Assessment of intracranial aneurysm neck deformation after contour deployment.

PURPOSE: The contour neurovascular system (CNS) is a novel device to treat intracranial wide-necked bifurcation aneurysms, with few studies assessing its long-term effects. Particularly its impact on aneurysm morphology has not been explored yet. We present a preliminary study to explore this impact for the first time, focusing on the neck curve and ostium of the aneurysm. METHODS: We investigated seven aneurysms treated with the CNS to assess ostium deformation after CNS deployment by comparing models extracted from in vivo medical pre-treatment and follow-up scans via morphological analysis. Time between pre- and follow-up scans was ten months on average. Size and shape indices like area, neck diameter, ellipticity index, undulation index, and more were assessed. RESULTS: Ostium size was reduced after treatment. On average, ostium area was reduced at a rate of - 0.58 (± 4.88) mm2 per year, from 15.52 (± 3.51) mm2 to 13.30 (± 2.27) mm2, and ostium width from 5.01 (± 0.54) mm to 4.49 (± 0.45) mm, with an average reduction of - 0.59 (± 0.87) mm. This shrinking positively correlated with time passing. Shape deformation was low, though notably mean ellipticity index was reduced by 0.06 (± 0.15) on average, indicating ostia were less elongated after treatment. CONCLUSION: We interpret the shrinking of the ostium as part of the healing process. Shape changes were found to be small enough to conclude no shape deformation of the ostium from CNS deployment, but the analysis of more cases with more parameters and information is necessary.

Application of immersive virtual reality for assessing chronic neglect in individuals with stroke: the immersive virtual road-crossing task.

BACKGROUND: Neglect can be a long-term consequence of chronic stroke that can impede an individual's ability to perform daily activities, but chronic and discrete forms can be difficult to detect. We developed and evaluated the "immersive virtual road-crossing task" (iVRoad) to identify and quantify discrete neglect symptoms in chronic stroke patients. METHOD: The iVRoad task requires crossing virtual intersections and placing a letter in a mailbox placed either on the left or right. We tested three groups using the HTC Vive Pro Eye: (1) chronic right hemisphere stroke patients with (N = 20) and (2) without (N = 20) chronic left-sided neglect, and (3) age and gender-matched healthy controls (N = 20). We analyzed temporal parameters, errors, and head rotation to identify group-specific patterns, and applied questionnaires to measure self-assessed pedestrian behavior and usability. RESULTS: Overall, the task was well-tolerated by all participants with fewer cybersickness-induced symptoms after the VR exposure than before. Reaction time, left-sided errors, and lateral head movements for traffic from left most clearly distinguished between groups. Neglect patients committed more dangerous crossings, but their self-rated pedestrian behavior did not differ from that of stroke patients without neglect. This demonstrates their reduced awareness of the risks in everyday life and highlights the clinical relevance of the task. CONCLUSIONS: Our findings suggest that a virtual road crossing task, such as iVRoad, has the potential to identify subtle symptoms of neglect by providing virtual scenarios that more closely resemble the demands and challenges of everyday life. iVRoad is an immersive, naturalistic virtual reality task that can measure clinically relevant behavioral variance and identify discrete neglect symptoms.

Surgical phase and instrument recognition: how to identify appropriate dataset splits.

PURPOSE: Machine learning approaches can only be reliably evaluated if training, validation, and test data splits are representative and not affected by the absence of classes. Surgical workflow and instrument recognition are two tasks that are complicated in this manner, because of heavy data imbalances resulting from different length of phases and their potential erratic occurrences. Furthermore, sub-properties like instrument (co-)occurrence are usually not particularly considered when defining the split. METHODS: We present a publicly available data visualization tool that enables interactive exploration of dataset partitions for surgical phase and instrument recognition. The application focuses on the visualization of the occurrence of phases, phase transitions, instruments, and instrument combinations across sets. Particularly, it facilitates assessment of dataset splits, especially regarding identification of sub-optimal dataset splits. RESULTS: We performed analysis of the datasets Cholec80, CATARACTS, CaDIS, M2CAI-workflow, and M2CAI-tool using the proposed application. We were able to uncover phase transitions, individual instruments, and combinations of surgical instruments that were not represented in one of the sets. Addressing these issues, we identify possible improvements in the splits using our tool. A user study with ten participants demonstrated that the participants were able to successfully solve a selection of data exploration tasks. CONCLUSION: In highly unbalanced class distributions, special care should be taken with respect to the selection of an appropriate dataset split because it can greatly influence the assessments of machine learning approaches. Our interactive tool allows for determination of better splits to improve current practices in the field. The live application is available at https://cardio-ai.github.io/endovis-ml/ .

