Aortic dissection is a life-threatening cardiovascular disease characterized by blood entering the media layer of the aortic vessel wall. This creates a second flow channel, known as the false lumen, which weakens the aortic wall and can potentially lead to fatal aortic rupture. Current risk stratification of aortic dissections is primarily based on morphological features of the aorta. However, hemodynamics also play a significant role in disease progression, though their investigation and visualization remain challenging. Common flow visualizations often experience visual clutter, especially when dealing with the intricate morphologies of aortic dissections. In this work, we implement and evaluate different approaches to visualizing the flow in aortic dissections effectively. We employ three techniques, namely streaklines with depth-dependent halos, transparent streaklines, and smoke surfaces. The latter is a technique based on streak surfaces, enhanced with opacity modulations, to produce a smoke-like appearance that improves visual clarity. We adapt the original opacity modulation of smoke surfaces to visualize flow even within the complex geometries of aortic dissections, thereby enhancing visual fidelity. To effectively capture dissection hemodynamics, we developed customized seeding structures that adapt to the shape of the surrounding lumen. Our evaluation, conducted via an online questionnaire, included medical professionals, fluid simulation experts, and visualization specialists. By analyzing results across these groups, we highlight differences in preference and interpretability, offering insight into domain-specific needs. No single visualization technique emerged as the best overall. Smoke surfaces provide the best overall clarity and visual realism. However, participants found streaklines with halos to be the best for quantifying flow, dispite them introducing significant visual clutter. Transparent streaklines serve as a middle ground, offering improved clarity over halos while maintaining some level of detail. Across all participant groups, smoke surfaces were rated as the most visually appealing and lifelike, with medical professionals highlighting their resemblance to contrast-agent injections used in clinical practice.

Aortic dissection is a rare disease affecting the aortic wall layers splitting the aortic lumen into two flow channels: the true and false lumen. The rarity of the disease leads to a sparsity of available datasets resulting in a low amount of available training data for in-silico studies or the training of machine learning algorithms . To mitigate this issue, we use statistical shape modeling to create a database of Stanford type B dissection surface meshes. We account for the complex disease anatomy by modeling two separate flow channels in the aorta, the true and false lumen. Former approaches mainly modeled the aortic arch including its branches but not two separate flow channels inside the aorta. To our knowledge, our approach is the first to attempt generating synthetic aortic dissection surface meshes. For the statistical shape model, the aorta is parameterized using the centerlines of the respective lumen and the according ellipses describing the cross-section of the lumen while being aligned along the centerline employing rotation-minimizing frames. To evaluate our approach we introduce disease-specific quality criteria by investigating the torsion and twist of the true lumen.

Aortic dissection is a life-threatening cardiovascular disease constituted by the delamination of the aortic wall. Due to the weakened structure of the false lumen, the aorta often dilates over time, which can – after certain diameter thresholds are reached – increase the risk of fatal aortic rupture. The identification of patients with a high risk of late adverse events is an ongoing clinical challenge, further complicated by the complex dissection anatomy and the wide variety among patients. Moreover, patient-specific risk stratification depends not only on morphological, but also on hemodynamic factors, which can be derived from computer simulations or 4D flow magnetic resonance imaging (MRI). However, comprehensible visualizations that depict the complex anatomical and functional information in a single view are yet to be developed. These visualization tools will assist clinical research and decision-making by facilitating a comprehensive understanding of the aortic state. For that purpose, we identified several visualization tasks and requirements in close collaboration with cardiovascular imaging scientists and radiologists. We displayed true and false lumen hemodynamics using pathlines as well as surface hemodynamics on the dissection flap and the inner vessel wall. Pathlines indicate antegrade and retrograde flow, blood flow through fenestrations, and branch vessel supply. Dissection-specific hemodynamic measures, such as interluminal pressure difference and flap compliance, provide further insight of the blood flow throughout the cardiac cycle. Finally, we evaluated our visualization techniques with cardiothoracic and vascular surgeons in two separate virtual sessions. • Correct mapping of flow in true and false lumen, preventing incorrect integration. • Display of dissection flap deformation, compliance, and interluminal pressure difference. • Display of the impacts of blood flow dynamics on the weakened false lumen outer wall. • Display of luminal drainage patterns through the encoding of blood stream origins.

Abstract Map‐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.

Abstract 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.

Abstract Dental 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.

Abstract Breast 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.

Abstract We 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, assessingcontextualinformation 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 thestructuralinformation 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.

