VCBM 2021: Eurographics Workshop on Visual Computing for Biology and Medicine

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AR-Assisted Craniotomy Planning for Tumour Resection
(The Eurographics Association, 2021) Wooning, Joost; Benmahdjoub, Mohamed; Walsum, Theo van; Marroquim, Ricardo; Oeltze-Jafra, Steffen and Smit, Noeska N. and Sommer, Björn and Nieselt, Kay and Schultz, Thomas
Craniotomy is a procedure where neurosurgeons open the patient's skull to gain direct access to the brain. The craniotomy's position defines the access path from the skull surface to the tumour and, consequently, the healthy brain tissue to be removed to reach the tumour. This is a complex procedure where a neurosurgeon is required to mentally reconstruct spatial relations of important brain structures to avoid removing them as much as possible. We propose a visualisation method using Augmented Reality to assist in the planning of a craniotomy. The goal of this study is to visualise important brain structures aligned with the physical position of the patient and to allow a better perception of the spatial relations of the structures. Additionally, a heat map was developed that is projected on top of the skull to provide a quick overview of the structures between a chosen location on the skull and the tumour. In the experiments, tracking accuracy was assessed, and colour maps were assessed for use in an AR device. Additionally, we conducted a user study amongst neurosurgeons and surgeons from other fields to evaluate the proposed visualisation using a phantom head. Most participants indeed agree that the visualisation can assist in planning a craniotomy and feedback on future improvements towards the clinical scenario was collected.
Automatic Animations to Analyze Blood Flow Data
(The Eurographics Association, 2021) Apilla, Vikram; Behrendt, Benjamin; Lawonn, Kai; Preim, Bernhard; Meuschke, Monique; Oeltze-Jafra, Steffen and Smit, Noeska N. and Sommer, Björn and Nieselt, Kay and Schultz, Thomas
We present an approach for computing camera animations composed of optimal views to support the visual exploration of blood flow data using cerebral aneurysms as major example. Medical researchers are interested in hemodynamic parameters and vessel wall characteristics. The time-dependent character of blood flow data complicates the visual analysis. Our approach is modeled as an optimization problem to automatically determine camera paths during the cardiac cycle. These consist of optimal viewpoints showing regions with suspicious characteristics of wall- and flow-related parameters. This provides medical researchers with an efficient method of obtaining a fast overview of patient-specific blood flow data.
Automatic Cutting and Flattening of Carotid Artery Geometries
(The Eurographics Association, 2021) Eulzer, Pepe; Richter, Kevin; Meuschke, Monique; Hundertmark, Anna; Lawonn, Kai; ,; Oeltze-Jafra, Steffen and Smit, Noeska N. and Sommer, Björn and Nieselt, Kay and Schultz, Thomas
We propose a novel method to cut and flatten vascular geometry that results in an intuitive mapping between the 3D and 2D domains. Our approach is fully automatic, and the sole input is the vessel geometry. We aim to simplify parameter analysis on vessel walls for research on vascular disease and computational hemodynamics. We present a use case for the flattening to aid efforts in investigating the pathophysiology of carotid stenoses (vessel constrictions that are a root cause of stroke). To achieve an intuitive mapping, we introduce the notion of natural vessel cuts. They remain on one side of vessel branches, meaning they adhere to the longitudinal direction and thus result in a comprehensible unfolding of the geometry. Vessel branches and endpoints are automatically detected, and a 2D layout configuration is found that retains the original branch layout. We employ a quasi-isometric surface parameterization to map the geometry to the 2D domain as a single patch. The flattened depiction resolves the need for tedious 3D interaction as the whole surface is visible at once.We found this overview particularly beneficial for exploring temporally resolved parameters.
Reducing Model Uncertainty in Crossing Fiber Tractography
(The Eurographics Association, 2021) Gruen, Johannes; Voort, Gemma van der; Schultz, Thomas; Oeltze-Jafra, Steffen and Smit, Noeska N. and Sommer, Björn and Nieselt, Kay and Schultz, Thomas
Diffusion MRI (dMRI) tractography permits the non-invasive reconstruction of major white matter tracts, and is therefore widely used in neurosurgical planning and in neuroscience. However, it is affected by various sources of uncertainty. In this work, we consider the model uncertainty that arises in crossing fiber tractography, from having to select between alternative mathematical models for the estimation of multiple fiber orientations in a given voxel. This type of model uncertainty is a source of instability in dMRI tractography that has not received much attention so far. We develop a mathematical framework to quantify it, based on computing posterior probabilities of competing models, given the local dMRI data. Moreover, we explore a novel strategy for crossing fiber tractography, which computes tracking directions from a consensus of multiple mathematical models, each one contributing with a weight that is proportional to its probability. Experiments on different white matter tracts in multiple subjects indicate that reducing model uncertainty in this way increases the accuracy of crossing fiber tractography.
The Role of Depth Perception in XR from a Neuroscience Perspective: A Primer and Survey
(The Eurographics Association, 2021) Hushagen, Vetle; Tresselt, Gustav C.; Smit, Noeska N.; Specht, Karsten; Oeltze-Jafra, Steffen and Smit, Noeska N. and Sommer, Björn and Nieselt, Kay and Schultz, Thomas
