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We present a framework with two options for selecting a subgroup of training cases for intracranial aneurysm (IA) treatment. Option one, general training, describes training cases that represent a variety of different IAs that represent the diversity of real world cases. This can be achieved via instance selection (IS), which reduces size of a dataset by eliminating redundancies via outlier removal and clustering. Option two, a specific training scenario, describes training that is specialized based on IA features of a specific case, for which we present the novel reverse instance selection (RIS), which introduces similarity to the specific case to the IS methodology. We evaluated our IS and RIS by comparing them to subsets selected based on similarity (SIM) and random sampling (RndS). RIS outperformed SIM and RndS in 79% of experiments. IS outperformed RndS in 33% of experiments. In both scenarios, we observed that our approaches, which balance the weaknesses of SIM and RndS, perform best for small subset sizes close to the database cluster size. Our IS and RIS are flexible in regards to the underlying machine learning and weighting of metrics for evaluation, thus providing a way to select a representative and diverse subset not just for IAs, but also for different kinds of data.

Intracranial mirror aneurysms are pathologic dilatations of cerebral arteries that occur at identical locations in both hemispheres. They do not always have identical sizes, shapes, or rupture states, despite identical patient-specific risk factors. As a subgroup of multiple aneurysms and thus small amounts of data, clinical research about their causes and development has not been conclusive. Using eight patients with 16 mirror aneurysms with different rupture states, we investigated differences between the hemispheres. We virtually performed stitching: detaching the aneurysm domes to place them on the opposite parent vessel to assess the influence of aneurysm morphology and upstream parent vessel shape on hemodynamics. In the unaltered datasets, we found that blood flow within aneurysms in the left hemisphere has higher wall shear stress and velocity. After stitching, we found that both upstream parent vessel as well as aneurysm morphology influence hemodynamics. Morphologic parameters like size, undulation index, and aneurysm angle had a higher influence. In a short rupture risk analysis we found a correlation between rupture and aspect ratio and non-sphericity index. Overall, we found possible indicators for mirror aneurysm analysis that illustrated the importance of morphological analysis, though more datasets are necessary for conclusive statements about special treatment for mirror aneurysms.

During neuroradiological interventions such as treating intracranial aneurysms, neuroradiologists have to navigate a catheter in the 3D anatomy while watching a 2D projection of the vessels. To train this visuospatial ability, we present a virtual reality application where the user has to identify anatomically relevant positions on the Circle of Willis based on a 2D projection image. This image is generated based on a 3D model using a Fresnel shader. To increase motivation, which is an important aspect of training, we included several game elements. We qualitatively evaluated our application with two neuroradiologists. Although they rated our implementation of the task as difficult due to the small image section, both participants emphasized the importance of this training. Even small changes that have been suggested can adjust the difficulty, leading to an appropriate training that could be integrated into the curriculum. The results also indicate that the included game elements are motivating and that the application in general has a high usability. The proposed immersive virtual reality application provides training for visuospatial ability in a concrete medical context. Due to gamification, fun, and motivation are increased, which are two important factors of training affecting the frequency of use and outcome.

Attrition or dropout is the most severe missingness problem in longitudinal cohort study data where some participants do not show up for follow‐up examinations. Dropouts result in biased data and cause the reduction of 1ata set size. Moreover, they limit the power of statistical analysis and the validity of study findings. Visualization can play a strong role in analysing and displaying the missingness patterns. In this work, we present VIVID, a framework for the visual analysis of missing values in cohort study data. VIVID is inspired by discussions with epidemiologists and adds visual components to their current statistics‐based approaches. VIVID provides functions for exploration, imputation and validity check of imputations. The main focus of this paper is multiple imputation to fix the missing data. [ABSTRACT FROM AUTHOR]

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.

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.

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.

Simulations and measurements of blood and airflow inside the human circulatory and respiratory system play an increasingly important role in personalized medicine for prevention, diagnosis and treatment of diseases. This survey focuses on three main application areas. (1) Computational fluid dynamics (CFD) simulations of blood flow in cerebral aneurysms assist in predicting the outcome of this pathologic process and of therapeutic interventions. (2) CFD simulations of nasal airflow allow for investigating the effects of obstructions and deformities and provide therapy decision support. (3) 4D phase‐contrast (4D PC) magnetic resonance imaging of aortic haemodynamics supports the diagnosis of various vascular and valve pathologies as well as their treatment. An investigation of the complex and often dynamic simulation and measurement data requires the coupling of sophisticated visualization, interaction and data analysis techniques. In this paper, we survey the large body of work that has been conducted within this realm. We extend previous surveys by incorporating nasal airflow, addressing the joint investigation of blood flow and vessel wall properties and providing a more fine‐granular taxonomy of the existing techniques. From the survey, we extract major research trends and identify open problems and future challenges. The survey is intended for researchers interested in medical flow but also more general, in the combined visualization of physiology and anatomy, the extraction of features from flow field data and feature‐based visualization, the visual comparison of different simulation results and the interactive visual analysis of the flow field and derived characteristics. Simulations and measurements of blood and airflow inside the human circulatory and respiratory system play an increasingly important role in personalized medicine for prevention, diagnosis and treatment of diseases. This survey focuses on three main application areas. (1) Computational fluid dynamics (CFD) simulations of blood flow in cerebral aneurysms assist in predicting the outcome of this pathologic process and of therapeutic interventions. (2) CFD simulations of nasal airflow allow for investigating the effects of obstructions and deformities and provide therapy decision support. (3) 4D phase‐contrast (4D PC) magnetic resonance imaging of aortic haemodynamics supports the diagnosis of various vascular and valve pathologies as well as their treatment. An investigation of the complex and often dynamic simulation and measurement data requires the coupling of sophisticated visualization, interaction and data analysis techniques. [ABSTRACT FROM AUTHOR]