Search

Digital media are becoming increasingly influential in society, especially among the younger generation. Therefore, an augmented reality (AR) app was developed that simulates experiments with radioactive sources. The app runs experiments on the range and penetration power of alpha, beta and gamma radiation. It assigns virtual radiation sources, shielding materials or a detector to printed image markers, and superimposes their 3D images on the camera image. Alpha, beta and gamma radiation are clearly distinguishable by choosing different visualizations. The detector displays the measured count rates. At school, the app can be used in different ways. A concept for a teaching unit in Grade 10 was developed and tested in several classes based on a prototype of the app. The learning progress from the AR experiments was examined. Furthermore, an evaluation of the app was carried out. The most recent version of the app can be found here: https://seafile.projekt.uni-hannover.de/d/dd033aaaf5df4ec18362/

Purpose 7T time-of-flight (TOF) MRI provides high resolution for the evaluation of cerebrovascular vessels and pathologies. In combination with 4D flow fields acquired with phase-contrast (PC) MRI, hemodynamic information can be extracted to enhance the analysis by providing direct measurements in the larger arteries or patient-specific boundary conditions. Hence, a registration between both modalities is required. Methods To combine TOF and PC-MRI data, we developed a hybrid registration approach. Vessels and their centerlines are segmented from the TOF data. The centerline is fit to the intensity ridges of the lower resolved PC-MRI data, which provides temporal information. We used a metric that utilizes a scaled sum of weighted intensities and gradients on the normal plane. The registration is then guided by decoupled local affine transformations. It is applied hierarchically following the branching order of the vessel tree. Results A landmark validation over Monte Carlo simulations yielded an average mean squared error of 184.73 mm and an average Hausdorff distance of 15.20 mm. The hierarchical traversal that transforms child vessels with their parents registers even small vessels not detectable in the PC-MRI. Conclusion The presented work combines high-resolution tomographic information from 7T TOF-MRI and measured flow data from 4D 7T PC-MRI scan for the arteries of the brain. This enables usage of patient-specific flow parameters for realistic simulations, thus supporting research in areas such as cerebral small vessel disease. Automatization and free deformations can help address the limiting error measures in the future.

Training clipping of intracranial aneurysms is challenging due to less frequent interventions because of minimalinvasive methods, ethical aspects regarding cadaver training and consumption of resources when using 3D printed models. By proposing an immersive virtual reality training system, we make use of increased motivation, engagement and realism when using a virtual operating room. In this simulation, a selected microsurgical clip can be applied at the aneurysm neck. Before closing the clip and deforming the vessels, the affected area is visualized to assess the clip position. Our qualitative evaluation with two neurosurgeons with different levels of experience indicates benefits such as increased motivation, presence, and the possibility to try out different strategies. However, some surgical steps can be refined to increase realism and learning effect, and interactions can be further improved. The proposed training system benefits from training by trial and error in an engaging environment leading to an improved training experience.

In contrast to unstructured meshes, structured meshes yield faster simulation results for bio-medical simulations, but are very time-consuming to create. A preprocessing step in the generation of structured meshes is the manual construction of a blockstructure approximating the vessel. Here, we present an automatic centerline calculation and blockstructure generation to reduce the user effort and time of structured mesh generation. The centerline is detected as points in between opposite faces. Based on the centerline, cross sections are determined and a blockstructure which approximates the vessel is automatically generated. The centerline detection does not require time-consuming user input and meshes with more than 195,000 vertices are processed in less than 160 seconds. The results of the presented automatic centerline detection are compared to a centerline with manual input generated by the widely used vmtk tool. The centerlines are similar, small differences occur at bifurcation and at the aneurysm.

7T TOF MRI scans provide high resolution images of intracranial vasculature. When segmented, the Circle of Willis is detailed and thus opens up new possibilities, in research but also in education. We propose a segmentation pipeline for the Circle ofWillis, and introduce a prototype that enables exploration of not just the entire Circle of Willis, but also of its centerline, in an immersive VR enviroment. In our prototype, the model can be freely rotated, placed and scaled. A qualitative evaluation was performed with two experienced neuroradiologists, who rated the prototype and its potential positively.

Objectives 4D flow MRI enables quantitative assessment of helical flow. We sought to generate normal values and elucidate changes of helical flow (duration, volume, length, velocities and rotational direction) and flow jet (displacement, flow angle) as well as wall shear stress (WSS). Methods We assessed the temporal helical existence (THEX), maximum helical volume (HVmax), accumulated helical volume (HVacc), accumulated helical volume length (HVLacc), maximum forward velocity (maxVfor), maximum circumferential velocity (maxVcirc), rotational direction (RD) and maximum wall shear stress (WSS) as reported elsewhere using the software tool Bloodline in 86 healthy volunteers (46 females, mean age 41 ± 13 years). Results WSS decreased by 42.1% and maxVfor by 55.7% across age. There was no link between age and gender regarding the other parameters. Conclusion This study provides age-dependent normal values regarding WSS and maxVfor and age- and gender-independent normal values regarding THEX, HVmax, HVacc, HVLacc, RD and maxVcirc. Key Points • 4D flow provides numerous new parameters; therefore, normal values are mandatory. • Wall shear stress decreases over age. • Maximum helical forward velocity decreases over age.

