| 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 | 137 | 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 | 97 | 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 | 66 | 2019 |
| 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 | 30 | 2014 |
| 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 | 24 | 2019 |
| 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 | 24 | 2017 |
| Deep Learning in Photoacoustic Tomography: Current approaches and future directions A Hauptmann, BT Cox Journal of Biomedical Optics 25 (11), 112903, 2020 | 22 | 2020 |
| A direct D-bar method for partial boundary data electrical impedance tomography with a priori information M Alsaker, SJ Hamilton, A Hauptmann Inverse Problems and Imaging 11 (3), 427 - 454, 2017 | 20 | 2017 |
| 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 International Workshop on Machine Learning for Medical Image Reconstruction …, 2018 | 19 | 2018 |
| Open 2D electrical impedance tomography data archive A Hauptmann, V Kolehmainen, NM Mach, T Savolainen, A Seppänen, ... arXiv preprint arXiv:1704.01178, 2017 | 17 | 2017 |
| 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 | 14 | 2016 |
| Toward accurate quantitative photoacoustic imaging: learning vascular blood oxygen saturation in three dimensions C Bench, A Hauptmann, BT Cox Journal of Biomedical Optics 25 (8), 085003, 2020 | 12* | 2020 |
| Networks for nonlinear diffusion problems in imaging S Arridge, A Hauptmann Journal of Mathematical Imaging and Vision 62, 471–487, 2020 | 12 | 2020 |
| A Data-Driven Edge-Preserving D-bar Method for Electrical Impedance Tomography S Hamilton, A Hauptmann, S Siltanen Inverse Problems and Imaging 8 (4), 1053-1072, 2014 | 12 | 2014 |
| Direct inversion from partial-boundary data in electrical impedance tomography A Hauptmann, M Santacesaria, S Siltanen Inverse Problems 33 (2), 025009, 2017 | 11 | 2017 |
| Multi-scale learned iterative reconstruction A Hauptmann, J Adler, SR Arridge, O Oktem IEEE Transactions on Computational Imaging, 2020 | 8 | 2020 |
| Revealing cracks inside conductive bodies by electric surface measurements A Hauptmann, M Ikehata, H Itou, S Siltanen Inverse Problems 35 (2), 2019 | 8 | 2019 |
| Learning and correcting non-Gaussian model errors D Smyl, TN Tallman, JA Black, A Hauptmann, D Liu Journal of Computational Physics 432, 110152, 2021 | 4 | 2021 |
| On learned operator correction in inverse problems S Lunz, A Hauptmann, T Tarvainen, CB Schönlieb, S Arridge arXiv preprint arXiv:2005.07069, 2020 | 4 | 2020 |
| Approximation of full-boundary data from partial-boundary electrode measurements A Hauptmann Inverse Problems 33 (12), 125017, 2017 | 4 | 2017 |
Simon ARRIDGEUniversity College LondonVerified email at cs.ucl.ac.uk
