19 citations · 33 across the 9 of their papers we have counts for
5 papers · 1 filter
Deep Learning Based Reconstruction Methods for Electrical Impedance Tomography
Alexander Denker, Fabio Margotti, Jianfeng Ning +5
Electrical Impedance Tomography (EIT) is a powerful imaging modality widely used in medical diagnostics, industrial monitoring, and environmental studies. The EIT inverse problem i…
Data-driven approaches for electrical impedance tomography image segmentation from partial boundary data
Alexander Denker, Zeljko Kereta, Imraj Singh +4
Electrical impedance tomography (EIT) plays a crucial role in non-invasive imaging, with both medical and industrial applications. In this paper, we present three data-driven recon…
Score-Based Generative Models for PET Image Reconstruction
Imraj RD Singh, Alexander Denker, Riccardo Barbano +5
Score-based generative models have demonstrated highly promising results for medical image reconstruction tasks in magnetic resonance imaging or computed tomography. However, their…
Conditional Invertible Neural Networks for Medical Imaging
Alexander Denker, Maximilian Schmidt, Johannes Leuschner +1
Over the last years, deep learning methods have become an increasingly popular choice to solve tasks from the field of inverse problems. Many of these new data-driven methods have…
Conditional Normalizing Flows for Low-Dose Computed Tomography Image Reconstruction
Alexander Denker, Maximilian Schmidt, Johannes Leuschner +2
Image reconstruction from computed tomography (CT) measurement is a challenging statistical inverse problem since a high-dimensional conditional distribution needs to be estimated.…