Search Results

Now showing 1 - 10 of 99
  • Item
    Anatomy-Guided Multi-Level Exploration of Blood Flow in Cerebral Aneurysms
    (The Eurographics Association and Blackwell Publishing Ltd., 2011) Neugebauer, Mathias; Janiga, Gabor; Beuing, Oliver; Skalej, Martin; Preim, Bernhard; H. Hauser, H. Pfister, and J. J. van Wijk
    For cerebral aneurysms, the ostium, the area of inflow, is an important anatomic landmark, since it separates the pathological vessel deformation from the healthy parent vessel. A better understanding of the inflow characteristics, the flow inside the aneurysm and the overall change of pre- and post-aneurysm flow in the parent vessel provide insights for medical research and the development of new risk-reduced treatment options. We present an approach for a qualitative, visual flow exploration that incorporates the ostium and derived anatomical landmarks. It is divided into three scopes: a global scope for exploration of the in- and outflow, an ostium scope that provides characteristics of the flow profile close to the ostium and a local scope for a detailed exploration of the flow in the parent vessel and the aneurysm. The approach was applied to five representative datasets, including measured and simulated blood flow. Informal interviews with two board-certified radiologists confirmed the usefulness of the provided exploration tools and delivered input for the integration of the ostium-based flow analysis into the overall exploration workflow.
  • Item
    Adapted Surface Visualization of Cerebral Aneurysms with Embedded Blood Flow Information
    (The Eurographics Association, 2010) Gasteiger, Rocco; Neugebauer, Mathias; Kubisch, Christoph; Preim, Bernhard; Dirk Bartz and Charl Botha and Joachim Hornegger and Raghu Machiraju and Alexander Wiebel and Bernhard Preim
    Cerebral aneurysms are a vascular dilatation induced by a pathological change of the vessel wall and often require treatment to avoid rupture. Therefore, it is of main interest, to estimate the risk of rupture, to gain a deeper understanding of aneurysm genesis, and to plan an actual intervention, the surface morphology and the internal blood flow characteristics. Visual exploration is primarily used to understand such complex and variable type of data. Since the blood flow data is strongly influenced by the surrounding vessel morphology both have to be visually combined to efficiently support visual exploration. Since the flow is spatially embedded in the surrounding aneurysm surface, occlusion problems have to be tackled. Thereby, a meaningful visual reduction of the aneurysm surface that still provides morphological hints is necessary. We accomplish this by applying an adapted illustrative rendering style to the aneurysm surface. Our contribution lies in the combination and adaption of several rendering styles, which allow us to reduce the problem of occlusion and avoid most of the disadvantages of the traditional semi-transparent surface rendering, like ambiguities in perception of spatial relationships. In interviews with domain experts, we derived visual requirements. Later, we conducted an initial survey with 40 participants (13 medical experts of them), which leads to further improvements of our approach.
  • Item
    Effective Visual Exploration of Hemodynamics in Cerebral Aneurysms
    (The Eurographics Association, 2013) Neugebauer, Mathias; Gasteiger, Rocco; Janiga, Gábor; Beuing, Oliver; Preim, Bernhard; Hans-Christian Hege and Anna Vilanova
    Cerebral aneurysms are a pathological vessel dilatation that bear a high risk of rupture. For the understanding of this risk, the analysis of hemodynamic information plays an important role in clinical research. These information are obtained by computational fluid dynamics (CFD) simulations. Thus, an effective visual exploration of patient-specific blood flow behavior in cerebral aneurysms was developed to support the domain experts in their investigation process. We present advanced visualization and interaction techniques, which provide an overview, focus-and-context views as well as multi-level explorations. Moreover, an automatic extraction process of qualitative flow characteristics, which are correlated with the risk of rupture is introduced. Although not established in clinical routine yet, interviews and informal user studies confirm the usefulness of these methods.
