Dirk-Bartz-Prize 2025

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https://diglib.eg.org/handle/10.2312/3607196

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    EuroVis 2025 Dirk Bartz Prize: Frontmatter
    (The Eurographics Association, 2025) Meuschke, Monique; Kuhlen, Torsten W.; Meuschke, Monique; Kuhlen, Torsten W.
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    Too Heart to Handle? Exploring Self-Directed And Collaborative Virtual Learning Environments in Anatomy Education.
    (The Eurographics Association, 2025) Schott, Danny; Kunz, Matthias; Albrecht, Anne; Braun-Dullaeus, Rüdiger; Hansen, Christian; Meuschke, Monique; Kuhlen, Torsten W.
    The integration of Extended Reality (XR) into medical education represents a transformative shift, particularly in anatomy training, where immersive simulations enhance cognitive engagement and knowledge retention. The developing heart is characterized by rapid morphological changes within a short time frame, which poses a significant pedagogical challenge. Conventional 2D imaging and static models often fail to convey these processes, limiting learners' ability to conceptualize critical spatial relationships-a barrier in understanding congenital anomalies. To address these limitations, this work leverages XRdriven visualization and interaction paradigms to create virtual learning environments. Based on this, we propose methods for designing XR educational modules that adapt to both collaborative and self-directed learning contexts, using embryonic cardiogenesis as an illustrating case study. We present findings from mixed-methods user studies involving a total of 264 students, along with feedback from lecturers, highlighting the importance of an iterative, user-centered design approach.
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    Visual Disease Stories: Empowering Health Literacy and Promotion
    (The Eurographics Association, 2025) Mittenentzwei, Sarah; Mlitzke, Sophie; Budich, Beatrice; Kleinau, Anna; Preim, Bernhard; Meuschke, Monique; Meuschke, Monique; Kuhlen, Torsten W.
    Narrative medical visualization bridges the gap between limited health literacy and the need for health communication. We explore disease stories to promote preventive behaviors, educate patients about their conditions and treatment options to foster informed consent, which is embraced by clinicians for its potential to alleviate the burden on healthcare systems and providers. We integrate data visualization with storytelling to make scientific information more accessible to diverse audiences. However, the expansive design space presents significant challenges, as design choices can greatly influence levels of engagement, memorability, and comprehension. Our work investigates the fundamental dimensions of the narrative design space - conflict, content, character, and structure - with the aim of maximizing its impact on disease communication.
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    X-ray simulations with gVirtualXray in medicine and life sciences
    (The Eurographics Association, 2025) Vidal, Franck P.; Afshari, Shaghayegh; Albiol, Alberto; Albiol, Francisco; Bellot, Alberto Corbí; Brun, Anna Louise; Chou, Chengy-Ying; Desbarats, Pascal; García, Marcos; Giovannelli, Jean-Francois; Hatton, Clémentine; Henry, Audrey; Kelly, Graham; Michelet, Claire; Mihail, Radu P.; Racy, Malek; Rouwane, Ali; Seznec, Herve; Sújar, Aarón; Tugwell-Allsup, Jenna; Villard, Pierre-Frédéric; Meuschke, Monique; Kuhlen, Torsten W.
    gVirtualXray (gVXR) is a programming interface framework to simulate realistic X-ray projections in realtime on graphics processing units (GPUs). It solves the Beer-Lambert law (attenuation law) using a deterministic X-ray simulation algorithm based on 3D computer graphics, namely rasterisation. Implemented as multi-pass rendering makes it more computationally optimal than the ray-tracing technique, which is a brute-force and straightforward approach to simulate X-ray images. Although written in C++ using OpenGL and its shading language (GLSL) to leverage the GPU, gVXR is available for other programming languages such as Python. Extensive validation studies, including comparisons with Monte Carlo simulations and real experimental data, have confirmed the accuracy of gVXR's simulations. gVXR was initially used in medical virtual reality (VR) for training purposes. It was then used in medical physics, and high-throughput data applications including mathematical optimisation and machine learning (ML). Micro-imaging studies on the C. elegans biological model are also reported.