Utility of catheter-shaping using mixed-reality devices in cerebral aneurysm coil embolization.

BACKGROUND: Catheter shaping is vital in cerebral aneurysm coil embolization; however, understanding three-dimensional (3D) vascular structures on two-dimensional screens is challenging. Although 3D-printed vascular models are helpful, they demand time, effort, and sterility. This study explores whether mixed-reality (MR) devices displaying 3D computer graphics (3D-CG) can address these issues. METHODS: This study focused on magnetic resonance imaging (MRI) of seven cases of cerebral aneurysms. Head-mounted display (HMD) and spatial reality display (SRD) MR devices were used, and applications for 3D-CG display at a 1:1 scale and a 3D-CG control panel were developed. Catheters shaped using a 3D printer, HMD, and SRD were inserted into hollow models to assess their accessibility and positioning. RESULTS: The concordance rate of the 3D printer and HMD groups in terms of accessibility to the aneurysm was 71.4 %, while that of the 3D printer and SRD group was 85.7 %, and that of the HMD and SRD group was 85.7 %. The concordance rates of positioning in the 3D printer and HMD groups, 3D printer and SRD groups, and HMD and SRD groups were 85.7 %, 85.7 %, and 100 %, respectively. CONCLUSIONS: MR devices facilitate catheter shaping in cerebral aneurysm coil embolization and offer a time-efficient, precise, and sterile alternative to traditional 3D printing methods.

Evaluating the feasibility of using augmented reality for tooth preparation.

OBJECTIVES: Tooth preparation is complicated because it requires the preparation of an abutment while simultaneously predicting the ideal shape of the tooth. This study aimed to develop and evaluate a system using augmented reality (AR) head-mounted displays (HMDs) that provide dynamic navigation capabilities for tooth preparation. METHODS: The proposed system utilizes optical see-through HMDs to overlay digital information onto the real world and enrich the user's environment. By integrating tracking algorithms and three-dimensional modeling, the system provides real-time visualization and navigation capabilities during tooth preparation by using two different visualization techniques. The experimental setup involved a comprehensive analysis of the distance to the surface and cross-sectional angles between the ideal and prepared teeth using three scenarios: traditional (without AR), overlay (AR-assisted visualization of the ideal prepared tooth), and cross-sectional (AR-assisted visualization with cross-sectional views and angular displays). RESULTS: A user study (N = 24) revealed that the cross-sectional approach was more effective for angle adjustment and reduced the occurrence of over-reduction. Additional questionnaires revealed that the AR-assisted approaches were perceived as less difficult, with the cross-sectional approach excelling in terms of performance. CONCLUSIONS: Visualization and navigation using cross-sectional approaches have the potential to support safer tooth preparation with less overreduction than traditional and overlay approaches do. The angular displays provided by the cross-sectional approach are considered helpful for tooth preparation. CLINICAL SIGNIFICANCE: The AR navigation system can assist dentists during tooth preparation and has the potential to enhance the accuracy and safety of prosthodontic treatment.

The Visualization of Intraneural Venous Compression in Trigeminal Neuralgia by Using Advanced Three-Dimensional Computer Graphics: An Illustrative Case Report.

Trigeminal neuralgia (TN) caused by venous compression presents challenges in surgical management, unlike the arterial type. Preoperative diagnostic certainty regarding venous etiology and anatomical relationships is crucial for surgical success. We discuss a case of TN caused by a vein passing through the nerve that was challenging to visualize on conventional MRI and was treated successfully by leveraging information from modern surgical simulation technology with 3D computer graphics. We recognized a potentially troublesome anatomical feature in advance and mitigated the risk by identifying a collateral drainage route for the causative vein, making it feasible to be sacrificed while ensuring treatment efficacy.

Endoscopic Endonasal Transsphenoidal Surgery for Intrasellar Mixed Germ Cell Tumors.