Quantitative 4D flow MRI-derived thoracic aortic normal values of 2D flow MRI parameters in healthy volunteers.

PURPOSE: To utilize 4 D flow MRI to acquire normal values of "conventional 2 D flow MRI parameters" in healthy volunteers in order to replace multiple single 2 D flow measurements with a single 4 D flow acquisition. MATERIALS AND METHODS: A kt-GRAPPA accelerated 4 D flow sequence was used. Flow volumes were assessed by forward (FFV), backward (BFV), and net flow volumes (NFV) [ml/heartbeat] and flow velocities by axial (VAX) and absolute velocity (VABS) [m/s] in 116 volunteers (58 females, 43 ±â€Š13 years). The aortic regurgitant fraction (RF) was calculated. RESULTS: The sex-neutral mean FFV, BFV, NFV, and RF in the ascending aorta were 93.5 ±â€Š14.8, 3.6 ±â€Š2.8, 89.9 ±â€Š0.6 ml/heartbeat, and 3.9 ±â€Š2.9 %, respectively. Significantly higher values were seen in males regarding FFV, BFV, NFV and RF, but there was no sex dependency regarding VAX and VABS. The mean maximum VAX was lower (1.01 ±â€Š0.31 m/s) than VABS (1.23 ±â€Š0.35 m/s). We were able to determine normal ranges for all intended parameters. CONCLUSION: This study provides quantitative 4 D flow-derived thoracic aortic normal values of 2 D flow parameters in healthy volunteers. FFV, BFV, NFV, and VAX did not differ significantly from single 2 D flow acquisitions and could therefore replace time-consuming multiple single 2 D flow acquisitions. VABS should not be used interchangeably. KEY POINTS: · 4 D flow MRI can be used to replace 2 D flow MRI measurements.. · The parameter absolute velocities can be assessed by 4 D flow MRI.. · There are sex-dependent differences regarding forward, backward, net aortic blood flow and the aortic valve regurgitant fraction..

Comparison of aortic blood flow rotational direction in healthy volunteers and patients with bicuspid aortic valves using volumetric velocity-sensitive cardiovascular magnetic resonance imaging.

Background: The rotational direction (RD) of helical blood flow can be classified as either a clockwise (RD+) or counter-clockwise (RD-) flow. We hypothesized that this simple classification might not be sufficient for analysis in vivo and a simultaneous existence of RD+/- may occur. We utilized volumetric velocity-sensitive cardiovascular magnetic resonance imaging (4D flow MRI) to analyze rotational blood flow in the thoracic aorta. Methods: Forty volunteers (22 females; mean age, 41±16 years) and seventeen patients with bicuspid aortic valves (BAVs) (9 females; mean age, 42±14 years) were prospectively included. The RDs and the calculation of the rotating blood volumes (RBVs) in the thoracic aorta were performed using a pathline-projection strategy. Results: We could confirm a mainly clockwise RD in the ascending, descending aorta and in the aortic arch. Furthermore, we found a simultaneous existence of RD+/RD-. The RD+/--volume in the ascending aorta was significantly higher in BAV patients, the mean RD+/RD- percentage was approximately 80%/20% vs. 60%/40% in volunteers (P<0.01). The maximum RBV always occurred during systole. There was significantly more clockwise than counter-clockwise rotational flow in the ascending aorta (P<0.01) and the aortic arch (P<0.01), but no significant differences in the descending aorta (P=0.48). Conclusions: A simultaneous occurrence of RD+/RD- indicates that a simple categorization in either of both is insufficient to describe blood flow in vivo. Rotational flow in the ascending aorta and in the aortic arch differs significantly from flow in the descending aorta. BAV patients show significantly more clockwise rotating volume in the ascending aorta compared to healthy volunteers.

