| Model based learning for accelerated, limited-view 3D photoacoustic tomography A Hauptmann, F Lucka, M Betcke, N Huynh, J Adler, B Cox, P Beard, ... IEEE Transactions on Medical Imaging, 2018 | 372 | 2018 |
| Deep D-bar: Real-time electrical impedance tomography imaging with deep neural networks SJ Hamilton, A Hauptmann IEEE transactions on medical imaging 37 (10), 2367-2377, 2018 | 340 | 2018 |
| Real-time Cardiovascular MR with Spatio-temporal Artifact Suppression using Deep Learning - Proof of Concept in Congenital Heart Disease A Hauptmann, S Arridge, F Lucka, V Muthurangu, JA Steeden Magnetic Resonance in Medicine 81 (2), 2019 | 256 | 2019 |
| Deep learning in photoacoustic tomography: current approaches and future directions A Hauptmann, B Cox Journal of Biomedical Optics 25 (11), 112903-112903, 2020 | 167 | 2020 |
| Beltrami-net: domain-independent deep D-bar learning for absolute imaging with electrical impedance tomography (a-EIT) SJ Hamilton, A Hänninen, A Hauptmann, V Kolehmainen Physiological measurement 40 (7), 074002, 2019 | 76 | 2019 |
| Learned reconstruction methods with convergence guarantees: A survey of concepts and applications S Mukherjee, A Hauptmann, O Öktem, M Pereyra, CB Schönlieb IEEE Signal Processing Magazine 40 (1), 164-182, 2023 | 75* | 2023 |
| Rapid whole-heart CMR with single volume super-resolution JA Steeden, M Quail, A Gotschy, KH Mortensen, A Hauptmann, S Arridge, ... Journal of Cardiovascular Magnetic Resonance 22 (1), 56, 2020 | 71 | 2020 |
| Toward accurate quantitative photoacoustic imaging: learning vascular blood oxygen saturation in three dimensions C Bench, A Hauptmann, B Cox Journal of Biomedical Optics 25 (8), 085003-085003, 2020 | 67 | 2020 |
| A variational reconstruction method for undersampled dynamic x-ray tomography based on physical motion models M Burger, H Dirks, L Frerking, A Hauptmann, T Helin, S Siltanen Inverse Problems 33 (12), 124008, 2017 | 65 | 2017 |
| Machine learning in magnetic resonance imaging: image reconstruction J Montalt-Tordera, V Muthurangu, A Hauptmann, JA Steeden Physica Medica 83, 79-87, 2021 | 62 | 2021 |
| Open 2D electrical impedance tomography data archive A Hauptmann, V Kolehmainen, NM Mach, T Savolainen, A Seppänen, ... arXiv preprint arXiv:1704.01178, 2017 | 58 | 2017 |
| On learned operator correction in inverse problems S Lunz, A Hauptmann, T Tarvainen, CB Schönlieb, S Arridge SIAM Journal on Imaging Sciences 14 (1), 2021 | 57 | 2021 |
| Multi-scale learned iterative reconstruction A Hauptmann, J Adler, S Arridge, O Öktem IEEE transactions on computational imaging 6, 843-856, 2020 | 56 | 2020 |
| Graph convolutional networks for model-based learning in nonlinear inverse problems W Herzberg, DB Rowe, A Hauptmann, SJ Hamilton IEEE transactions on computational imaging 7, 1341-1353, 2021 | 48 | 2021 |
| Approximate k-space models and deep learning for fast photoacoustic reconstruction A Hauptmann, B Cox, F Lucka, N Huynh, M Betcke, P Beard, S Arridge Machine Learning for Medical Image Reconstruction: First International …, 2018 | 48 | 2018 |
| Structural engineering from an inverse problems perspective A Gallet, S Rigby, TN Tallman, X Kong, I Hajirasouliha, A Liew, D Liu, ... Proceedings of the Royal Society A 478 (2257), 20210526, 2022 | 41 | 2022 |
| A model-based iterative learning approach for diffuse optical tomography M Mozumder, A Hauptmann, I Nissilä, SR Arridge, T Tarvainen IEEE Transactions on Medical Imaging, 2022 | 39 | 2022 |
| Tomographic X-ray data of a lotus root filled with attenuating objects TA Bubba, A Hauptmann, S Huotari, J Rimpeläinen, S Siltanen arXiv preprint arXiv:1609.07299, 2016 | 38 | 2016 |
| Total variation regularization for large-scale X-ray tomography K Hämäläinen, L Harhanen, A Hauptmann, A Kallonen, E Niemi, ... Int. J. Tomogr. Simul 25 (1), 1-25, 2014 | 38 | 2014 |
| Material decomposition in spectral CT using deep learning: a Sim2Real transfer approach JFPJ Abascal, N Ducros, V Pronina, S Rit, PA Rodesch, T Broussaud, ... IEEE Access 9, 25632-25647, 2021 | 37 | 2021 |
Simon ARRIDGEUniversity College LondonOverená e-mailová adresa na: cs.ucl.ac.uk