Purpose Intracranial aneurysms (IAs) are pathological changes of the intracranial vessel wall, although clinical image data can only show the vessel lumen. Histology can provide wall information but is typically restricted to ex vivo 2D slices where the shape of the tissue is altered. Methods We developed a visual exploration pipeline for a comprehensive view of an IA. We extract multimodal information (like stain classification and segmentation of histologic images) and combine them via 2D to 3D mapping and virtual inflation of deformed tissue. Histological data, including four stains, micro-CT data and segmented calcifications as well as hemodynamic information like wall shear stress (WSS), are combined with the 3D model of the resected aneurysm. Results Calcifications were mostly present in the tissue part with increased WSS. In the 3D model, an area of increased wall thickness was identified and correlated to histology, where the Oil red O (ORO) stained images showed a lipid accumulation and the alpha-smooth muscle actin (aSMA) stained images showed a slight loss of muscle cells. Conclusion Our visual exploration pipeline combines multimodal information about the aneurysm wall to improve the understanding of wall changes and IA development. The user can identify regions and correlate how hemodynamic forces, e.g. WSS, are reflected by histological structures of the vessel wall, wall thickness and calcifications.

Purpose The treatment of intracranial arteriovenous malformations (AVM) is challenging due to their complex anatomy. For this vessel pathology, arteries are directly linked to veins without a capillary bed in between. For endovascular treatment, embolization is carried out, where the arteries that supply the AVM are consecutively blocked. A virtual embolization could support the medical expert in treatment planning. Method We designed and implemented an immersive VR application that allows the visualization of the simulated blood flow by displaying millions of particles. Furthermore, the user can interactively block or unblock arteries that supply the AVM and analyze the altered blood flow based on pre-computed simulations. Results In a pilot study, the application was successfully adapted to three patient-specific cases. We performed a qualitative evaluation with two experienced neuroradiologist who regularly conduct AVM embolizations. The feature of virtually blocking or unblocking feeders was rated highly beneficial, and a desire for the inclusion of quantitative information was formulated. Conclusion The presented application allows for virtual embolization and interactive blood flow visualization in an immersive virtual reality environment. It could serve as useful addition for treatment planning and education in clinical practice, supporting the understanding of AVM topology as well as understanding the influence of the AVM’s feeding arteries.

We developed a virtual road crossing assessment tool called iVRoad - immersive Virtual Road, which allows to put the patient into realistic road crossing situations and to record various parameters that can be used to quantify unilateral spatial neglect. We present a study with 18 stroke patients in which we evaluate our system with respect to usability, satisfaction, sense of presence and possible occurring cybersickness symptoms. Unilateral spatial neglect is a cognitive disturbance, often occurring after right hemispheric stroke. Conventional neuropsychological tests, such as paper-and-pencil tests, for assessing unilateral spatial neglect, often lack sensitivity. Especially in mild forms, symptoms can be seen in everyday life, but are hard to detect in formal testing. We examined patients with and without unilateral spatial neglect in order to identify parameters that could be feasible to separate these patient groups. Using everyday life tasks as a diagnostic instrument, however, is challenging because it is time-consuming, hard to control and to quantify. Computer-aided diagnostic systems are promising for analysing the behaviour of patients in detail. Modern virtual reality technology allows to place the patient in realistic situations. Especially situations in which patients often have difficulties or that are too dangerous in reality can be assessed with VR.

We present a system to visualize multiple time-dependent scalar fields defined on 3D surfaces as they arise from numerical simulations. Domain experts want to understand correlations between multiple scalar fields to verify underlying processes. However, existing visualization techniques require high exploration effort to perceive temporal changes and relations of multiple fields on 3D surfaces. This makes analysis time-consuming and carries a high risk of overlooking regions with specific correlations. Therefore, we introduce a novel concept to facilitate exploration of multiple time-dependent scalar fields simultaneously. The basic idea is to use a novel glyph-based depiction, called skyline visualization, which is inspired by the skyscraper metaphor from information visualization. The skyscrapers are represented by 2D and 3D bar charts, where their floors show the temporal behavior of scalar fields of the corresponding surface region. The user can interact with the skyline visualization to see at which surface areas the scalar fields take on striking values or whether correlations exist between them. Furthermore, the user can immediately see in which regions scalar fields change strongly over time or show strongly inverted behavior. We evaluated our system with several CFD engineers, radiologists and visualization experts. Compared to existing systems, they were able to find regions of interest quickly and with high accuracy.

