The research group of Prof. Hesser focuses on the field of inverse problems. To mention a few, image reconstruction, segmentation, classification etc. play a vital role for clinical decision systems (CDS) or therapy support systems (TSS) but also for preclinical research. Deep learning changed the field substantially by learning the model or the prior distribution via measured or simulated data. Also issues like uncertainty and causal inference are of high importance. We are focusing on basic mathematical tools, their properties and how they can be applied for selected applications in computational medicine.
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Jan 2, 2024
Jerg, Katharina, 2024, "Real-time definition of single seed placement sensitivity in low-dose-rate prostate brachytherapy [code and patient data]", https://doi.org/10.11588/data/Y3KIPM, heiDATA, V1
PURPOSE: In low-dose-rate brachytherapy iodine seeds are implanted based on a treatment plan, generated with respect to different dose constraints. The quality of the dose distribution depends on a precise seed placement, however, it is not clear which seed misplacements have a l...
Oct 12, 2023
Jerg, Katharina, 2023, "Diffuse Domain Approach for Flexible Needle Insertion and Relaxation [data]", https://doi.org/10.11588/data/QYCZLB, heiDATA, V1
Needle insertion simulations play an important role in medical training and surgical planning. Most simulations require boundary conforming meshes, while the diffuse domain approach, currently limited to stiff needles, eliminates the need for meshing geometries. In this paper the...
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