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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 Augmenting Node-Link Diagrams with Topographic Attribute Maps(The Eurographics Association and John Wiley & Sons Ltd., 2020) Preiner, Reinhold; Schmidt, Johanna; Krösl, Katharina; Schreck, Tobias; Mistelbauer, Gabriel; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, TatianaWe propose a novel visualization technique for graphs that are attributed with scalar data. In many scenarios, these attributes (e.g., birth date in a family network) provide ambient context information for the graph structure, whose consideration is important for different visual graph analysis tasks. Graph attributes are usually conveyed using different visual representations (e.g., color, size, shape) or by reordering the graph structure according to the attribute domain (e.g., timelines). While visual encodings allow graphs to be arranged in a readable layout, assessing contextual information such as the relative similarities of attributes across the graph is often cumbersome. In contrast, attribute-based graph reordering serves the comparison task of attributes, but typically strongly impairs the readability of the structural information given by the graph's topology. In this work, we augment force-directed node-link diagrams with a continuous ambient representation of the attribute context. This way, we provide a consistent overview of the graph's topological structure as well as its attributes, supporting a wide range of graph-related analysis tasks. We resort to an intuitive height field metaphor, illustrated by a topographic map rendering using contour lines and suitable color maps. Contour lines visually connect nodes of similar attribute values, and depict their relative arrangement within the global context. Moreover, our contextual representation supports visualizing attribute value ranges associated with graph nodes (e.g., lifespans in a family network) as trajectories routed through this height field. We discuss how user interaction with both the structural and the contextual information fosters exploratory graph analysis tasks. The effectiveness and versatility of our technique is confirmed in a user study and case studies from various application domains.Item Transdisciplinary Visualization of Aortic Dissections(The Eurographics Association, 2023) Mistelbauer, Gabriel; Bäumler, Kathrin; Mastrodicasa, Domenico; Hahn, Lewis D.; Pepe, Antonio; Sandfort, Veit; Hinostroza, Virginia; Ostendorf, Kai; Schroeder, Aaron; Sailer, Anna M.; Willemink, Martin J.; Walters, Shannon; Preim, Bernhard; Fleischmann, Dominik; Raidou, Renata; Kuhlen, TorstenAortic dissection is a life-threatening condition caused by the abrupt formation of a secondary blood flow channel within the vessel wall. Patients surviving the acute phase remain at high risk for late complications, such as aneurysm formation and aortic rupture. The timing of these complications is variable, making long-term imaging surveillance crucial for aortic growth monitoring. Morphological characteristics of the aorta, its hemodynamics, and, ultimately, risk models impact treatment strategies. Providing such a wealth of information demands expertise across a broad spectrum to understand the complex interplay of these influencing factors. We present results of our longstanding transdisciplinary efforts to confront this challenge. Our team has identified four key disciplines, each requiring specific expertise overseen by radiology: lumen segmentation and landmark detection, risk predictors and inter-observer analysis, computational fluid dynamics simulations, and visualization and modeling. In each of these disciplines, visualization supports analysis and serves as communication medium between stakeholders, including patients. For each discipline, we summarize the work performed, the related work, and the results.Item Knowledge-Assisted Comparative Assessment of Breast Cancer using Dynamic Contrast-Enhanced Magnetic Resonance Imaging(The Eurographics Association and John Wiley & Sons Ltd., 2020) Nie, Kai; Baltzer, Pascal; Preim, Bernhard; Mistelbauer, Gabriel; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, TatianaBreast perfusion data are dynamic medical image data that depict perfusion characteristics of the investigated tissue. These data consist of a series of static datasets that are acquired at different time points and aggregated into time intensity curves (TICs) for each voxel. The characteristics of these TICs provide important information about a lesion's composition, but their analysis is time-consuming due to their large number. Subsequently, these TICs are used to classify a lesion as benign or malignant. This lesion scoring is commonly done manually by physicians and may therefore be subject to bias. We propose an approach that addresses both of these problems by combining an automated lesion classification with a visual confirmatory analysis, especially for uncertain cases. Firstly, we cluster the TICs of a lesion using ordering points to identify the clustering structure (OPTICS) and then visualize these clusters. Together with their relative size, they are added to a library. We then model fuzzy inference rules by using the lesion's TIC clusters as antecedents and its score as consequent. Using a fuzzy scoring system, we