A mixed germ cell tumor (MGCT) in the neurohypophysis is very rare, with only a few reported cases1-4 but none with surgical videos. In this report, the endoscopic endonasal transsphenoidal approach for MGCT in the neurohypophysis is presented (Video 1). A 12-year-old girl with ocular pain, fatigue, and nausea presented with gradual onset of quadrant hemianopsia and left oculomotor palsy. Magnetic resonance imaging showed an enhanced mass in the sella turcica with multiple components involving the pituitary gland and stalk. Her endocrinological examination showed decreased levels of pituitary hormones and simultaneously elevated serum levels of alpha-fetoprotein and beta-human chorionic gonadotropin. After pituitary hormone replacement, endoscopic endonasal transsphenoidal surgery was planned. The tumor was strongly adherent to the surrounding structures, and gross total resection was achieved. The histological diagnosis was MGCT with a teratoma component. Postoperatively, her vision and oculomotor palsy improved swiftly, and adjuvant chemotherapy and radiotherapy were administered. In this case, 3-dimensional computer graphics were created from the preoperative computed tomography and magnetic resonance imaging studies. Preoperative simulation with the 3-dimensional computer graphic images and intraoperative verification with indocyanine green images facilitated our understanding of the surrounding anatomy, including the tumor components, pituitary gland, and internal carotid arteries.5 After removal of the tumor, multilayer fascial closure was performed for skull base reconstruction.6 MGCT in the neurohypophysis can be strongly adherent to the surrounding structures, requiring careful dissection and resection under endoscopy. At the last follow-up (8 months after surgery), the tumor was successfully controlled, and the patient had no neurological symptoms with pituitary hormone replacement therapy.

From complex data to clear insights: visualizing molecular dynamics trajectories.

Advances in simulations, combined with technological developments in high-performance computing, have made it possible to produce a physically accurate dynamic representation of complex biological systems involving millions to billions of atoms over increasingly long simulation times. The analysis of these computed simulations is crucial, involving the interpretation of structural and dynamic data to gain insights into the underlying biological processes. However, this analysis becomes increasingly challenging due to the complexity of the generated systems with a large number of individual runs, ranging from hundreds to thousands of trajectories. This massive increase in raw simulation data creates additional processing and visualization challenges. Effective visualization techniques play a vital role in facilitating the analysis and interpretation of molecular dynamics simulations. In this paper, we focus mainly on the techniques and tools that can be used for visualization of molecular dynamics simulations, among which we highlight the few approaches used specifically for this purpose, discussing their advantages and limitations, and addressing the future challenges of molecular dynamics visualization.

Real-time soft body dissection simulation with parallelized graph-based shape matching on GPU.

BACKGROUND AND OBJECTIVE: Interactive soft tissue dissection has been a fundamental procedure in virtual surgery systems. Existing cutting algorithms involve complex topology changes of simulation meshes, which can increase simulation overhead and produce visual artifacts. In this paper, we proposed a novel graph-based shape-matching method that allows for real-time, flexible, progressive, and discontinuous cuts on soft tissue. METHODS: We employed shape-matching constraints within the position-based dynamics (PBD) framework, a widely adopted approach for real-time simulation applications. The soft tissue was effectively modeled using overlapping clusters, each governed by shape-matching constraints. The dissection process was bifurcated into two distinct stages. In the first stage, the surgical scalpel presses the surface of the soft tissue. The soft tissue is cut apart when the surface pressure exceeds a threshold, entering the second stage. To address the discrepancy between the visual mesh and the simulation model during cluster separation, we developed an Aggregate Finding Connected Components (AFCC) algorithm, optimized for GPU computation and integrated with a background grid. This approach also avoids ghost forces and fragmentation artifacts. To control the increase in the number of clusters, we also propose a merging strategy that can run in parallel. RESULTS: Our simulation outcomes demonstrated that the AFCC dissection algorithm effectively manages cluster separation and expansion with robustness. There were no ghost forces between the cutting surface and unrealistic fragments. Our simulation capability extended to supporting intricate and discontinuous cutting routes. Our dissection simulation maintained real-time performance even with over 100,000 particles constituting the soft tissue. CONCLUSIONS: Our real-time and robust surgical dissection simulation technique enables the performance of complex cuts in various surgical scenarios, demonstrating its potential in virtual surgery applications.

Render lighting dataset: A collection of rendered images with varied lighting conditions using blender render engines.