Surgical planning software is a key component in the treatment of tumor diseases. However, desktop-based systems provide only limited visualization and interaction opportunities. Moreover, collaborative planning among members of a surgical team is only possible to a limited extent. In this work, a collaborative virtual reality (VR) environment to assist liver surgeons in tumor surgery planning is presented. Our aim is to improve virtual resection planning between surgeons in a remote or co-located environment. The system allows surgeons to define and adjust virtual resections on patient-specific organ 3D surfaces and 2D image slices. Changes on both modalities are synchronized, which will enable surgeons to iterate and refine the resection surfaces quickly. In addition, a real-time risk map visualization is presented that displays safety margins around tumors. An evaluation performed by liver surgeons provides information on potential benefits, such as the possibility to visualize complex cases and assessing the safety-critical areas, applicability, and limitations for further improvement.

Purpose Intracranial aneurysms are local dilations of brain vessels. Their rupture, as well as their treatment, is associated with high risk of morbidity and mortality. In this work, we propose shape indices for aneurysm ostia for the rupture risk assessment of intracranial aneurysms. Methods We analyzed 84 middle cerebral artery bifurcation aneurysms (27 ruptured and 57 unruptured) and their ostia, with respect to their size and shape. We extracted 3D models of the aneurysms and vascular trees. A semi-automatic approach was used to separate the aneurysm from its parent vessel and to reconstruct the ostium. We used known indices to quantitatively describe the aneurysms. For the ostium, we present new shape indices: the 2D Undulation Index (UI$$_\mathrm{2D}$$ 2 D ), the 2D Ellipticity Index (EI$$_\mathrm{2D}$$ 2 D ) and the 2D Noncircularity Index (NCI$$_\mathrm{2D}$$ 2 D ). Results were analyzed using the Student t test, the Mann–Whitney U test and a correlation analysis between indices of the aneurysms and their ostia. Results Of the indices, none was significantly associated with rupture status. Most aneurysms have an NCI$$_\mathrm{2D}$$ 2 D below 0.2. Of the aneurysms that have an NCI$$_\mathrm{2D}$$ 2 D above 0.5, only one is ruptured, which indicates that ruptured aneurysms often have a circular-shaped ostium. Furthermore, the ostia of ruptured aneurysms tend to have a smaller area, which is also correlated with the aneurysm’s size. While also other variables were significantly correlated, strong linear correlations can only be seen between the area of the ostium with the aneurysm’s volume and surface. Conclusion The proposed shape indices open up new possibilities to quantitatively describe and compare ostia, which can be beneficial for rupture risk assessment and subsequent treatment decision. Additionally, this work shows that the ostium area and the size of the aneurysm are correlated. Further longitudinal studies are necessary to analyze whether stable and unstable aneurysms can be distinguished by their ostia.

Interdisciplinary exchange of medical datasets between clinicians and engineers is essential for clinical research. Due to the Data Protection Act, which preserves the rights of patients, full anonymization is necessary before any exchange can take place. Due to the continuous improvement of image quality of tomographic datasets, anonymization of patient-specific information is not sufficient. In this work, we present a prototype that allows to reliably obscure the facial features of patient data, thus enabling anonymization of neurological datasets in image space.

We present Aneulysis , a system to improve risk assessment and treatment planning of cerebral aneurysms. Aneurysm growth, rupture and treatment success depend on the interplay of vascular morphology and hemodynamics. Blood flow simulations can obtain the patient-specific hemodynamics. However, analyzing the time-dependent, multi-attribute data is time-consuming and error-prone. Aneulysis supports the visual analysis of morphological and hemodynamic aneurysm information by providing different data processing and visualization modules that form an application. In this paper, we integrated a web-based module to analyze aneurysm data, which was designed and evaluated together with domain experts who confirmed its usefulness and clinical relevance. The web-based module supports the delivery of exploration results to people who do not have direct access to the entire database, such as experts at different locations, students or patients. Thus, data sets including findings from previous exploration sessions can be exploited without installing the complete non-web-based application locally.

Vascular structures are assessed, e.g., in tumor surgery to understand the influence of a planned resection on the vascular supply and venous drainage. The understanding of complex branching vascular structures may benefit from immersive virtual reality (VR) visualization. Therefore, the estimation of distance, depth and shape information is a crucial task to support diagnosis and therapy decisions. Depending on the visualization techniques used, perceptual issues can influence this process and may thus lead to false conclusions. Many studies were carried out to study depth perception for different variants of vessel visualization. However, these studies are restricted to desktop applications. Since VR exhibits specific perceptual problems, we aim at an understanding of the appropriateness of vessel visualization techniques in VR. Therefore, this paper presents a user study that investigates the effects of three commonly used visualization techniques on depth perception. The set of visualization techniques comprises Phong shading, pseudo-chromadepth and fog shading. An immersive VR setup of the study using a head-mounted display (HMD) was compared to a traditional desktop setup. Results suggest that depth judgments are less error-prone and more certain in VR than in desktop environments. Moreover, depth-enhancing visualization techniques had greater effects in the desktop study compared to the VR study.

We present an analysis tool for subgroup identification in medical research based on feature analysis. Our use case is intracranial aneurysms. In the tool, an aneurysm-of-interest’s most similar aneurysms within a database are found. Similarity is defined via user-selected parameters, which can be entirely arbitrary. Different interactive outputs and visualizations include a heatmap view and a graph, which give an intuitive feedback to support researchers in the consideration of research questions, which in the present use case often relate to rupture risk analysis. The tool was evaluated with a pilot study and phantom database and received favorable results for its requirements of reliability and appropriate and clear outputs.

