Browsing by Author "Vilanova, A."

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    ComVis‐Sail: Comparative Sailing Performance Visualization for Coaching
    (Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Pieras, M.; Marroquim, R.; Broekens, D.; Eisemann, E.; Vilanova, A.; Hauser, Helwig and Alliez, Pierre
    During training sessions, sailors rely on feedback provided by the coaches to reinforce their skills and improve their performance. Nowadays, the incorporation of sensors on the boats enables coaches to potentially provide more informed feedback to the sailors. A common exercise during practice sessions, consists of two boats of the same class, sailing side by side in a straight line with different boat handling techniques. Coaches try to understand which techniques are that make one boat go faster than the other. The analysis of the obtained data from the boats is challenging given its multi‐dimensional, time‐varying and spatial nature. At present, coaches only rely on aggregated statistics reducing the complexity of the data, hereby losing local and temporal information. We describe a new domain characterization and present a visualization design that allows coaches to analyse the data, structuring their analysis and explore the data from different perspectives. A central element of the tool is the glyph design to intuitively represent and aggregate multiple aspects of the sensor data. We have conducted multiple user studies with naive users, sailors and coaches to evaluate the design and potential of the overall tool.
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    Data Assimilation for Full 4D PC‐MRI Measurements: Physics‐Based Denoising and Interpolation
    (© 2020 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2020) de Hoon, N. H. L. C.; Jalba, A.C.; Farag, E.S.; van Ooij, P.; Nederveen, A.J.; Eisemann, E.; Vilanova, A.; Benes, Bedrich and Hauser, Helwig
    Phase‐Contrast Magnetic Resonance Imaging (PC‐MRI) surpasses all other imaging methods in quality and completeness for measuring time‐varying volumetric blood flows and has shown potential to improve both diagnosis and risk assessment of cardiovascular diseases. However, like any measurement of physical phenomena, the data are prone to noise, artefacts and has a limited resolution. Therefore, PC‐MRI data itself do not fulfil physics fluid laws making it difficult to distinguish important flow features. For data analysis, physically plausible and high‐resolution data are required. Computational fluid dynamics provides high‐resolution physically plausible flows. However, the flow is inherently coupled to the underlying anatomy and boundary conditions, which are difficult or sometimes even impossible to adequately model with current techniques. We present a novel methodology using data assimilation techniques for PC‐MRI noise and artefact removal, generating physically plausible flow close to the measured data. It also allows us to increase the spatial and temporal resolution. To avoid sensitivity to the anatomical model, we consider and update the full 3D velocity field. We demonstrate our approach using phantom data with various amounts of induced noise and show that we can improve the data while preserving important flow features, without the need of a highly detailed model of the anatomy.