  • Item
    Semi-Automatic Vessel Boundary Detection in Cardiac 4D PC-MRI Data Using FTLE fields
    (The Eurographics Association, 2016) Behrendt, Benjamin; Köhler, Benjamin; Gräfe, Daniel; Grothoff, Matthias; Gutberlet, Matthias; Preim, Bernhard; Stefan Bruckner and Bernhard Preim and Anna Vilanova and Helwig Hauser and Anja Hennemuth and Arvid Lundervold
    Four-dimensional phase-contrast magnetic resonance imaging (4D PC-MRI) is a method to non-invasively acquire in-vivo blood flow, e.g. in the aorta. It produces three-dimensional, time-resolved datasets containing both flow speed and direction for each voxel. In order to perform qualitative and quantitative data analysis on these datasets, a vessel segmentation is often required. These segmentations are mostly performed manually or semi-automatically, based on three-dimensional intensity images containing the maximal flow speed over all time steps. To allow for a faster segmentation, we propose a method that, in addition to intensity, incorporates the flow trajectories into the segmentation process. This is accomplished by extracting Lagrangian Coherent Structures (LCS) from the flow data, which indicate physical boundaries in a dynamical system. To approximate LCS in our discrete images, we employ Finite Time Lyapunov Exponent (FTLE) fields to quantify the rate of separation of neighboring flow trajectories. LCS appear as ridges or valleys in FTLE images, indicating the presence of either a flow structure boundary or physical boundary. We will show that the process of segmenting low-contrast 4D PC-MRI datasets can be simplified by using the generated FLTE data in combination with intensity images.
  • Item
    Guided Analysis of Cardiac 4D PC-MRI Blood Flow Data
    (The Eurographics Association, 2015) Köhler, Benjamin; Preim, Uta; Grothoff, Matthias; Gutberlet, Matthias; Fischbach, Katharina; Preim, Bernhard; H.-C. Hege and T. Ropinski
    Four-dimensional phase-contrast magnetic resonance imaging (4D PC-MRI) allows the non-invasive acquisition of temporally resolved, three-dimensional blood flow information. Quantitative and qualitative data analysis helps to assess the cardiac function, severity of diseases and find indications of different cardiovascular pathologies. However, various steps are necessary to achieve expressive visualizations and reliable results. This comprises the correction of special MR-related artifacts, the segmentation of vessels, flow integration with feature extraction and the robust quantification of clinically important measures. A fast and easy-to-use processing pipeline is essential since the target user group are physicians. We present a system that offers such a guided workflow for cardiac 4D PC-MRI data. The aorta and pulmonary artery can be analyzed within ten minutes including vortex extraction and robust determination of the stroke volume as well as the percentaged backflow. 64 datasets of healthy volunteers and of patients with variable diseases such as aneurysms, coarctations and insufficiencies were processed so far.
  • Item
    A Survey of Visual Analytics for Public Health
    (© 2020 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2020) Preim, Bernhard; Lawonn, Kai; Benes, Bedrich and Hauser, Helwig
    We describe visual analytics solutions aiming to support public health professionals, and thus, preventive measures. Prevention aims at advocating behaviour and policy changes likely to improve human health. Public health strives to limit the outbreak of acute diseases as well as the reduction of chronic diseases and injuries. For this purpose, data are collected to identify trends in human health, to derive hypotheses, e.g. related to risk factors, and to get insights in the data and the underlying phenomena. Most public health data have a temporal character. Moreover, the spatial character, e.g. spatial clustering of diseases, needs to be considered for decision‐making. Visual analytics techniques involve (subspace) clustering, interaction techniques to identify relevant subpopulations, e.g. being particularly vulnerable to diseases, imputation of missing values, visual queries as well as visualization and interaction techniques for spatio‐temporal data. We describe requirements, tasks and visual analytics techniques that are widely used in public health before going into detail with respect to applications. These include outbreak surveillance and epidemiology research, e.g. cancer epidemiology. We classify the solutions based on the visual analytics techniques employed. We also discuss gaps in the current state of the art and resulting research opportunities in a research agenda to advance visual analytics support in public health.
  • Item
    Implicit Modeling of Patient-Specific Aortic Dissections with Elliptic Fourier Descriptors
    (The Eurographics Association and John Wiley & Sons Ltd., 2021) Mistelbauer, Gabriel; Rössl, Christian; Bäumler, Kathrin; Preim, Bernhard; Fleischmann, Dominik; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
    Aortic dissection is a life-threatening vascular disease characterized by abrupt formation of a new flow channel (false lumen) within the aortic wall. Survivors of the acute phase remain at high risk for late complications, such as aneurysm formation, rupture, and death. Morphologic features of aortic dissection determine not only treatment strategies in the acute phase (surgical vs. endovascular vs. medical), but also modulate the hemodynamics in the false lumen, ultimately responsible for late complications. Accurate description of the true and false lumen, any communications across the dissection membrane separating the two lumina, and blood supply from each lumen to aortic branch vessels is critical for risk prediction. Patient-specific surface representations are also a prerequisite for hemodynamic simulations, but currently require time-consuming manual segmentation of CT data. We present an aortic dissection cross-sectional model that captures the varying aortic anatomy, allowing for reliable measurements and creation of high-quality surface representations. In contrast to the traditional spline-based cross-sectional model, we employ elliptic Fourier descriptors, which allows users to control the accuracy of the cross-sectional contour of a flow channel. We demonstrate (i) how our approach can solve the requirements for generating surface and wall representations of the flow channels, (ii) how any number of communications between flow channels can be specified in a consistent manner, and (iii) how well branches connected to the respective flow channels are handled. Finally, we discuss how our approach is a step forward to an automated generation of surface models for aortic dissections from raw 3D imaging segmentation masks.
