TY - CHAP A1 - Bauer, Patrick A1 - Frikel, Jürgen T1 - BPConvNet: a deep learning based ρ-Filtered layergram reconstruction method for computed tomography T2 - AIP Conference Proceedings N2 - 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 KW - Convolutional neural network KW - Deep learning KW - Learning and learning models KW - Computed tomography Y1 - 2025 U6 - https://doi.org/10.1063/5.0286063 SN - 0094-243X VL - 3315 IS - 1 PB - AIP Publishing ER - TY - CHAP A1 - Selig, Tim A1 - Bauer, Patrick A1 - Frikel, Jürgen A1 - März, Thomas A1 - Storath, Martin A1 - Weinmann, Andreas 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 - Two-stage Approach for Low-dose and Sparse-angle CT Reconstruction using Backprojection T2 - Bildverarbeitung für die Medizin 2025 (BVM 2025): Proceedings, German Conference on Medical Image Computing, Regensburg March 09-11, 2025 N2 - This paper presents a novel two-stage approach for computed tomography (CT) reconstruction, focusing on sparse-angle and low-dose setups to minimize radiation exposure while maintaining high image quality. Two-stage approaches consist of an initial reconstruction followed by a neural network for image refinement. In the initial reconstruction, we apply the backprojection (BP) instead of the traditional filtered backprojection (FBP). This enhances computational speed and offers potential advantages for more complex geometries, such as fan-beam and cone-beam CT. Additionally, BP addresses noise and artifacts in sparse-angle CT by leveraging its inherent noise-smoothing effect, which reduces streaking artifacts common in FBP reconstructions. For the second stage, we fine-tune the DRUNet proposed by Zhang et al. to further improve reconstruction quality. We call our method BP-DRUNet and evaluate its performance on a synthetically generated ellipsoid dataset alongside thewell-established LoDoPaBCT dataset. Our results show that BP-DRUNet produces competetive results in terms of PSNR and SSIM metrics compared to the FBP-based counterpart, FBPDRUNet, and delivers visually competitive results across all tested angular setups. Y1 - 2025 SN - 978-3-658-47421-8 U6 - https://doi.org/10.1007/978-3-658-47422-5_67 SP - 286 EP - 291 PB - Springer VS CY - Wiesbaden ER -