The quality of datasets is crucial in computer graphics and machine learning research and development. This paper presents the Render Lighting Dataset, featuring 63,648 rendered images of Blender's primitive shapes with various lighting conditions and engines. The images were created using Blender 4.0's Cycles and Eevee Render Engines, with careful attention to detail in texture mapping and UV unwrapping. The dataset covers six different lighting conditions, including Area Light, Spotlight, Point Light, Tri-Light, HDRI (Sunlight), and HDRI (Overcast), each adjusted using Blender's different options in the Color Management panel. With thirteen unique materials, ranging from Coastal Sand to Glossy Plastic, the dataset provides visual diversity for researchers to explore material properties under different lighting conditions using different render engines. This dataset serves as a valuable resource for researchers looking to enhance 3D rendering engines. Its diverse set of rendered images under varied lighting conditions and material properties allows researchers to benchmark and evaluate the performance of different rendering engines, develop new rendering algorithms and techniques, optimize rendering parameters, and understand rendering challenges. By enabling more realistic and efficient rendering, advancing research in lighting simulation, and facilitating the development of AI-driven rendering techniques, this dataset has the potential to shape the future of computer graphics and rendering technology.

A Concise Review of Biomolecule Visualization.

The structural characteristics of biomolecules are a major focus in the field of structural biology. Molecular visualization plays a crucial role in displaying structural information in an intuitive manner, aiding in the understanding of molecular properties. This paper provides a comprehensive overview of core concepts, key techniques, and tools in molecular visualization. Additionally, it presents the latest research findings to uncover emerging trends and highlights the challenges and potential directions for the development of the field.

Saliency detection of textured 3D models based on multi-view information and texel descriptor.

Saliency-driven mesh simplification methods have shown promising results in maintaining visual detail, but effective simplification requires accurate 3D saliency maps. The conventional mesh saliency detection method may not capture salient regions in 3D models with texture. To address this issue, we propose a novel saliency detection method that fuses saliency maps from multi-view projections of textured models. Specifically, we introduce a texel descriptor that combines local convexity and chromatic aberration to capture texel saliency at multiple scales. Furthermore, we created a novel dataset that reflects human eye fixation patterns on textured models, which serves as an objective evaluation metric. Our experimental results demonstrate that our saliency-driven method outperforms existing approaches on several evaluation metrics. Our method source code can be accessed at https://github.com/bkballoon/mvsm-fusion and the dataset can be accessed at 10.5281/zenodo.8131602.

Weathering Effects on Concrete Objects Based on 3D Scanned Examples and Geometric Features.

In this paper, we introduce a method for simulating the deformation of concrete surfaces due to weathering employing an example-based approach to replicate shape changes observed in real-world objects. A key challenge in implementing this approach is the scarcity of opportunities to measure shapes both before and after the weathering process. To overcome this limitation, we utilize concrete bricks collected from real-world environments as standardized examples, allowing for an analysis of erosion. By measuring erosion based on the estimated original shape, we correlate the characteristics of erosion with geometric features such as curvature and accessibility. We then apply this analysis to simulate new weathering effects in a given input model in alignment with its own geometric features. Our method yields visually compelling results while reproducing the variation of geometric weathering effects.

Study on the Fractal Characteristics and Seepage Properties of Channels Filled by Coal Particles.

Studying the seepage process in fracture channels (where coal particles are deposited) is of great significance for improving the performance of both on-site coal seam water injection and dust reduction technology. Through a self-developed simulation experiment of water-borne coal particle migration and accumulation and computer graphics, we investigated the influencing factors of particle accumulation in water injection and their influence law on seepage, discussed the interaction relationship between the fractal structure of coal and the characteristics of accumulated coal particles, and established a new fractal model of fracture permeability based on different particle accumulation states. The results show that the seepage velocity and the particle size jointly affect the migration and accumulation process of water-borne coal particles. When the coal particle size is constant and the seepage velocity increases, then the output of the coal powder increases, the deposition decreases, and the structure fractal dimension D3 of fractures decreases. At the same seepage velocity, with the increase of the coal particle size, the output of coal powder decreases, the deposition increases, and the structure fractal dimension D3 of fractures increases. In addition, the amount of coal powder produced in the intermittent water injection process is smaller than that produced in the continuous water injection process, more easily leading to accumulation. The variation law of the theoretical permeability with porosity remains consistent for different particle accumulation states: with the increase of porosity, the structure fractal dimension D3 of fractures decreases, while the theoretical permeability increases. The above research results can provide a theoretical basis for reducing the seepage damage of coal under the particle blocking effect.

Image classification adversarial attack with improved resizing transformation and ensemble models.

