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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 LundervoldFour-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. RopinskiFour-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, HelwigWe 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 vonAortic 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, HeikeWe 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, ThomasWe 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 GeorgiaWe 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.Item Robust Cardiac Function Assessment in 4D PC-MRI Data(The Eurographics Association, 2014) Köhler, Benjamin; Preim, Uta; Gutberlet, Matthias; Fischbach, Katharina; Preim, Bernhard; Ivan Viola and Katja Buehler and Timo RopinskiFour-dimensional phase-contrast magnetic resonance imaging (4D PC-MRI) is a relatively young image modality that allows the non-invasive acquisition of time-resolved, three-dimensional blood flow information. Stroke volumes and regurgitation fractions are two of the main measures to assess the cardiac function and severity of pathologies. The flow volumes in forward and backward direction through a plane inside the vessel are required for their quantification. Unfortunately, the calculations are highly sensitive towards the plane's angulation since orthogonally passing flow is considered. This often leads to physiologically implausible results. In this work, a robust quantification method is introduced to overcome this problem. Collaborating radiologists and cardiologists were carefully observed while estimating stroke volumes in various healthy volunteer and patient datasets with conventional quantification. This facilitated the automatization of their approach which, in turn, allows to derive statistical information about the plane angulation sensitivity. Moreover, the experts expect a continuous decrease of the stroke volume along the vessel course after a peak value above the aortic valve. Conventional methods are often unable to produce this behavior. Thus, we present a procedure to fit a function that ensures such physiologically plausible results. In addition, the technique was adapted for the robust quantification of regurgitation fractions. The performed qualitative evaluation shows the capability of our method to support diagnosis, a parameter evaluation confirms the robustness. Vortex flow was identified as main cause for quantification uncertainties.Item Comparative Evaluation of Feature Line Techniques for Shape Depiction(The Eurographics Association, 2014) Lawonn, Kai; Baer, Alexandra; Saalfeld, Patrick; Preim, Bernhard; Jan Bender and Arjan Kuijper and Tatiana von Landesberger and Holger Theisel and Philipp UrbanThis paper presents a qualitative evaluation of feature line techniques on various surfaces. We introduce the most commonly used feature lines and compare them. The techniques were analyzed with respect to the degree of realism in comparison with a shaded image with respect to the aesthetic impression they create. First, a pilot study with 20 participants was conducted to make an inquiry about their behavior and the duration. Based on the result of the pilot study, the final evaluation was carried out with 129 participants. We evaluate and interpret the trial results by using the Schulze method and give recommendations for which kind of surface, which feature line technique is most appropriate.Item COMFIS - Comparative Visualization of Simulated Medical Flow Data(The Eurographics Association, 2022) Meuschke, Monique; Voß, Samuel; Eulzer, Pepe; Janiga, Gabor; Arens, Christoph; Wickenhöfer, Ralph; Preim, Bernhard; Lawonn, Kai; Renata G. Raidou; Björn Sommer; Torsten W. Kuhlen; Michael Krone; Thomas Schultz; Hsiang-Yun WuSimulations of human blood and airflow are playing an increasing role in personalized medicine. Comparing flow data of different treatment scenarios or before and after an intervention is important to assess treatment options and success. However, existing visualization tools are either designed for the evaluation of a single data set or limit the comparison to a few partial aspects such as scalar fields defined on the vessel wall or internal flow patterns. Therefore, we present COMFIS, a system for the comparative visual analysis of two simulated medical flow data sets, e.g. before and after an intervention. We combine various visualization and interaction methods for comparing different aspects of the underlying, often time-dependent data. These include comparative views of different scalar fields defined on the vessel/mucous wall, comparative depictions of the underlying volume data, and comparisons of flow patterns. We evaluated COMFIS with CFD engineers and medical experts, who were able to efficiently find interesting data insights that help to assess treatment options.