A Survey of Medical Visualization through the Lens of Metaphors.

We provide an overview of metaphors that were used in medical visualization and related user interfaces. Metaphors are employed to translate concepts from a source domain to a target domain. The survey is grounded in a discussion of metaphor-based design involving the identification and reflection of candidate metaphors. We consider metaphors that have a source domain in one branch of medicine, e.g., the virtual mirror that solves problems in orthopedics and laparoscopy with a mirror that resembles the dentist's mirror. Other metaphors employ the physical world as the source domain, such as crepuscular rays that inspire a solution for access planning in tumor therapy. Aviation is another source of inspiration, leading to metaphors, such as surgical cockpits, surgical control towers, and surgery navigation according to an instrument flight. This paper should raise awareness for metaphors and their potential to focus the design of computer-assisted systems on useful features and a positive user experience. Limitations and potential drawbacks of a metaphor-based user interface design for medical applications are also considered.

Prognostic role of radiomics-based body composition analysis for the 1-year survival for hepatocellular carcinoma patients.

BACKGROUND: Parameters of body composition have prognostic potential in patients with oncologic diseases. The aim of the present study was to analyse the prognostic potential of radiomics-based parameters of the skeletal musculature and adipose tissues in patients with advanced hepatocellular carcinoma (HCC). METHODS: Radiomics features were extracted from a cohort of 297 HCC patients as post hoc sub-study of the SORAMIC randomized controlled trial. Patients were treated with selective internal radiation therapy (SIRT) in combination with sorafenib or with sorafenib alone yielding two groups: (1) sorafenib monotherapy (n = 147) and (2) sorafenib and SIRT (n = 150). The main outcome was 1-year survival. Segmentation of muscle tissue and adipose tissue was used to retrieve 881 features. Correlation analysis and feature cleansing yielded 292 features for each patient group and each tissue type. We combined 9 feature selection methods with 10 feature set compositions to build 90 feature sets. We used 11 classifiers to build 990 models. We subdivided the patient groups into a train and validation cohort and a test cohort, that is, one third of the patient groups. RESULTS: We used the train and validation set to identify the best feature selection and classification model and applied it to the test set for each patient group. Classification yields for patients who underwent sorafenib monotherapy an accuracy of 75.51% and area under the curve (AUC) of 0.7576 (95% confidence interval [CI]: 0.6376-0.8776). For patients who underwent treatment with SIRT and sorafenib, results are accuracy = 78.00% and AUC = 0.8032 (95% CI: 0.6930-0.9134). CONCLUSIONS: Parameters of radiomics-based analysis of the skeletal musculature and adipose tissue predict 1-year survival in patients with advanced HCC. The prognostic value of radiomics-based parameters was higher in patients who were treated with SIRT and sorafenib.

A teaching concept for school experiments on radioactivity using augmented reality methods.

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/.

Multimodal exploration of the intracranial aneurysm wall.

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.

Deep learning-based semantic vessel graph extraction for intracranial aneurysm rupture risk management.

PURPOSE: Intracranial aneurysms are vascular deformations in the brain which are complicated to treat. In clinical routines, the risk assessment of intracranial aneurysm rupture is simplified and might be unreliable, especially for patients with multiple aneurysms. Clinical research proposed more advanced analysis of intracranial aneurysm, but requires many complex preprocessing steps. Advanced tools for automatic aneurysm analysis are needed to transfer current research into clinical routine. METHODS: We propose a pipeline for intracranial aneurysm analysis using deep learning-based mesh segmentation, automatic centerline and outlet detection and automatic generation of a semantic vessel graph. We use the semantic vessel graph for morphological analysis and an automatic rupture state classification. RESULTS: The deep learning-based mesh segmentation can be successfully applied to aneurysm surface meshes. With the subsequent semantic graph extraction, additional morphological parameters can be extracted that take the whole vascular domain into account. The vessels near ruptured aneurysms had a slightly higher average torsion and curvature compared to vessels near unruptured aneurysms. The 3D surface models can be further employed for rupture state classification which achieves an accuracy of 83.3%. CONCLUSION: The presented pipeline addresses several aspects of current research and can be used for aneurysm analysis with minimal user effort. The semantic graph representation with automatic separation of the aneurysm from the parent vessel is advantageous for morphological and hemodynamical parameter extraction and has great potential for deep learning-based rupture state classification.