Convolutional neural networks have achieved great success in computer vision, but incorrect predictions would be output when applying intended perturbations on original input. These human-indistinguishable replicas are called adversarial examples, which on this feature can be used to evaluate network robustness and security. White-box attack success rate is considerable, when already knowing network structure and parameters. But in a black-box attack, the adversarial examples success rate is relatively low and the transferability remains to be improved. This article refers to model augmentation which is derived from data augmentation in training generalizable neural networks, and proposes resizing invariance method. The proposed method introduces improved resizing transformation to achieve model augmentation. In addition, ensemble models are used to generate more transferable adversarial examples. Extensive experiments verify the better performance of this method in comparison to other baseline methods including the original model augmentation method, and the black-box attack success rate is improved on both the normal models and defense models.

A new group-based online job interview training program using computer graphics robots for individuals with autism spectrum disorders.

Introduction: Job interviews are a major barrier to employment for individuals with autism spectrum disorders (ASD). During the coronavirus pandemic, establishing online job interview training at home was indispensable. However, many hurdles prevent individuals with ASD from concentrating on online job interview training. To facilitate the acquisition of interview skills from home for individuals with ASD, we developed a group interview training program with a virtual conferencing system (GIT-VICS Program) that uses computer graphics (CG) robots. Methods: This study investigated the feasibility of the GIT-VICS Program in facilitating skill acquisition for face-to-face job interviews in pre-post measures. In the GIT-VICS Program, five participants were grouped and played the roles of interviewees (1), interviewers (2), and human resources (2). They alternately practiced each role in GIT-VICS Program sessions conducted over 8 or 9 days over three consecutive weeks. Before and after the GIT-VICS Program, the participants underwent a mock face-to-face job interview with two experienced human interviewers (MFH) to evaluate its effect. Results: Fourteen participants completed the trial procedures without experiencing any technological challenges or distress that would have led to the termination of the session. The GIT-VICS Program improved their job interview skills (verbal competence, nonverbal competence, and interview performance). Discussion: Given the promising results of this study and to draw clear conclusions about the efficacy of CG robots for mock online job interview training, future studies adding appropriate guidance for manner of job interview by experts are needed.

Manipulating Pixels in Computer Graphics by Converting Raster Elements to Vector Shapes as a Function of Hue.

This paper proposes a method for changing pixel shape by converting a CMYK raster image (pixel) to an HSB vector image, replacing the square cells of the CMYK pixels with different vector shapes. The replacement of a pixel by the selected vector shape is done depending on the detected color values for each pixel. The CMYK values are first converted to the corresponding RGB values and then to the HSB system, and the vector shape is selected based on the obtained hue values. The vector shape is drawn in the defined space, according to the row and column matrix of the pixels of the original CMYK image. Twenty-one vector shapes are introduced to replace the pixels depending on the hue. The pixels of each hue are replaced by a different shape. The application of this conversion has its greatest value in the creation of security graphics for printed documents and the individualization of digital artwork by creating structured patterns based on the hue.

Simulation experiment of Internet online art design teaching based on computer image segmentation and cluster algorithm.

With the continuous development of the economy, people pay more attention to spiritual needs, so the cultural industry has developed by leaps and bounds. The development of Chinese culture and art is transforming toward internationalization and industrialization, and new art forms and styles are constantly emerging. In addition, China's online education is currently in a critical period of transformation. The form of online education is comprehensively deepened, first of all in the field of basic vocational education. The second is the training of students in basic education. At present, the art design teaching courses in Chinese schools have not been fully digitized, and they are faced with the problems of outdated teaching materials, slow updating speed and high purchase cost. Based on this, this study designs an online art teaching system by introducing computer image segmentation technology. Through analysis, it can be concluded that the core algorithm of the system is superior to FCM, FCM_S1 and FCM_S2, etc., and is excellent in division entropy, coefficient and accuracy, and can fully drive the system designed in this paper. The main functional modules of the system include learning guidance, course guidance and teaching resources. Through the simulation experiment, the online art design teaching system of the Internet can fully meet the needs of many students to access through the intranet at the same time, and the external network can meet 500 concurrent users without pressure. So, the overall system has good performance. This paper introduces the computer image segmentation technology into the field of art design teaching and designs a kind of effective Internet online system.