Abstract Mathematical models of ordinary differential equations are used to describe and understand biological phenomena. These models are dynamical systems that often describe the time evolution of more than three variables, i.e., their dynamics take place in a multi‐dimensional space, called the phase space. Currently, mathematical domain scientists use plots of typical trajectories in the phase space to analyze the qualitative behavior of dynamical systems. These plots are called phase portraits and they perform well for 2D and 3D dynamical systems. However, for 4D, the visual exploration of trajectories becomes challenging, as simple subspace juxtaposition is not sufficient. We propose ManyLands to support mathematical domain scientists in analyzing 4D models of biological systems. By describing the subspaces as Lands, we accompany domain scientists along a continuous journey through 4D HyperLand, 3D SpaceLand, and 2D FlatLand, using seamless transitions. The Lands are also linked to 1D TimeLines. We offer an additional dissected view of trajectories that relies on small‐multiple compass‐alike pictograms for easy navigation across subspaces and trajectory segments of interest. We show three use cases of 4D dynamical systems from cell biology and biochemistry. An informal evaluation with mathematical experts confirmed that ManyLands helps them to visualize and analyze complex 4D dynamics, while facilitating mathematical experiments and simulations.

Temporal data visualization is used to analyze dependent variables that vary over time, with time being an independent variable. Visualizing temporal data is inherently difficult, due to the many aspects that need to be communicated to the users (e.g., time and variable changes). This is an important topic in visualization, and a wide range of visualization techniques dealing with different tasks have already been designed. In this paper we propose popup-plots, a novel concept where the common interaction of 3D rotation is used to navigate through the data. This allows the users to view the data from different perspectives without having to learn and adapt to new interaction concepts. Popup-plots are therefore a novel method for visualizing and interacting with dependent variables over time. We extend 2D plots with the temporal information by bending the space according to the time. The bending is calculated based on a spherical coordinates approach, which is continuously influenced by the viewing direction towards the plot. Hence, the plot can be viewed from various angles with seamless transitions in between, offering the possibility to analyze different aspects of the represented data. As the current viewing direction is inherently depicted by the shape of the data, the users are able to deduce which part of the data is currently viewed. The temporal information is encoded into the visualization itself, resembling annual rings of a tree. We demonstrate our method by applying it to data from two different domains, comprising measurements at spatial positions over time, and we also evaluated the usability of our solution.

Abstract In many areas of medicine, visualization research can help with task simplification, abstraction or complexity reduction. A common visualization approach is to facilitate parameterization techniques which flatten a usually 3D object into a 2D plane. Within this state of the art report (STAR), we review such techniques used in medical visualization and investigate how they can be classified with respect to the handled data and the underlying tasks. Many of these techniques are inspired by mesh parameterization algorithms which help to project a triangulation inℝ3to a simpler domain inℝ2. It is often claimed that this makes complex structures easier to understand and compare by humans and machines. Within this STAR we review such flattening techniques which have been developed for the analysis of the following medical entities: the circulation system, the colon, the brain, tumors, and bones. For each of these five application scenarios, we have analyzed the tasks and requirements, and classified the reviewed techniques with respect to a developed coding system. Furthermore, we present guidelines for the future development of flattening techniques in these areas.

Abstract This paper presents a method for the visual quantification of cerebral arteries, known as the Circle of Willis (CoW). It is an arterial structure with the responsibility of supplying the brain with blood, however, dysfunctions can lead to strokes. The diagnosis of such a time‐critical/urgent event depends on the expertise of radiologists and the applied software tools. They use basic display methods of the volumetric data without any support of advanced image processing and visualization techniques. The goal of this paper is to present an automated method for the standardized description of cerebral arteries in stroke patients in order to provide an overview of the CoW's configuration. This novel representation provides visual indications of problematic areas as well as straightforward comparisons between multiple patients. Additionally, we offer a pipeline for extracting the CoW from Time‐of‐Flight Magnetic Resonance Angiography (TOF‐MRA) data sets together with an enumeration technique for labelling the arterial segments by detecting the main supplying arteries of the CoW. We evaluated the feasibility of our visual quantification approach in a study of 63 TOF‐MRA data sets and compared our findings to those of three radiologists. The obtained results demonstrate that our proposed techniques are effective in detecting the arteries and visually capturing the overall configuration of the CoW.