Purpose For the status evaluation of intracranial aneurysms (IAs), morphological and hemodynamic parameters can provide valuable information. For their extraction, a separation of the aneurysm sac from its parent vessel is required that yields the neck curve and the ostium. However, manual and subjective neck curve and ostium definitions might lead to inaccurate IA assessments. Methods The research project VICTORIA was initiated, allowing users to interactively define the neck curve of five segmented IA models using a web application. The submitted results were qualitatively and quantitatively compared to identify the minimum, median and maximum aneurysm surface area. Finally, image-based blood flow simulations were carried out to assess the effect of variable neck curve definitions on relevant flow- and shear-related parameters. Results In total, 55 participants (20 physicians) from 18 countries participated in VICTORIA. For relatively simple aneurysms, a good agreement with respect to the neck curve definition was found. However, differences among the participants increased with increasing complexity of the aneurysm. Furthermore, it was observed that the majority of participants excluded any small arteries occurring in the vicinity of an aneurysm. This can lead to non-negligible deviations among the flow- and shear-related parameters, which need to be carefully evaluated, if quantitative analysis is desired. Finally, no differences between participants with medical and non-medical background could be observed. Conclusions VICTORIAs findings reveal the complexity of aneurysm neck curve definition, especially for bifurcation aneurysms. Standardization appears to be mandatory for future sac-vessel-separations. For hemodynamic simulations a careful neck curve definition is crucial to avoid inaccuracies during the quantitative flow analysis.

The exploration of large-scale conflicts, as well as their causes and effects, is an important aspect of socio-political analysis. Since event data related to major conflicts are usually obtained from different sources, researchers developed a semi-automatic matching algorithm to integrate event data of different origins into one comprehensive dataset using hierarchical taxonomies. The validity of the corresponding integration results is not easy to assess since the results depend on user-defined input parameters and the relationships between the original data sources. However, only rudimentary visualization techniques have been used so far to analyze the results, allowing no trustworthy validation or exploration of how the final dataset is composed. To overcome this problem, we developed VEHICLE, a web-based tool to validate and explore the results of the hierarchical integration. For the design, we collaborated with a domain expert to identify the underlying domain problems and derive a task and workflow description. The tool combines both traditional and novel visual analysis techniques, employing statistical and map-based depictions as well as advanced interaction techniques. We showed the usefulness of VEHICLE in two case studies and by conducting an evaluation together with conflict researchers, confirming domain hypotheses and generating new insights.

Purpose For the evaluation and rupture risk assessment of intracranial aneurysms, clinical, morphological and hemodynamic parameters are analyzed. The reliability of intracranial hemodynamic simulations strongly depends on the underlying models. Due to the missing information about the intracranial vessel wall, the patient-specific wall thickness is often neglected as well as the specific physiological and pathological properties of the vessel wall. Methods In this work, we present a model for structural simulations with patient-specific wall thickness including different tissue types based on postmortem histologic image data. Images of histologic 2D slices from intracranial aneurysms were manually segmented in nine tissue classes. After virtual inflation, they were combined into 3D models. This approach yields multiple 3D models of the inner and outer wall and different tissue parts as a prerequisite for subsequent simulations. Result We presented a pipeline to generate 3D models of aneurysms with respect to the different tissue textures occurring in the wall. First experiments show that including the variance of the tissue in the structural simulation affect the simulation result. Especially at the interfaces between neighboring tissue classes, the larger influence of stiffer components on the stability equilibrium became obvious. Conclusion The presented approach enables the creation of a geometric model with differentiated wall tissue. This information can be used for different applications, like hemodynamic simulations, to increase the modeling accuracy.

Purpose Integrated operating rooms provide rich sources of temporal information about surgical procedures, which has led to the emergence of surgical data science. However, little emphasis has been put on interactive visualization of such temporal datasets to gain further insights. Our goal is to put heterogeneous data sequences in relation to better understand the workflows of individual procedures as well as selected subsets, e.g., with respect to different surgical phase distributions and surgical instrument usage patterns. Methods We developed a reusable web-based application design to analyze data derived from surgical procedure recordings. It consists of aggregated, synchronized visualizations for the original temporal data as well as for derived information, and includes tailored interaction techniques for selection and filtering. To enable reproducibility, we evaluated it across four types of surgeries from two openly available datasets (HeiCo and Cholec80). User evaluation has been conducted with twelve students and practitioners with surgical and technical background. Results The evaluation showed that the application has the complexity of an expert tool (System Usability Score of 57.73) but allowed the participants to solve various analysis tasks correctly (78.8% on average) and to come up with novel hypotheses regarding the data. Conclusion The novel application supports postoperative expert-driven analysis, improving the understanding of surgical workflows and the underlying datasets. It facilitates analysis across multiple synchronized views representing information from different data sources and, thereby, advances the field of surgical data science.

