TY - CHAP A1 - Frikel, Jürgen A1 - Göppel, Simon A1 - Haltmeier, Markus ED - Palm, Christoph T1 - Combining Reconstruction and Edge Detection in Computed Tomography T2 - Bildverarbeitung für die Medizin 2021 : Proceedings, German Workshop on Medical Image Computing: Regensburg, March 7-9, 2021 N2 - We present two methods that combine image reconstruction and edge detection in computed tomography (CT) scans. Our first method is as an extension of the prominent filtered backprojection algorithm. In our second method we employ l1-regularization for stable calculation of the gradient. As opposed to the first method, we show that this approach is able to compensate for undersampled CT data. Y1 - 2021 SN - 978-3-658-33197-9 U6 - https://doi.org/10.1007/978-3-658-33198-6_37 SP - 153 EP - 157 PB - Springer Vieweg CY - Wiesbaden, Germany ER - TY - CHAP A1 - Göppel, Simon A1 - Frikel, Jürgen A1 - Haltmeier, Markus ED - Palm, Christoph ED - Breininger, Katharina ED - Deserno, Thomas M. ED - Handels, Heinz ED - Maier, Andreas ED - Maier-Hein, Klaus H. ED - Tolxdorff, Thomas M. T1 - Data-proximal Neural Networks for Limited-view CT T2 - Bildverarbeitung für die Medizin 2025 : Proceedings, German Conference on Medical Image Computing, Regensburg March 09-11, 2025 N2 - Limited-angle computed tomography (CT) requires solving an inverse problem that is both ill-conditioned and underdetermined. In recent years, learned reconstruction methods have proven highly effective in addressing this challenge. Most of these methods follow a two-step process: first, an initial reconstruction method is applied to the data to generate an auxiliary reconstruction; second, a neural network is used to map the auxiliary reconstruction closer to the ground truth images. However, when applied to unseen data, there are no guarantees that the network’s output will remain consistent with the available measurement data. To address this, we recently introduced a data-proximal network architecture. In this paper, we implement this approach for limited-angle CT and compare its performance with a standard residual network and a null space network. Y1 - 2025 SN - 978-3-658-47421-8 U6 - https://doi.org/10.1007/978-3-658-47422-5_41 SP - 185 EP - 190 PB - Springer Fachmedien Wiesbaden CY - Wiesbaden ER -