| Model-Based Learning for Accelerated, Limited-View 3-D Photoacoustic Tomography A Hauptmann, F Lucka, M Betcke, N Huynh, J Adler, B Cox, P Beard, ... IEEE transactions on medical imaging 37 (6), 1382-1393, 2018 | 181 | 2018 |
| Simulating transcranial direct current stimulation with a detailed anisotropic human head model SM Rampersad, AM Janssen, F Lucka, Ü Aydin, B Lanfer, S Lew, ... IEEE Transactions on Neural Systems and Rehabilitation Engineering 22 (3 …, 2014 | 151 | 2014 |
| Accelerated high-resolution photoacoustic tomography via compressed sensing S Arridge, P Beard, M Betcke, B Cox, N Huynh, F Lucka, O Ogunlade, ... Physics in Medicine & Biology 61 (24), 8908, 2016 | 108 | 2016 |
| 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), 1143-1156, 2019 | 87 | 2019 |
| On the adjoint operator in photoacoustic tomography SR Arridge, MM Betcke, BT Cox, F Lucka, BE Treeby Inverse Problems 32 (11), 115012, 2016 | 86 | 2016 |
| Hierarchical Bayesian inference for the EEG inverse problem using realistic FE head models: depth localization and source separation for focal primary currents F Lucka, S Pursiainen, M Burger, CH Wolters Neuroimage 61 (4), 1364-1382, 2012 | 76 | 2012 |
| Maximum a posteriori estimates in linear inverse problems with log-concave priors are proper Bayes estimators M Burger, F Lucka Inverse Problems 30 (11), 114004, 2014 | 42 | 2014 |
| The role of blood vessels in high-resolution volume conductor head modeling of EEG LDJ Fiederer, J Vorwerk, F Lucka, M Dannhauer, S Yang, M Dümpelmann, ... NeuroImage 128, 193-208, 2016 | 41 | 2016 |
| Using reciprocity for relating the simulation of transcranial current stimulation to the EEG forward problem S Wagner, F Lucka, J Vorwerk, CS Herrmann, G Nolte, M Burger, ... NeuroImage 140, 163-173, 2016 | 34 | 2016 |
| The influence of sulcus width on simulated electric fields induced by transcranial magnetic stimulation AM Janssen, SM Rampersad, F Lucka, B Lanfer, S Lew, Ü Aydin, ... Physics in Medicine & Biology 58 (14), 4881, 2013 | 32 | 2013 |
| Complete electrode model in EEG: relationship and differences to the point electrode model S Pursiainen, F Lucka, CH Wolters Physics in Medicine & Biology 57 (4), 999, 2012 | 28 | 2012 |
| Fast Markov chain Monte Carlo sampling for sparse Bayesian inference in high-dimensional inverse problems using L1-type priors F Lucka Inverse Problems 28 (12), 125012, 2012 | 26 | 2012 |
| 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 | 23 | 2018 |
| Sub-sampled Fabry-Perot photoacoustic scanner for fast 3D imaging N Huynh, F Lucka, E Zhang, M Betcke, S Arridge, P Beard, B Cox Photons Plus Ultrasound: Imaging and Sensing 2017 10064, 100641Y, 2017 | 23 | 2017 |
| Enhancing compressed sensing 4D photoacoustic tomography by simultaneous motion estimation F Lucka, N Huynh, M Betcke, E Zhang, P Beard, B Cox, S Arridge SIAM Journal on Imaging Sciences 11 (4), 2224-2253, 2018 | 22 | 2018 |
| Three-dimensional photoacoustic imaging and inversion for accurate quantification of chromophore distributions M Fonseca, E Malone, F Lucka, R Ellwood, L An, S Arridge, P Beard, ... Photons Plus Ultrasound: Imaging and Sensing 2017 10064, 1006415, 2017 | 21 | 2017 |
| Explorative Imaging and Its Implementation at the FleX-ray Laboratory SB Coban, F Lucka, WJ Palenstijn, D Van Loo, KJ Batenburg Journal of Imaging 6 (4), 18, 2020 | 19 | 2020 |
| A hierarchical Bayesian perspective on majorization-minimization for non-convex sparse regression: application to M/EEG source imaging Y Bekhti, F Lucka, J Salmon, A Gramfort Inverse Problems 34 (8), 085010, 2018 | 16 | 2018 |
| Fast Gibbs sampling for high-dimensional Bayesian inversion F Lucka Inverse Problems 32 (11), 115019, 2016 | 14 | 2016 |
| A cone-beam X-ray computed tomography data collection designed for machine learning H Der Sarkissian, F Lucka, M van Eijnatten, G Colacicco, SB Coban, ... Scientific Data 6 (1), 1-8, 2019 | 13 | 2019 |
Ben CoxUniversity College LondonVerified email at ucl.ac.uk