Purpose In this work, a virtual environment for interprofessional team training in laparoscopic surgery is proposed. Our objective is to provide a tool to train and improve intraoperative communication between anesthesiologists and surgeons during laparoscopic procedures. Methods An anesthesia simulation software and laparoscopic simulation software are combined within a multi-user virtual reality (VR) environment. Furthermore, two medical training scenarios for communication training between anesthesiologists and surgeons are proposed and evaluated. Testing was conducted and social presence was measured. In addition, clinical feedback from experts was collected by following a think-aloud protocol and through structured interviews. Results Our prototype is assessed as a reasonable basis for training and extensive clinical evaluation. Furthermore, the results of testing revealed a high degree of exhilaration and social presence of the involved physicians. Valuable insights were gained from the interviews and the think-aloud protocol with the experts of anesthesia and surgery that showed the feasibility of team training in VR, the usefulness of the system for medical training, and current limitations. Conclusion The proposed VR prototype provides a new basis for interprofessional team training in surgery. It engages the training of problem-based communication during surgery and might open new directions for operating room training.

Purpose Medical case-based reasoning solves problems by applying experience gained from the outcome of previous treatments of the same kind. Particularly for complex treatment decisions, for example, incidentally found intracranial aneurysms (IAs), it can support the medical expert. IAs bear the risk of rupture and may lead to subarachnoidal hemorrhages. Treatment needs to be considered carefully, since it may entail unnecessary complications for IAs with low rupture risk. With a rupture risk prediction based on previous cases, the treatment decision can be supported. Methods We present an interactive visual exploration tool for the case-based reasoning of IAs. In presence of a new aneurysm of interest, our application provides visual analytics techniques to identify the most similar cases with respect to morphology. The clinical expert can obtain the treatment, including the treatment outcome, for these cases and transfer it to the aneurysm of interest. Our application comprises a heatmap visualization, an adapted scatterplot matrix and fully or partially directed graphs with a circle- or force-directed layout to guide the interactive selection process. To fit the demands of clinical applications, we further integrated an interactive identification of outlier cases as well as an interactive attribute selection for the similarity calculation. A questionnaire evaluation with six trained physicians was used. Result Our application allows for case-based reasoning of IAs based on a reference data set. Three classifiers summarize the rupture state of the most similar cases. Medical experts positively evaluated the application. Conclusion Our case-based reasoning application combined with visual analytic techniques allows for representation of similar IAs to support the clinician. The graphical representation was rated very useful and provides visual information of the similarity of the k most similar cases.

4D flow MRI enables quantitative assessment of helical flow. Current methods are susceptible to noise. To evaluate helical flow patterns in healthy volunteers and patients with bicuspid aortic valves (BAV) at 1.5 T and 3 T using pressure-based helix-extraction in 4D flow MRI. Two intraindividual 4D flow MRI examinations were performed at 1.5 T and 3 T in ten healthy volunteers (5 females, 32 ± 3 years) and 2 patients with BAV using different acceleration techniques (kt-GRAPPA and centra). Several new quantitative parameters for the evaluation of volumes [ml], lengths [mm] as well as temporal parameters [ms] of helical flow were introduced and analyzed using the software tool Bloodline. We found good correlations between measurements in volunteers at 1.5 T and 3 T regarding helical flow volumes (R = 0.98) and temporal existence (R = 0.99) of helices in the ascending aorta. Furthermore, we found significantly larger (11.7 vs. 77.6 ml) and longer lasting (317 vs. 769 ms) helices in patients with BAV than in volunteers. The assessed parameters do not depend on the magnetic field strength used for the acquisition. The technique of pressure-based extraction of 4D flow MRI pattern is suitable for differentiation of normal and pathological flow.

Background: Medical visualization employs elements from computer graphics to create meaningful, interactive visual representations of medical data, and it has become an influential field of research for many advanced applications like radiation oncology, among others. Visual representations employ the user’s cognitive capabilities to support and accelerate diagnostic, planning, and quality assurance workflows based on involved patient data. Summary: This article discusses the basic underlying principles of visualization in the application domain of radiation oncology. The main visualization strategies, such as slice-based representations and surface and volume rendering are presented. Interaction topics, i.e., the combination of visualization and automated analysis methods, are also discussed. Key Messages: Slice-based representations are a common approach in radiation oncology, while volume visualization also has a long-standing history in the field. Perception within both representations can benefit further from advanced approaches, such as image fusion and multivolume or hybrid rendering. While traditional slice-based and volume representations keep evolving, the dimensionality and complexity of medical data are also increasing. To address this, visual analytics strategies are valuable, particularly for cohort or uncertainty visualization. Interactive visual analytics approaches represent a new opportunity to integrate knowledgeable experts and their cognitive abilities in exploratory processes which cannot be conducted by solely automatized methods.

