensor differences. We also apply our new glyphs to investigate the differences between DTI datasets of the human brain in two different contexts using different b-values, and to compare datasets from a healthy and HIV-infected subject.

Interactive Visual Analysis of Image-Centric Cohort Study Data

Epidemiological population studies impose information about a set of subjects (a cohort) to characterize disease-specific risk factors. Cohort studies comprise heterogenous variables describing the medical condition as well as demographic and lifestyle factors and, more recently, medical image data. We propose an Interactive Visual Analysis (IVA) approach that enables epidemiologists to rapidly investigate the entire data pool for hypothesis validation and generation. We incorporate image data, which involves shape-based object detection and the derivation of attributes describing the object shape. The concurrent investigation of image-based and non-image data is realized in a web-based multiple coordinated view system, comprising standard views from information visualization and epidemiological data representations such as pivot tables. The views are equipped with brushing facilities and augmented by 3D shape renderings of the segmented objects, e.g., each bar in a histogram is overlaid with a mean shape of the associated subgroup of the cohort. We integrate an overview visualization, clustering of variables and object shape for data-driven subgroup definition and statistical key figures for measuring the association between variables. We demonstrate the IVA approach by validating and generating hypotheses related to lower back pain as part of a qualitative evaluation.

Comparative Blood Flow Visualization for Cerebral Aneurysm Treatment Assessment

A pathological vessel dilation in the brain, termed cerebral aneurysm, bears a high risk of rupture, and is associated with a high mortality. In recent years, incidental findings of unruptured aneurysms have become more frequent, mainly due to advances in medical imaging. The pathological condition is often treated with a stent that diverts the blood flow from the aneurysm sac back to the original vessel. Prior to treatment, neuroradiologists need to decide on the optimal stent configuration and judge the long‐term rupture risk, for which blood flow information is essential. Modern patient‐specific simulations can model the hemodynamics for various stent configurations, providing important indicators to support the decision‐making process. However, the necessary visual analysis of these data becomes tedious and time‐consuming, because of the abundance of information. We introduce a comprehensive comparative visualization that integrates morphology with blood flow indicators to facilitate treatment assessment. To deal with the visual complexity, we propose a details‐on‐demand approach, combining established medical visualization techniques with innovative glyphs inspired by information visualization concepts. In an evaluation we have obtained informal feedback from domain experts, gauging the value of our visualization.

AmniVis - a system for qualitative exploration of near-wall hemodynamics in cerebral aneurysms

The qualitative exploration of near‐wall hemodynamics in cerebral aneurysms provides important insights for risk assessment. For instance, a direct relation between complex flow patterns and aneurysm formation could be observed. Due to the high complexity of the underlying time‐dependent flow data, the exploration is challenging, in particular for medical researchers not familiar with such data. We present the AmniVis‐Explorer, a system that is designed for the preparation of a qualitative medical study. The provided features were developed in close collaboration with medical researchers involved in the study. This comprises methods for a purposeful selection of surface regions of interest and a novel approach to provide a 2D overview of flow patterns that are represented by streamlines at these regions. Furthermore, we present a specialized interface that supports binary classification of patterns and temporal exploration as well as methods for selection, highlighting and automatic 3D navigation to particular patterns. Based on eight representative datasets, we conducted informal interviews with two bord‐certified radiologists and a flow expert to evaluate the system. It was confirmed that the AmniVis‐Explorer allows for an easy selection, qualitative exploration and classification of near‐wall flow patterns that are represented by streamlines.

Line integral convolution for real-time illustration of molecular surface shape and salient regions

We present a novel line drawing algorithm that illustrates surfaces in real‐time to convey their shape. We use line integral convolution (LIC) and employ ambient occlusion for illustrative surface rendering. Furthermore, our method depicts salient regions based on the illumination gradient. Our method works on animated surfaces in a frame‐coherent manner. Therefore, it yields an illustrative representation of time‐dependent surfaces as no preprocessing step is needed. In this paper, the method is used to highlight the structure of molecular surfaces and to illustrate important surface features like cavities, channels, and pockets. The benefit of our method was evaluated with domain experts. We also demonstrate the applicability of our method to medical visualization.

Combined Visualization of Wall Thickness and Wall Shear Stress for the Evaluation of Aneurysms

For an individual rupture risk assessment of aneurysms, the aneurysms 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.

Visualization and Analysis of Lumbar Spine Canal Variability in Cohort Study Data

AbstractLarge-scale longitudinal epidemiological studies, such as the Study of Health in Pomerania (SHIP), investigatethousands of individuals with common characteristics or experiences (a cohort) including a multitude of socio-demographic and biological factors. Unique for SHIP is the inclusion of medical image data acquired via anextensive whole-body MRI protocol. Based on this data, we study the variability of the lumbar spine and itsrelation to a subset of socio-demographic and biological factors. We focus on the shape of the lumbar spinal canalwhich plays a crucial role in understanding the causes of lower back pain.We propose an approach for the reproducible analysis of lumbar spine canal variability in a cohort. It is basedon the centerline of each individual canal, which is derived from a semi-automatic, model-based detection of thelumbar spine. The centerlines are clustered by means of Agglomerative Hierarchical Clustering to form groupswith low intra-group and high inter-group shape variability. The number of clusters is computed automatically.The clusters are visualized by means of representatives to reduce visual clutter and simplify a comparison betweensubgroups of the cohort. Special care is taken to convey the shape of the spinal canal also orthogonal to the viewplane. We demonstrate our approach for 490 individuals drawn from the SHIP data. We present preliminary resultsof investigating the clusters with respect to their associated socio-demographic and biological factors.Categories and Subject Descriptors