  • Item
    A Geometric Optimization Approach for the Detection and Segmentation of Multiple Aneurysms
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Lawonn, Kai; Meuschke, Monique; Wickenhöfer, Ralph; Preim, Bernhard; Hildebrandt, Klaus; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    We present a method for detecting and segmenting aneurysms in blood vessels that facilitates the assessment of risks associated with the aneurysms. The detection and analysis of aneurysms is important for medical diagnosis as aneurysms bear the risk of rupture with fatal consequences for the patient. For risk assessment and treatment planning, morphological descriptors, such as the height and width of the aneurysm, are used. Our system enables the fast detection, segmentation and analysis of single and multiple aneurysms. The method proceeds in two stages plus an optional third stage in which the user interacts with the system. First, a set of aneurysm candidate regions is created by segmenting regions of the vessels. Second, the aneurysms are detected by a classification of the candidates. The third stage allows users to adjust and correct the result of the previous stages using a brushing interface. When the segmentation of the aneurysm is complete, the corresponding ostium curves and morphological descriptors are computed and a report including the results of the analysis and renderings of the aneurysms is generated. The novelty of our approach lies in combining an analytic characterization of aneurysms and vessels to generate a list of candidate regions with a classifier trained on data to identify the aneurysms in the candidate list. The candidate generation is modeled as a global combinatorial optimization problem that is based on a local geometric characterization of aneurysms and vessels and can be efficiently solved using a graph cut algorithm. For the aneurysm classification scheme, we identified four suitable features and modeled appropriate training data. An important aspect of our approach is that the resulting system is fast enough to allow for user interaction with the global optimization by specifying additional constraints via a brushing interface.
  • Item
    Visual Analytics to Support Treatment Decisions in Late-Stage Melanoma Patients
    (The Eurographics Association, 2023) Pereira, Calida; Niemann, Uli; Braun, Andreas; Mengoni, Miriam; Tüting, Thomas; Preim, Bernhard; Meuschke, Monique; Hansen, Christian; Procter, James; Renata G. Raidou; Jönsson, Daniel; Höllt, Thomas
    We present a visual analytics system to support treatment decisions in late-stage Melanoma patients. With the aim of improving patient outcomes, personalized treatment decisions based on individual characteristics and medical histories are crucial. The research focuses on the design and development of a visual analytics system tailored specifically for tumor boards, where multidisciplinary teams collaborate to make informed decisions. By leveraging a comprehensive database containing treatment and tumor stage progression information from over 1100 patients, the system provides healthcare professionals with a holistic overview and facilitates the analysis of individual cases as well as comparisons between multiple patients. The distinction between tumor board preparation systems and systems used during discussions is emphasized to ensure user-centric design and usability. Through the use of visual analytics techniques, complex relationships between treatment outcomes, temporal features, and patient-specific factors are explored, enabling clinicians to identify patterns and trends that may impact treatment decisions. The findings of this research contribute to the growing field of visual analytics in healthcare and have the potential to enhance treatment decision-making and patient care in late-stage cancer scenarios.
  • Item
    A Visual Analytics Approach for Patient Stratification and Biomarker Discovery
    (The Eurographics Association, 2019) Alemzadeh, Shiva; Kromp, Florian; Preim, Bernhard; Taschner-Mandl, Sabine; Bühler, Katja; Kozlíková, Barbora and Linsen, Lars and Vázquez, Pere-Pau and Lawonn, Kai and Raidou, Renata Georgia
    We introduce discoVA as a visual analytics tool for the refinement of risk stratification of cancer patients and biomarker discovery. Currently, tools for the joint analysis of multiple biological and clinical information in this field are insufficient or lacking. Our tool fills this gap by enabling bio-medical experts to explore datasets of cancer patient cohorts. By using multiple coordinated visualization techniques, nested visual queries on various data types can be performed to generate/prove a hypothesis by identifying discrete sub-cohorts. We demonstrated the utility of discoVA by a case study involving bio-medical researchers.