Purpose To create an accurate 3D reconstruction of the vascular trees, it is necessary to know the exact geometrical parameters of the angiographic imaging system. Many previous studies used vascular structures to estimate the system’s exact geometry. However, utilizing interventional devices and their relative features may be less challenging, as they are unique in different views. We present a semi-automatic self-calibration approach considering the markers attached to the interventional instruments to estimate the accurate geometry of a biplane X-ray angiography system for neuroradiologic use. Methods A novel approach is proposed to detect and segment the markers using machine learning classification, a combination of support vector machine and boosted tree. Then, these markers are considered as reference points to optimize the acquisition geometry iteratively. Results The method is evaluated on four clinical datasets and three pairs of phantom angiograms. The mean and standard deviation of backprojection error for the catheter or guidewire before and after self-calibration are $$7.13\pm 6.47$$ 7.13 ± 6.47 mm and $$0.10\pm 0.06$$ 0.10 ± 0.06 mm, respectively. The mean and standard deviation of the 3D root-mean-square error (RMSE) for some markers in the phantom reduced from $$0.51\pm 0.11$$ 0.51 ± 0.11 to $$0.31\pm 0.08$$ 0.31 ± 0.08 mm. Conclusion A semi-automatic approach to estimate the accurate geometry of the C-arm system was presented. Results show the reduction in the 2D backprojection error as well as the 3D RMSE after using our proposed self-calibration technique. This approach is essential for 3D reconstruction of the vascular trees or post-processing techniques of angiography systems that rely on accurate geometry parameters.

The medical domain has been an inspiring application area in visualization research for many years already, but many open challenges remain. The driving forces of medical visualization research have been strengthened by novel developments, for example, in deep learning, the advent of affordable VR technology, and the need to provide medical visualizations for broader audiences. At IEEE VIS 2020, we hosted an Application Spotlight session to highlight recent medical visualization research topics. With this article, we provide the visualization community with ten such open challenges, primarily focused on challenges related to the visualization of medical imaging data. We first describe the unique nature of medical data in terms of data preparation, access, and standardization. Subsequently, we cover open visualization research challenges related to uncertainty, multimodal and multiscale approaches, and evaluation. Finally, we emphasize challenges related to users focusing on explainable AI, immersive visualization, P4 medicine, and narrative visualization. acceptedVersion

Purpose Intracranial aneurysms can be treated micro-surgically. This procedure involves an appropriate head position of the patient and a proper craniotomy. These steps enable a proper access, facilitating the subsequent steps. To train the access planning process, we propose a VR-based training system. Method We designed and implemented an immersive VR access simulation, where the user is surrounded by a virtual operating room, including medical equipment and virtual staff. The patient’s head can be positioned via hand rotation and an arbitrary craniotomy contour can be drawn. The chosen access can be evaluated by exposing the aneurysm using a microscopic view. Results The evaluation of the simulation took place in three stages: testing the simulation using the think-aloud method, conducting a survey and examining the precision of drawing the contour. Although there are differences between the virtual interactions and their counterparts in reality, the participants liked the immersion and felt present in the operating room. The calculated surface dice similarity coefficient, Hausdorff distance and feedback of the participants show that the difficulty of drawing the craniotomy is appropriate. Conclusion The presented training simulation for head positioning and access planning benefits from the immersive environment. Thus, it is an appropriate training for novice neurosurgeons and medical students with the goal to improve anatomical understanding and to become aware of the importance of the right craniotomy hole.

4D PC-MRI enables the assessment of blood flow velocity vectors of one heart beat. These time-dependent, three-dimensional data allow for a comprehensive analysis and contain plenty of information. So far, 4D PC-MRI is used for research purposes. One reason is a lack of standardized, easy-to-use evaluation software, which is crucial for potential application in clinical routine. In this system’s paper, we present our software Bloodline, a tool combining advanced visual exploration and analysis of 4D PC-MRI data. We will put emphasis on design decisions and requirements that arose from the close collaboration with cardiologists and radiologists. The tool provides a guided data processing and evaluation workflow. The pre-processing pipeline comprises generic data import, artifact correction, vessel segmentation, surface as well as centerline extraction, cardiac cycle definition, and landmark specification. Bloodline integrates advanced visualization techniques and facilitates a dataset evaluation within ten minutes. Qualitative and quantitative flow analysis methods are provided. The focus lies on the detection and characterization of vortices, which are correlated to cardiovascular diseases.

User studies are indispensable for visualization application papers in order to assess the value and limitations of the presented approach. Important aspects are how well depth and shape information can be perceived, as coding of these aspects is essential to enable an understandable representation of complex 3D data. In practice, there is usually little time to perform such studies, and the establishment and conduction of user studies can be labour-intensive. In addition, it can be difficult to reach enough participants to obtain expressive results regarding the quality of different visualization techniques. In this paper, we propose a framework that allows visualization researchers to quickly create task-based user studies on depth and shape perception for different surface visualizations and perform the resulting tasks via a web interface. With our approach, the effort for generating user studies is reduced and at the same time the web-based component allows researchers to attract more participants to their study. We demonstrate our framework by applying shape and depth evaluation tasks to visualizations of various surface representations used in many technical and biomedical applications.

