Quantitative comparison of deep learning-based image reconstruction methods for low-dose and sparse-angle CT applications J Leuschner, M Schmidt, PS Ganguly, V Andriiashen, SB Coban, ...
Journal of Imaging 7 (3), 44, 2021
61 2021 Conditional invertible neural networks for medical imaging A Denker, M Schmidt, J Leuschner, P Maass
Journal of Imaging 7 (11), 243, 2021
44 2021 PatchNR: learning from very few images by patch normalizing flow regularization F Altekrüger, A Denker, P Hagemann, J Hertrich, P Maass, G Steidl
Inverse Problems 39 (6), 064006, 2023
36 * 2023 An educated warm start for deep image prior-based micro CT reconstruction R Barbano, J Leuschner, M Schmidt, A Denker, A Hauptmann, P Maass, ...
IEEE Transactions on Computational Imaging 8, 1210-1222, 2022
31 * 2022 Conditional normalizing flows for low-dose computed tomography image reconstruction A Denker, M Schmidt, J Leuschner, P Maass, J Behrmann
arXiv preprint arXiv:2006.06270, 2020
22 2020 Steerable Conditional Diffusion for Out-of-Distribution Adaptation in Medical Image Reconstruction R Barbano, A Denker, H Chung, TH Roh, S Arridge, P Maass, B Jin, ...
IEEE Transactions on Medical Imaging, 2025
12 * 2025 DEFT: Efficient Fine-tuning of Diffusion Models by Learning the Generalised A Denker, F Vargas, S Padhy, K Didi, S Mathis, R Barbano, V Dutordoir, ...
Advances in Neural Information Processing Systems 37, 19636-19682, 2025
11 * 2025 Score-based generative models for PET image reconstruction IRD Singh, A Denker, R Barbano, Ž Kereta, B Jin, K Thielemans, P Maass, ...
arXiv preprint arXiv:2308.14190, 2023
11 2023 Invertible residual networks in the context of regularization theory for linear inverse problems C Arndt, A Denker, S Dittmer, N Heilenkötter, M Iske, T Kluth, P Maass, ...
Inverse Problems 39 (12), 125018, 2023
10 2023 Model-based deep learning approaches to the Helsinki Tomography Challenge 2022 C Arndt, A Denker, S Dittmer, J Leuschner, J Nickel, M Schmidt
Applied Mathematics for Modern Challenges 1 (2), 87-104, 2023
3 2023 Data-driven approaches for electrical impedance tomography image segmentation from partial boundary data A Denker, Z Kereta, I Singh, T Freudenberg, T Kluth, P Maass, S Arridge
arXiv preprint arXiv:2407.01559, 2024
2 2024 Plug-and-Play Half-Quadratic Splitting for Ptychography A Denker, J Hertrich, Z Kereta, S Cipiccia, E Erin, S Arridge
arXiv preprint arXiv:2412.02548, 2024
1 2024 Convergence Properties of Score-Based Models for Linear Inverse Problems Using Graduated Optimisation P Fernsel, Ž Kereta, A Denker
2024 IEEE 34th International Workshop on Machine Learning for Signal …, 2024
1 * 2024 Improved Mass Calibration in MALDI MSI Using Neural Network-Based Recalibration A Denker, J Behrmann, T Boskamp
Analytical chemistry 96 (19), 7542-7549, 2024
1 2024 Investigating intensity normalisation for pet reconstruction with supervised deep learning I Singh, A Denker, B Jin, K Thielemans, S Arridge
IEEE, 2024
1 2024 Iterative Importance Fine-tuning of Diffusion Models A Denker, S Padhy, F Vargas, J Hertrich
arXiv preprint arXiv:2502.04468, 2025
2025 Invertible neural networks and normalizing flows for image reconstruction A Denker
Universität Bremen, 2024
2024 MR-blob: Coordinate-Transformed Blobs for Parallel MRI Reconstruction IRD Singh, Ž Kereta, A Denker, R Barbano, B Jin, K Thielemans, ...
International Society for Magnetic Resonance in Medicine (ISMRM), 2024
2024 In Focus-hybrid deep learning approaches to the HDC2021 challenge. C Arndt, A Denker, J Nickel, J Leuschner, M Schmidt, G Rigaud
Inverse Problems & Imaging 17 (5), 2023
2023 The Deep Capsule Prior–advantages through complexity? M Schmidt, A Denker, J Leuschner
PAMM 21 (1), e202100166, 2021
2021