@inproceedings{BauerFrikel, author = {Bauer, Patrick and Frikel, J{\"u}rgen}, title = {BPConvNet: a deep learning based ρ-Filtered layergram reconstruction method for computed tomography}, series = {AIP Conference Proceedings}, volume = {3315}, booktitle = {AIP Conference Proceedings}, number = {1}, publisher = {AIP Publishing}, issn = {0094-243X}, doi = {10.1063/5.0286063}, abstract = {In this article, we address the reconstruction problem in computed tomography (CT) when dealing with sparse view data. Traditional approaches like filtered backprojection (FBP) often fail under these conditions, leading to streaking artifacts. We propose BPConvNet, a deep learning based version of the ρ-filtered layergram or backprojection filtration (BPF) technique (cf. [1]). Unlike FBP, the BPF method applies filtering (F) after backprojection (BP), hence the name. The proposed BPConvNet adapts the BPF workflow by substituting the filtering step with a residual convolutional neural network. Our numerical experiments demonstrate that BPConvNet is competitive to similar deep learning methods. Moreover, we explain that BPConvNet can be easily adapted to to different CT acquisition geometries, such as fan beam and 3D configurations}, language = {en} }