To compare two broadly used 4D-flow- with a 2D-flow-sequence in healthy volunteers, regarding absolute flow parameters, image quality (IQ), and eddy current correction (ECC). Forty volunteers (42 ± 11.8 years, 22 females) were examined with a 3T scanner. Thoracic aortic flow was assessed using a 3D-T2w-SPACE-STIR-sequence for morphology and two accelerated 4D-flow sequences for comparison, one with k-t undersampling and one with standard GRAPPA parallel-imaging. 2D-flow was used as reference standard. The custom-made software tool Bloodline enabled flow measurements for all analyses at the same location. Quantitative flow analyses were performed with and without ECC. One reader assessed pathline IQ (IQ-PATH) and occurrence of motion artefacts (IQ-ART) on a 3-point grading scale, the higher the better. k-t GRAPPA allowed a significant mean scan time reduction of 46% (17:56 ± 5:26 min vs. 10:40 ± 3:15 min) and provided significantly fewer motion artefacts than standard GRAPPA (IQ-ART 1.57 ± 0.55 vs. 0.84 ± 0.48; p k-t GRAPPA sequence performed best (R = 0.96 vs. 0.90). k-t GRAPPA 4D-flow was not inferior to a standard GRAPPA-sequence, showed fewer artefacts, comparable IQ and was almost two-fold faster.

BACKGROUND: Preoperative three-dimensional (3D) reconstructions for liver surgery planning have been shown to be effective in reduction of blood loss and operation time. However, the role of the ‘presentation modality’ is not well investigated. We present the first study to compare 3D PDFs, 3D printed models (PR) and virtual reality (VR) 3D models with regard to anatomical orientation and personal preferences in a high volume liver surgery center. METHODS: Thirty participants, 10 medical students, 10 residents, 5 fellows and 5 hepatopancreatobiliary (HPB) experts, assigned the tumor-bearing segments of 20 different patient’s individual liver reconstructions. Liver models were presented in a random order in all modalities. Time needed to specify the tumor location was recorded. In addition, a score was calculated factoring in correct, wrong and missing segment assignments. Furthermore, standardized test/questionnaires for spatial thinking and seeing, vegetative side effects and usability were completed. RESULTS: Participants named significantly more correct segments in VR (P=0.040) or PR (P=0.036) compared to PDF. Tumor assignment was significantly shorter with 3D PR models compared to 3D PDF (P

PurposeThe treatment of cerebral aneurysms shifted from microsurgical to endovascular therapy. But for some difficult aneurysm configurations, e.g. wide neck aneurysms, microsurgical clipping is better suited. From this combination of limited interventions and the complexity of these cases, the need for improved training possibilities for young neurosurgeons arises.MethodWe designed and implemented a clipping simulation that requires only a monoscopic display, mouse and keyboard. After a virtual craniotomy, the user can apply a clip at the aneurysm which is deformed based on a mass–spring model. Additionally, concepts for visualising distances as well as force were implemented. The distance visualisations aim to enhance spatial relations, improving the navigation of the clip. The force visualisations display the force acting on the vessel surface by the applied clip. The developed concepts include colour maps and visualisations based on rays, single objects and glyphs.ResultsThe concepts were quantitatively evaluated via an online survey and qualitatively evaluated by a neurosurgeon. Regarding force visualisations, a colour map is the most appropriate concept. The necessity of distance visualisations became apparent, as the expert was unable to estimate distances and to properly navigate the clip. The distance rays were the only concept supporting the navigation appropriately.ConclusionThe easily accessible surgical training simulation for aneurysm clipping benefits from a visualisation of distances and simulated forces.

To exploit the potential of virtual reality (VR) in medicine, the input devices must be selected carefully due to their different benefits. In this work, input devices for common interaction tasks in medical VR planning and training are compared. Depending on the specific purpose, different requirements exist. Therefore, an appropriate trade-off between meeting task-specific requirements and having a widely applicable device has to be found. We focus on two medical use cases, liver surgery planning and craniotomy training, to cover a broad medical domain. Based on these, relevant input devices are compared with respect to their suitability for performing precise VR interaction tasks. The devices are standard VR controllers, a pen-like VR Ink, data gloves and a real craniotome, the medical instrument used for craniotomy. The input devices were quantitatively compared with respect to their performance based on different measurements. The controllers and VR Ink performed significantly better than the remaining two devices regarding precision. Qualitative data concerning task load, cybersickness, and usability and appropriateness of the devices were assessed. Although no device stands out for both applications, most participants preferred using the VR Ink, followed by the controller and finally the data gloves and craniotome. These results can guide the selection of an appropriate device for future medical VR applications.