The human placenta is essential for the supply of the fetus. To monitor the fetal development, imaging data is acquired using (US). Although it is currently the gold-standard in fetal imaging, it might not capture certain abnormalities of the placenta. (MRI) is a safe alternative for the in utero examination while acquiring the fetus data in higher detail. Nevertheless, there is currently no established procedure for assessing the condition of the placenta and consequently the fetal health. Due to maternal respiration and inherent movements of the fetus during examination, a quantitative assessment of the placenta requires fetal motion compensation, precise placenta segmentation and a standardized visualization, which are challenging tasks. Utilizing advanced motion compensation and automatic segmentation methods to extract the highly versatile shape of the placenta, we introduce a novel visualization technique that presents the fetal and maternal side of the placenta in a standardized way. Our approach enables physicians to explore the placenta even in utero. This establishes the basis for a comparative assessment of multiple placentas to analyze possible pathologic arrangements and to support the research and understanding of this vital organ. Additionally, we propose a three-dimensional structure-aware surface slicing technique in order to explore relevant regions inside the placenta. Finally, to survey the applicability of our approach, we consulted clinical experts in prenatal diagnostics and imaging. We received mainly positive feedback, especially the applicability of our technique for research purposes was appreciated.

Abstract Segmentation of volumetric data is an important part of many analysis pipelines, but frequently requires manual inspection and correction. While plenty of volume editing techniques exist, it remains cumbersome and errorprone for the user to find and select appropriate regions for editing. We propose an approach to improve volume editing by detecting potential segmentation defects while considering the underlying structure of the object of interest. Our method is based on a novel histogram dissimilarity measure between individual regions, derived from structural information extracted from the initial segmentation. Based on this information, our interactive system guides the user towards potential defects, provides integrated tools for their inspection, and automatically generates suggestions for their resolution. We demonstrate that our approach can reduce interaction effort and supports the user in a comprehensive investigation for high‐quality segmentations.

Abstract Volume segmentation is important in many applications, particularly in the medical domain. Most segmentation techniques, however, work fully automatically only in very restricted scenarios and cumbersome manual editing of the results is a common task. In this paper, we introduce a novel approach for the editing of segmentation results. Our method exploits structural features of the segmented object to enable intuitive and robust correction and verification. We demonstrate that our new approach can significantly increase the segmentation quality even in difficult cases such as in the presence of severe pathologies.

Abstract Radiological investigations are common medical practice for the diagnosis of peripheral vascular diseases. Existing visualization methods such as Curved Planar Reformation (CPR) depict calcifications on vessel walls to determine if blood is still able to flow. While it is possible with conventional CPR methods to examine the whole vessel lumen by rotating around the centerline of a vessel, we propose Curvicircular Feature Aggregation (CFA), which aggregates these rotated images into a single view. By eliminating the need for rotation, vessels can be investigated by inspecting only one image. This method can be used as a guidance and visual analysis tool for treatment planning. We present applications of this technique in the medical domain and give feedback from radiologists.

Visualizations of vascular structures are frequently used in radiological investigations to detect and analyze vascular diseases. Obstructions of the blood flow through a vessel are one of the main interests of physicians, and several methods have been proposed to aid the visual assessment of calcifications on vessel walls. Curved Planar Reformation (CPR) is a wide-spread method that is designed for peripheral arteries which exhibit one dominant direction. To analyze the lumen of arbitrarily oriented vessels, Centerline Reformation (CR) has been proposed. Both methods project the vascular structures into 2D image space in order to reconstruct the vessel lumen. In this paper, we propose Curved Surface Reformation (CSR), a technique that computes the vessel lumen fully in 3D. This offers high-quality interactive visualizations of vessel lumina and does not suffer from problems of earlier methods such as ambiguous visibility cues or premature discretization of centerline data. Our method maintains exact visibility information until the final query of the 3D lumina data. We also present feedback from several domain experts.

Visualization of vascular structures is a common and frequently performed task in the field of medical imaging. There exist well established and applicable methods such as Maximum Intensity Projection (MIP) and Curved Planar Reformation (CPR). However, when calcified vessel walls are investigated, occlusion hinders exploration of the vessel interior with MIP. In contrast, CPR offers the possibility to visualize the vessel lumen by cutting a single vessel along its centerline. Extending the idea of CPR, we propose a novel technique, called Centerline Reformation (CR), which is capable of visualizing the lumen of spatially arbitrarily oriented vessels not necessarily connected in a tree structure. In order to visually emphasize depth, overlap and occlusion, halos can optionally envelope the vessel lumen. The required vessel centerlines are obtained from volumetric data by performing a scale-space based feature extraction. We present the application of the proposed technique in a focus and context setup. Further, we demonstrate how it facilitates the investigation of dense vascular structures, particularly cervical vessels or vessel data featuring peripheral arterial occlusive diseases or pulmonary embolisms. Finally, feedback from domain experts is given.