Augmented and virtual reality (XR) are potentially powerful tools for enhancing the efficiency of interactive visualization of complex data in biology and medicine. The benefits of visualization of digital objects in XR mainly arise from enhanced depth perception due to the stereoscopic nature of XR head mounted devices. With the added depth dimension, XR is in a prime position to convey complex information and support tasks where 3D information is important. In order to inform the development of novel XR applications in the biology and medicine domain, we present a survey which reviews the neuroscientific basis underlying the immersive features of XR. To make this literature more accessible to the visualization community, we first describe the basics of the visual system, highlighting how visual features are combined to objects and processed in higher cortical areas with a special focus on depth vision. Based on state of the art findings in neuroscience literature related to depth perception, we provide several recommendations for developers and designers. Our aim is to aid development of XR applications and strengthen development of tools aimed at molecular visualization, medical education, and surgery, as well as inspire new application areas.
Strategies for Generating Multi-Time Frame Localization of Cardiac MRI
(The Eurographics Association, 2021) Sabokrohiyeh, Samin; Ang, Kathleen; Samavati, Faramarz; Oeltze-Jafra, Steffen and Smit, Noeska N. and Sommer, Björn and Nieselt, Kay and Schultz, Thomas
4D Flow MRI is a recent promising technology that is able to capture blood flow information within the heart chambers over a cardiac cycle. To accurately study the flow inside the chambers, there is a need for a high quality anatomical reference which can be provided by another scan known as 3D cine MRI (short-axis 3D (multiple 2D slices) cine SSFP). To take advantage of both scans, data fusion can be done using an intensity-based registration. To reduce the impact of noise on the registration result and the chance of misalignment between the organs, defining a region of interest (localization) should be done prior to the registration. Localizing a dataset - especially a time-varying dataset - can be a daunting task since the localization should be provided for all time frames. We design and evaluate different strategies for extending single time frame localization to time varying data in order to register the 4D Flow MRI and 3D cine MRI over the cardiac cycle.
Visual Assessment of Growth Prediction in Brain Structures after Pediatric Radiotherapy
(The Eurographics Association, 2021) Magg, Caroline; Toussaint, Laura; Muren, Ludvig P.; Indelicato, Danny J.; Raidou, Renata Georgia; Oeltze-Jafra, Steffen and Smit, Noeska N. and Sommer, Björn and Nieselt, Kay and Schultz, Thomas
Pediatric brain tumor radiotherapy research is investigating how radiation influences the development and function of a patient's brain. To better understand how brain growth is affected by the treatment, the brain structures of the patient need to be explored and analyzed pre- and post-treatment. In this way, anatomical changes are observed over a long period and are assessed as potential early markers of cognitive or functional damage. In this early work, we propose an automated approach for the visual assessment of the growth prediction of brain structures in pediatric brain tumor radiotherapy patients. Our approach reduces the need for re-segmentation and the time required for it. We employ as a basis pre-treatment Computed Tomography (CT) scans with manual delineations (i.e., segmentation masks) of specific brain structures of interest. These pre-treatment masks are used as initialization, to predict the corresponding masks on multiple post-treatment follow-up Magnetic Resonance (MR) images, using an active contour model approach. For the accuracy quantification of the automatically predicted posttreatment masks, a support vector regressor (SVR) with features related to geometry, intensity, and gradients is trained on the pre-treatment data. Finally, a distance transform is employed to calculate the distances between pre- and post-treatment data and to visualize the predicted growth of a brain structure, along with its respective accuracy. Although segmentations of larger structures are more accurately predicted, the growth behavior of all structures is learned correctly, as indicated by the SVR results. This suggests that our pipeline is a positive initial step for the visual assessment of brain structure growth prediction.
Vologram: An Educational Holographic Sculpture for Volumetric Medical Data Physicalization
(The Eurographics Association, 2021) Pahr, Daniel; Wu, Hsiang-Yun; Raidou, Renata Georgia; Oeltze-Jafra, Steffen and Smit, Noeska N. and Sommer, Björn and Nieselt, Kay and Schultz, Thomas
Real-world sculptures that display patient imaging data for anatomical education purposes have seen a recent resurgence through the field of data physicalization. In this paper, we describe an automated process for the computer-assisted generation of sculptures that can be employed for anatomical education among the general population. We propose a workflow that supports non-expert users to generate and physically display volumetric medical data in a visually appealing and engaging way. Our approach generates slide-based, interactive sculptures-called volograms-that resemble holograms of underlying medical data. The volograms are made out of affordable and readily available materials (e.g., transparent foils and cardboard) and can be produced through commonly available means. To evaluate the educational value of the proposed approach with our target audience, we assess the volograms, as opposed to classical, on-screen medical visualizations in a user study. The results of our study, while highlighting current weaknesses of our physicalization, also point to interesting future directions.

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Browsing VCBM 2021: Eurographics Workshop on Visual Computing for Biology and Medicine by Subject "Life and medical sciences"

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