Background and objective: MeshCNN is a recently proposed Deep Learning framework that drew attention due to its direct operation on irregular, non-uniform 3D meshes. On selected benchmarking datasets, it outperformed state-of-the-art methods within classification and segmentation tasks. Especially, the medical domain provides a large amount of complex 3D surface models that may benefit from processing with MeshCNN. However, several limitations prevent outstanding performances of MeshCNN on highly diverse medical surface models. Within this work, we propose MedMeshCNN as an expansion for complex, diverse, and fine-grained medical data. Methods: MedMeshCNN follows the functionality of MeshCNN with a significantly increased memory efficiency that allows retaining patient-specific properties during the segmentation process. Furthermore, it enables the segmentation of pathological structures that often come with highly imbalanced class distributions. Results: We tested the performance of MedMeshCNN on a complex part segmentation task of intracranial aneurysms and their surrounding vessel structures and reached a mean Intersection over Union of 63.24\%. The pathological aneurysm is segmented with an Intersection over Union of 71.4\%. Conclusions: These results demonstrate that MedMeshCNN enables the application of MeshCNN on complex, fine-grained medical surface meshes. The imbalanced class distribution deriving from the pathological finding is considered by MedMeshCNN and patient-specific properties are mostly retained during the segmentation process., Comment: 7 pages, 7 figures, 1 table, Submitted to Computer Methods and Programs in Biomedicine

Objective: We propose an intelligent system that assists epidemiology experts in analysing the data of a population-based epidemiological study, in identifying relevant variables for an outcome and subpopulations with increased disease prevalence, and in validating the findings concerning variables and subpopulations in a further, expert-specified cohort. At present, the study of an outcome on a population-based cohort is hypothesis-driven, i.e. the expert must specify the variables to be studied. Our approach rather operates in a data-driven, semi-automated way, enabling the expert to identify variables of relevance and generate hypotheses on them. Methods: Our system DIVA supports the Discovery, Inspection and VAlidation of subpopulations with increased prevalence of an outcome, without requiring parameter tuning. DIVA takes as input the cohort of an epidemiological population-based study with all variables specified in the study’s protocol, as well as inputs from the expert on the similarity of a small number of cohort participants. DIVA uses semi-supervised subspace clustering and subspace construction to identify sets of variables – subspaces – that promote participant similarity with respect to the outcome and with respect to the expert inputs, and then discovers subpopulations with increased outcome prevalence in those subspaces (DIVA module “DRESS”). DIVA uses visual analytics techniques to assist the expert in juxtaposing, filtering and inspecting the characteristics of these subpopulations (web-based DIVA module “D-INSPECTOR”). If the expert has access to a second cohort on a comparable population, DIVA aligns the cohort used for discovery to this second cohort, and then checks whether the subpopulations found in the original cohort are also present in the second one (DIVA module “VALIDATOR”). Results: We applied DIVA to the third wave (SHIP-2) of the SHIP-CORE cohort of the Study of Health in Pomerania (Volzke et al., 2011) for the liver disorder “hepatic steatosis”, and on the first wave (TREND-0) of the SHIP-TREND cohort of the same study for the thyroid gland disorder “goitre”. We found that most of the subpopulations extracted automatically, and subsequently ranked and filtered by the modules of DIVA, had significantly higher disease prevalence than the general population. We varied the amount of inputs needed from the expert to drive the subpopulation extraction process and found that a very small amount of information, namely the outcome of as few as 4 cohort participants, is sufficient for the identification of several relevant variables and subpopulations. We used a subset of TREND-0 for the validation on goitre and the complete TREND-0 for the validation on hepatic steatosis and found that the significant difference in prevalence for the identified subpopulation also holds in the validation data. Conclusions: We have shown that DIVA discovers subpopulations and variables of importance with respect to an outcome, while requiring a very small amount of expert inputs. Each combination of variables and each subpopulation corresponds to a hypothesis, the validation of which would have required substantial human effort. Thus, DIVA allows for a more effective exploitation of population-based data, not fully automated but driven by the expert and without the need for technical parameter tuning. A shortcoming of DIVA design is the demand of a specific type of expert inputs, namely “constraints” on the similarity of pairs of participants. Currently, we generate the constraints with a naive utility that is based on random sampling, but we work on the development of an interactive algorithm that would allow the epidemiology expert to inspect a small choice of study participant and give statements on their similarity. The present version of DIVA considers a single wave of the cohort data, ignoring the evolution of the population during the horizon of the study. Hence, subspace and subpopulation discovery do not take account of changes in the importance of variables. We currently work on the incorporation of algorithms that derive additional variables from the longitudinal data and use them in the Discovery module.

This paper presents a method for clustering time-dependent blood flow data, represented by path lines, in cerebral aneurysms using a reliable similarity measure combined with a clustering technique. Such aneurysms bear the risk of rupture, whereas their treatment also carries considerable risks for the patient. Medical researchers emphasize the importance of investigating aberrant blood flow patterns for the patient-specific rupture risk assessment and treatment analysis. Therefore, occurring flow patterns are manually extracted and classified according to predefined criteria. The manual extraction is time-consuming for larger studies and affected by visual clutter, which complicates the subsequent classification of flow patterns. In contrast, our method allows an automatic and reliable clustering of intra-aneurysmal flow patterns that facilitates their classification. We introduce a similarity measure that groups spatio-temporally adjacent flow patterns. We combine our similarity measure with a commonly used clustering technique and applied it to five representative datasets. The clustering results are presented by 2D and 3D visualizations and were qualitatively compared and evaluated by four domain experts. Moreover, we qualitatively evaluated our similarity measure.