Browsing by Author "Muren, Ludvig P."

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    Lessons Learnt from Developing Visual Analytics Applications for Adaptive Prostate Cancer Radiotherapy
    (The Eurographics Association, 2020) Raidou, Renata Georgia; Furmanová, Katarína; Grossmann, Nicolas; Casares-Magaz, Oscar; Moiseenko, Vitali; Einck, John P.; Gröller, Eduard; Muren, Ludvig P.; Gillmann, Christina and Krone, Michael and Reina, Guido and Wischgoll, Thomas
    In radiotherapy (RT), changes in patient anatomy throughout the treatment period might lead to deviations between planned and delivered dose, resulting in inadequate tumor coverage and/or overradiation of healthy tissues. Adapting the treatment to account for anatomical changes is anticipated to enable higher precision and less toxicity to healthy tissues. Corresponding tools for the in-depth exploration and analysis of available clinical cohort data were not available before our work. In this paper, we discuss our on-going process of introducing visual analytics to the domain of adaptive RT for prostate cancer. This has been done through the design of three visual analytics applications, built for clinical researchers working on the deployment of robust RT treatment strategies. We focus on describing our iterative design process, and we discuss the lessons learnt from our fruitful collaboration with clinical domain experts and industry, interested in integrating our prototypes into their workflow.
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    State-of-the-Art Report: Visual Computing in Radiation Therapy Planning
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Schlachter, Matthias; Raidou, Renata Georgia; Muren, Ludvig P.; Preim, Bernhard; Putora, Paul Martin; Bühler, Katja; Laramee, Robert S. and Oeltze, Steffen and Sedlmair, Michael
    Radiation therapy (RT) is one of the major curative approaches for cancer. It is a complex and risky treatment approach, which requires precise planning, prior to the administration of the treatment. Visual Computing (VC) is a fundamental component of RT planning, providing solutions in all parts of the process-from imaging to delivery. Despite the significant technological advancements of RT over the last decades, there are still many challenges to address. This survey provides an overview of the compound planning process of RT, and of the ways that VC has supported RT in all its facets. The RT planning process is described to enable a basic understanding in the involved data, users and workflow steps. A systematic categorization and an extensive analysis of existing literature in the joint VC/RT research is presented, covering the entire planning process. The survey concludes with a discussion on lessons learnt, current status, open challenges, and future directions in VC/RT research.
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    Visual Assessment of Growth Prediction in Brain Structures after Pediatric Radiotherapy
    (The Eurographics Association, 2021) Magg, Caroline; Toussaint, Laura; Muren, Ludvig P.; Indelicato, Danny J.; Raidou, Renata Georgia; Oeltze-Jafra, Steffen and Smit, Noeska N. and Sommer, Björn and Nieselt, Kay and Schultz, Thomas
    Pediatric brain tumor radiotherapy research is investigating how radiation influences the development and function of a patient's brain. To better understand how brain growth is affected by the treatment, the brain structures of the patient need to be explored and analyzed pre- and post-treatment. In this way, anatomical changes are observed over a long period and are assessed as potential early markers of cognitive or functional damage. In this early work, we propose an automated approach for the visual assessment of the growth prediction of brain structures in pediatric brain tumor radiotherapy patients. Our approach reduces the need for re-segmentation and the time required for it. We employ as a basis pre-treatment Computed Tomography (CT) scans with manual delineations (i.e., segmentation masks) of specific brain structures of interest. These pre-treatment masks are used as initialization, to predict the corresponding masks on multiple post-treatment follow-up Magnetic Resonance (MR) images, using an active contour model approach. For the accuracy quantification of the automatically predicted posttreatment masks, a support vector regressor (SVR) with features related to geometry, intensity, and gradients is trained on the pre-treatment data. Finally, a distance transform is employed to calculate the distances between pre- and post-treatment data and to visualize the predicted growth of a brain structure, along with its respective accuracy. Although segmentations of larger structures are more accurately predicted, the growth behavior of all structures is learned correctly, as indicated by the SVR results. This suggests that our pipeline is a positive initial step for the visual assessment of brain structure growth prediction.