Evaluation of the cumulative Cherenkov converted dose on TSET patients with multiple Cherenkov cameras.

Cherenkov images can be used for the quality assurance of dose homogeneity in total skin electron therapy (TSET). For the dose mapping purpose, this study reconstructed the patient model from 3D scans using registration algorithms and computer animation techniques. The Cherenkov light emission of the patient's surface was extracted from multi-view Cherenkov images, converted into dose distribution, and projected onto the patient's 3D model, allowing for dose cumulation and evaluation. The projected result from multiple Cherenkov cameras provides additional information about Cherenkov emission on the sides of the patients, which improves the agreement between the Cherenkov converted dose and the OSLD measurements.

Observation-driven generation of texture maps depicting dust accumulation over time.

The perception of realism in computer-generated images can be significantly enhanced by subtle visual cues. Among those, one can highlight the presence of dust on synthetic objects, which is often subject to temporal variations in real settings. In this paper, we present a framework for the generation of textures representing the accumulation of this ubiquitous material over time in indoor settings. It employs a physically inspired approach to portray the effects of different levels of accumulated dust roughness on the appearance of substrate surfaces and to modulate these effects according to the different illumination and viewing geometries. The development of its core algorithms was guided by empirical insights and data obtained from observational experiments which are also described. To illustrate its applicability to the rendering of visually plausible depictions of time-dependent changes in dusty scenes, we provide sequences of images obtained considering distinct dust accumulation scenarios.

A brain flatmap data visualization tool for mouse, rat, and human.

Here we provide open-access brain data flatmap visualization and analysis tools for the mouse, rat, and human. The present work stems from a previous JCN Toolbox article that introduced a novel flatmap of the mouse brain and substantially enhanced flatmaps of the rat and human brain. These brain flatmap data visualization tools enable computer-generated graphical flatmap representation of tabulated user-entered data. For mouse and rat, they are designed to accommodate data resolved spatially up to the level of gray matter regions, supported by parcellation and nomenclature defined in current brain reference atlases. For human, Brodmann cerebral cortical parcellation is emphasized, and all other major brain divisions are represented. A comprehensive user guide is included along with several use examples. These brain data visualization tools enable the tabulation and automatic graphical flatmap representation of any type of mouse, rat, or human brain data that is spatially localized. The formalized presentation afforded by these graphical tools facilitates comparative analysis between data sets within or between the represented species.

VICO-DR: A Collaborative Virtual Dressing Room for Image Consulting.

In recent years, extended reality has increasingly been used to enhance the shopping experience for customers. In particular, some virtual dressing room applications have begun to develop, as they allow customers to try on digital clothes and see how they fit. However, recent studies found that the presence of an AI or a real shopping assistant could improve the virtual dressing room experience. In response to this, we have developed a collaborative synchronous virtual dressing room for image consulting that allows customers to try on realistic digital garments chosen by a remotely connected human image consultant. The application has different features for the image consultant and the customer. The image consultant can connect to the application, define a database of garments, select different outfits with different sizes for the customer to try, and communicate with the customer through a single RGB camera system. The customer-side application can visualize the description of the outfit that the avatar is wearing, as well as the virtual shopping cart. The main purpose of the application is to offer an immersive experience, ensured by the presence of a realistic environment, an avatar that resembles the customer, a real-time physically-based cloth simulation algorithm, and a video-chat system.

Numerical instability of Hill-type muscle models.

Hill-type muscle models are highly preferred as phenomenological models for musculoskeletal simulation studies despite their introduction almost a century ago. The use of simple Hill-type models in simulations, instead of more recent cross-bridge models, is well justified since computationally 'light-weight'-although less accurate-Hill-type models have great value for large-scale simulations. However, this article aims to invite discussion on numerical instability issues of Hill-type muscle models in simulation studies, which can lead to computational failures and, therefore, cannot be simply dismissed as an inevitable but acceptable consequence of simplification. We will first revisit the basic premises and assumptions on the force-length and force-velocity relationships that Hill-type models are based upon, and their often overlooked but major theoretical limitations. We will then use several simple conceptual simulation studies to discuss how these numerical instability issues can manifest as practical computational problems. Lastly, we will review how such numerical instability issues are dealt with, mostly in an ad hoc fashion, in two main areas of application: musculoskeletal biomechanics and computer animation.