Browsing by Author "Gutberlet, Matthias"

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    2.5D Geometric Mapping of Aortic Blood Flow Data for Cohort Visualization
    (The Eurographics Association, 2021) Behrendt, Benjamin; Pleuss-Engelhardt, David; Gutberlet, Matthias; Preim, Bernhard; Oeltze-Jafra, Steffen and Smit, Noeska N. and Sommer, Björn and Nieselt, Kay and Schultz, Thomas
    Four-dimensional phase-contrast magnetic resonance imaging (4D PC-MRI) allows for a non-invasive acquisition of timeresolved blood flow measurements, providing a valuable aid to clinicians and researchers seeking a better understanding of the interrelation between pathologies of the cardiovascular system and changes in blood flow patterns. Such research requires extensive analysis and comparison of blood flow data within and between different patient cohorts representing different age groups, genders and pathologies. However, a direct comparison between large numbers of datasets is not feasible due to the complexity of the data. In this paper, we present a novel approach to normalize aortic 4D PC-MRI datasets to enable qualitative and quantitative comparisons. We define normalized coordinate systems for the vessel surface as well as the intravascular volume, allowing for the computation of quantitative measures between datasets for both hemodynamic surface parameters as well as flow or pressure fields. To support the understanding of the geometric deformations involved in this process, individual transformations can not only be toggled on or off, but smoothly transitioned between anatomically faithful and fully abstracted states. In an informal interview with an expert radiologist, we confirm the usefulness of our technique. We also report on initial findings from exploring a database of 138 datasets consisting of both patient and healthy volunteers.
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    A Framework for Visual Comparison of 4D PC-MRI Aortic Blood Flow Data
    (The Eurographics Association, 2018) Behrendt, Benjamin; Ebel, Sebastian; Gutberlet, Matthias; Preim, Bernhard; Puig Puig, Anna and Schultz, Thomas and Vilanova, Anna and Hotz, Ingrid and Kozlikova, Barbora and Vázquez, Pere-Pau
    Four-dimensional phase-contrast magnetic resonance imaging (4D PC-MRI) allows for the non-invasive acquisition of in-vivo blood flow, producing a patient-specific blood flow model in selected vascular structures, e.g. the aorta. In the past, many specialized techniques for the visualization and exploration of such datasets have been developed, yet a tool for the visual comparison of multiple datasets is missing. Due to the complexity of the underlying data, a simple side-by-side comparison of two datasets using traditional visualization techniques can only yield coarse results. In this paper, we present a toolkit that allows for an efficient and robust registration of different 4D PC-MRI datasets and offers a variety of both qualitative and quantitative comparison techniques. Differences in the segmentation and time frame can be amended semi-automatically using landmarks on the vessel centerline and flow curve of the datasets. A set of measures quantifying the difference between the datasets, such as the flow jet displacement or flow angle and velocity difference, is automatically computed. To support the orientation in the spatio-temporal domain of the flow dataset, we provide bulls-eye plots that highlight potentially interesting regions. In an evaluation with three experienced radiologists, we confirmed the usefulness of our technique. With our application, they were able to discover previously unnoticed artifacts occurring in a dataset acquired with an experimental MRI sequence.
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    Is there a Tornado in Alex's Blood Flow? A Case Study for Narrative Medical Visualization
    (The Eurographics Association, 2022) Kleinau, Anna; Stupak, Evgenia; Mörth, Eric; Garrison, Laura A.; Mittenentzwei, Sarah; Smit, Noeska N.; Lawonn, Kai; Bruckner, Stefan; Gutberlet, Matthias; Preim, Bernhard; Meuschke, Monique; Renata G. Raidou; Björn Sommer; Torsten W. Kuhlen; Michael Krone; Thomas Schultz; Hsiang-Yun Wu
    Narrative visualization advantageously combines storytelling with new media formats and techniques, like interactivity, to create improved learning experiences. In medicine, it has the potential to improve patient understanding of diagnostic procedures and treatment options, promote confidence, reduce anxiety, and support informed decision-making. However, limited scientific research has been conducted regarding the use of narrative visualization in medicine. To explore the value of narrative visualization in this domain, we introduce a data-driven story to inform a broad audience about the usage of measured blood flow data to diagnose and treat cardiovascular diseases. The focus of the story is on blood flow vortices in the aorta, with which imaging technique they are examined, and why they can be dangerous. In an interdisciplinary team, we define the main contents of the story and the resulting design questions. We sketch the iterative design process and implement the story based on two genres. In a between-subject study, we evaluate the suitability and understandability of the story and the influence of different navigation concepts on user experience. Finally, we discuss reusable concepts for further narrative medical visualization projects.
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    Pressure-based Vortex Extraction in Cardiac 4D PC-MRI Blood Flow Data
    (The Eurographics Association, 2018) Köhler, Benjamin; Grothoff, Matthias; Gutberlet, Matthias; Preim, Bernhard; Jimmy Johansson and Filip Sadlo and Tobias Schreck
    We propose a technique for vortex extraction in cardiac 4D PC-MRI blood flow data that employs an intravascular, relative pressure calculation. The method is easy to implement, runs fully automatically, and requires no user-defined parameters. We qualitatively evaluated 100+ datasets of the aorta, pulmonary artery, or left ventricle from healthy volunteers as well as from patients acquired with different MR scanners. In all cases, the results suffer from significantly less noise than comparable approaches using the common λ 2 vortex criterion.