A hybrid hierarchical strategy for registration of 7T TOF-MRI to 7T PC-MRI intracranial vessel data.

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.

GUCCI - Guided Cardiac Cohort Investigation of Blood Flow Data.

We present the framework GUCCI (Guided Cardiac Cohort Investigation), which provides a guided visual analytics workflow to analyze cohort-based measured blood flow data in the aorta. In the past, many specialized techniques have been developed for the visual exploration of such data sets for a better understanding of the influence of morphological and hemodynamic conditions on cardiovascular diseases. However, there is a lack of dedicated techniques that allow visual comparison of multiple data sets and defined cohorts, which is essential to characterize pathologies. GUCCI offers visual analytics techniques and novel visualization methods to guide the user through the comparison of predefined cohorts, such as healthy volunteers and patients with a pathologically altered aorta. The combination of overview and glyph-based depictions together with statistical cohort-specific information allows investigating differences and similarities of the time-dependent data. Our framework was evaluated in a qualitative user study with three radiologists specialized in cardiac imaging and two experts in medical blood flow visualization. They were able to discover cohort-specific characteristics, which supports the derivation of standard values as well as the assessment of pathology-related severity and the need for treatment.

Interactive visual exploration of surgical process data.

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.

Deep-learning-based Segmentation of Skeletal Muscle Mass in Routine Abdominal CT Scans.

BACKGROUND: For prediction of many types of clinical outcome, the skeletal muscle mass can be used as an independent biomarker. Manual segmentation of the skeletal muscles is time-consuming, therefore we present a deeplearning-based approach for the identification of muscle mass at the L3 level in clinical routine computed tomographic (CT) data. PATIENTS AND METHODS: We conducted a retrospective study of 130 patient datasets. Individual CT slice analysis at the L3 level was fed into a U-Net architecture. As a result, we obtained segmentations of the musculus rectus abdominis, abdominal wall muscles, musculus psoas major, musculus quadratus lumborum and musculus erector spinae in the CT-slice at the L3 level. RESULTS: The Dice score was 0.95±0.02, 0.86±0.12, 0.93±0.05, 0.92±0.05, 0.86±0.08 for the erector spine, rectus, abdominal wall, psoas and quadratus lumborum muscles, respectively. For the overall skeletal muscle mass, the test data achieved a Dice score of 0.95±0.03. CONCLUSION: Our network achieved Dice scores larger than 0.86 for each of the five different muscle types and 0.95 for the overall skeletal muscle mass. The subdivision of muscle types can serve as a basis for obtaining future biomarkers. Our network is publicly available so that it might be beneficial for others to improve the clinical workflow within examination of routine CT scans.

Quantitative normal values of helical flow, flow jets and wall shear stress of healthy volunteers in the ascending aorta.

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.

Self-calibration of C-arm imaging system using interventional instruments during an intracranial biplane angiography.

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 [Formula: see text] mm and [Formula: see text] mm, respectively. The mean and standard deviation of the 3D root-mean-square error (RMSE) for some markers in the phantom reduced from [Formula: see text] to [Formula: see text] 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.

A comparison of input devices for precise interaction tasks in VR-based surgical planning and training.

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.

VR-based training of craniotomy for intracranial aneurysm surgery.

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.

Virtual embolization for treatment support of intracranial AVMs using an interactive desktop and VR application.

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.

Ten Open Challenges in Medical Visualization.

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.