Mathematik und Informatik

Prof. Dr. Lars Linsen, Institut für Informatik

Current PublicationsEvers, Marina; Derstroff, Adrian; Leistikow, Simon; Schneider, Tom; Mallepree, Larissa; Stampke, Jan; Leisgang, Moritz; Sprafke, Sebastian; Schuhl, Melina; Krefft, Niklas; Droese, Felix; Linsen, Lars Visual analytics of soccer player performance using objective ratings. Information Visualization Vol. 1–15, 2024 online
Evers, Marina; Böttinger, Michael; Linsen, Lars Interactive Visual Analysis of Regional Time Series Correlation in Multi-field Climate Ensembles. Workshop on Visualisation in Environmental Sciences (EnvirVis), 2023 online
Borrelli, Gabriel; Hagemann, Lars; Steinkühler, Jannik;Derstroff, Adrian;Evers, Marina;Huesmann, Karim;Leistikow, Simon;Rave, Hennes;Gol, Reyhaneh Sabbagh;Linsen, Lars 2022 IEEE Scientific Visualization Contest Winner: Multifield Analysis of Vorticity-Driven Lateral Spread in Wildfire Ensembles. IEEE Computer Graphics and Applications Vol. 44 (1), 2023 online
Evers M, Linsen L Multi-dimensional parameter-space partitioning of spatio-temporal simulation ensembles. Computers and Graphics Vol. 104, 2022 online
Evers M, Herick M, Molchanov V, Linsen L Coherent Topological Landscapes for Simulation Ensembles. Computer Vision, Imaging and Computer Graphics Theory and Applications, 2022, pp 223-237 online
Schwarz C, Buchholz R, Jawad M, Hoesker V, Terwesten-Solé C, Karst U, Linsen L, Vogl T, Hoerr V, Wildgruber M, Faber C Fingerprints of Element Concentrations in Infective Endocarditis Obtained by Mass Spectrometric Imaging and t-Distributed Stochastic Neighbor Embedding. ACS Infectious Diseases Vol. 8 (2), 2022, pp null online
Matute J., Linsen L. Evaluating Data-type Heterogeneity in Interactive Visual Analyses with Parallel Axes. Computer Graphics Forum Vol. 41 (1), 2022, pp 335-349 online
Nahardani, Ali; Leistikow, Simon; Grün, Katja; Krämer, Martin; Herrmann, Karl-Heinz; Schrepper, Andrea; Jung, Christian; Moradi, Sara; Schulze, Paul Christian; Linsen, Lars; Reichenbach, Jürgen R; Hoerr, Verena; Franz, Marcus Pulmonary Arteriovenous Pressure Gradient and Time-Averaged Mean Velocity of Small Pulmonary Arteries Can Serve as Sensitive Biomarkers in the Diagnosis of Pulmonary Arterial Hypertension: A Preclinical Study by 4D-Flow MRI. Diagnostics Vol. 12 (1), 2022 online
Rave H., Fincke J., Averkamp S., Tangerding B., Wehrenberg L.P., Gerrits T., Huesmann K., Leistikow S., Linsen L. Multifaceted Visual Analysis of Oceanographic Simulation Ensemble Data. IEEE Computer Graphics and Applications Vol. 42 (4), 2022, pp 80-88 online
Current ProjectsRetrospektive CT-Untersuchungen zur Schlüsselbeinossifikation - Entwicklung eines klinischen Entscheidungshilfesystems mit skalenbasierten Bewertungen und modernen Methoden des maschinellen Lernens zur Verbesserung der Gültigkeit und Zuverlässigkeit forensischer Altersbegutachtungen online
HiResHemo: Hemodynamics at High Spatio-temporal Resolution by Comparative Visual Analysis of 4D PC-MRI Data and CFD Simulation Ensembles

Advances in 4D phase-contrast magnetic resonance imaging (PC-MRI) allow for fast in-vivo measurements of unsteady blood flow in animals and humans. Still, the low spatio-temporal resolution, the low signal-to-noise ratio, and the uncertainty due to acceleration inhibit a proper analysis of the hemodynamic parameters which hamper their application as diagnostic markers in the clinical routine. Data-driven computational fluid dynamics (CFD) allow for simulated blood flow at high spatio-temporal resolution. Tuning the many simulation parameters to obtain a simulated blood flow that best matches the imaging data in an iterative procedure is a tedious task though and prone to detecting sub-optimal solutions that do not match the measured blood flow well enough. We propose to transform the task to a data analysis task by generating a simulation ensemble with many feasible parameter settings and analyzing the ensemble in comparison to the measured data. This approach requires multiple contributions: First, a fast reliable PC-MRI sequence will be developed to produce high-quality imaging data with low bias in-vitro and in-vivo in an appropriate measurement time. Second, an efficient data-driven CFD approach that allows for moving walls will be developed. Third, an effective analysis methodology for comparing measured and simulated data will be developed. To incorporate expert knowledge in the analysis process, we propose a user-centric approach that allows for interactive visual comparisons at a global level as well as in selected spatio-temporal regions of interest. Putting the three contributions together in a data assimilation process delivers a software tool to generate subject-specific blood flow fields from measured PC-MRI data at high spatio-temporal resolution. By compensating the limited spatio-temporal domains of MRI datasets it enables an accurate quantitative analysis of hemodynamic parameters. Using this software tool, we will provide a comprehensive description of the hemodynamic changes in the development and progression of atherosclerosis in a murine model.

CRC 1450 Z01 - Interactive and computational analysis of large multiscale imaging data The multiscale imaging strategy central to this initiative imposes novel data analysis challenges. The high complexity of the acquired data results from their nature of being volumetric, time-varying, large, multiscale, and forming cohorts. Meeting these challenges requires basic research in the fields of image analysis, machine learning, and visualization. Machine learning will be used to uncover inherent relationships between patterns at multiple scales. An interactive visual approach supports the user-centric analysis of detected features. The deliverable of this project will be generally applicable, effective, and efficient methods supporting the overall goal of multiscale data analysis. online
E-Maillinsen at uni-muenster dot de
Phone+49 251 83-32714
FAX+49 251 83-33755
Secretary   Sekretariat Sichma
Frau Katharina Sichma
Telefon +49 251 83-32700
Fax +49 251 83-33755
Zimmer 604b
AddressProf. Dr. Lars Linsen
Institut für Informatik
Fachbereich Mathematik und Informatik der Universität Münster
Einsteinstrasse 62
48149 Münster
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