can suggest a score for a new lesion. Secondly, to allow physicians to confirm the suggestion in uncertain cases, we display the TIC clusters together with their spatial distribution and allow them to compare two lesions side by side. With our knowledge-assisted comparative visual analysis, physicians can explore and classify breast lesions. The true positive prediction accuracy of our scoring system achieved 71.4% in one-fold cross-validation using 14 lesions.Item Vessel Maps: A Survey of Map-Like Visualizations of the Cardiovascular System(The Eurographics Association and John Wiley & Sons Ltd., 2022) Eulzer, Pepe; Meuschke, Monique; Mistelbauer, Gabriel; Lawonn, Kai; Bruckner, Stefan; Turkay, Cagatay; Vrotsou, KaterinaMap-like visualizations of patient-specific cardiovascular structures have been applied in numerous medical application contexts. The term map-like alludes to the characteristics these depictions share with cartographic maps: they show the spatial relations of data attributes from a single perspective, they abstract the underlying data to increase legibility, and they facilitate tasks centered around overview, navigation, and comparison. A vast landscape of techniques exists to derive such maps from heterogeneous data spaces. Yet, they all target similar purposes within disease diagnostics, treatment, or research and they face coinciding challenges in mapping the spatial component of a treelike structure to a legible layout. In this report, we present a framing to unify these approaches. On the one hand, we provide a classification of the existing literature according to the data spaces such maps can be derived from. On the other hand, we view the approaches in light of the manifold requirements medical practitioners and researchers have in their efforts to combat the ever-growing burden of cardiovascular disease. Based on these two perspectives, we offer recommendations for the design of map-like visualizations of the cardiovascular system.Item Visual Analytics in Dental Aesthetics(The Eurographics Association and John Wiley & Sons Ltd., 2020) Amirkhanov, Aleksandr; Bernhard, Matthias; Karimov, Alexey; Stiller, Sabine; Geier, Andreas; Gröller, Eduard; Mistelbauer, Gabriel; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-LueDental healthcare increasingly employs computer-aided design software, to provide patients with high-quality dental prosthetic devices. In modern dental reconstruction, dental technicians address the unique anatomy of each patient individually, by capturing the dental impression and measuring the mandibular movements. Subsequently, dental technicians design a custom denture that fits the patient from a functional point of view. The current workflow does not include a systematic analysis of aesthetics, and dental technicians rely only on an aesthetically pleasing mock-up that they discuss with the patient, and on their experience. Therefore, the final denture aesthetics remain unknown until the dental technicians incorporate the denture into the patient. In this work, we present a solution that integrates aesthetics analysis into the functional workflow of dental technicians. Our solution uses a video recording of the patient, to preview the denture design at any stage of the denture design process. We present a teeth pose estimation technique that enables denture preview and a set of linked visualizations that support dental technicians in the aesthetic design of dentures. These visualizations assist dental technicians in choosing the most aesthetically fitting preset from a library of dentures, in identifying the suitable denture size, and in adjusting the denture position. We demonstrate the utility of our system with four use cases, explored by a dental technician. Also, we performed a quantitative evaluation for teeth pose estimation, and an informal usability evaluation, with positive outcomes concerning the integration of aesthetics analysis into the functional workflow.Item Shading Style Assessment for Vessel Wall and Lumen Visualization(The Eurographics Association, 2021) Ostendorf, Kai; Mastrodicasa, Domenico; Bäumler, Kathrin; Codari, Marina; Turner, Valery; Willemink, Martin J.; Fleischmann, Dominik; Preim, Bernhard; Mistelbauer, Gabriel; Oeltze-Jafra, Steffen and Smit, Noeska N. and Sommer, Björn and Nieselt, Kay and Schultz, ThomasCurrent blood vessel rendering usually depicts solely the surface of vascular structures and does not visualize any interior structures. While this approach is suitable for most applications, certain cardiovascular diseases, such as aortic dissection would benefit from a more comprehensive visualization. In this work, we investigate different shading styles for the visualization of the aortic inner and outer wall, including the dissection flap. Finding suitable shading algorithms, techniques, and appropriate parameters is time-consuming when practitioners fine-tune them manually. Therefore, we build a shading pipeline using wellknown shading algorithms such as Blinn-Phong, Oren-Nayar, Cook-Torrance, Toon, and extended Lit-Sphere shading with techniques such as the Fresnel effect and screen space ambient occlusion. We interviewed six experts from various domains to find the best combination of shadings for preset combinations that maximize user experience and the applicability in clinical settings.