| Joint reconstruction of PET-MRI by exploiting structural similarity MJ Ehrhardt, K Thielemans, L Pizarro, D Atkinson, S Ourselin, BF Hutton, ... Inverse Problems 31 (1), 015001, 2015 | 75 | 2015 |
| Vector-valued image processing by parallel level sets MJ Ehrhardt, SR Arridge IEEE Transactions on Image Processing 23 (1), 9-18, 2014 | 49 | 2014 |
| Multicontrast MRI Reconstruction with Structure-Guided Total Variation MJ Ehrhardt, MM Betcke SIAM Journal on Imaging Sciences 9 (3), 1084-1106, 2016 | 48 | 2016 |
| Stochastic primal-dual hybrid gradient algorithm with arbitrary sampling and imaging applications A Chambolle, MJ Ehrhardt, P Richtárik, CB Schönlieb SIAM Journal on Optimization 28 (4), 2783-2808, 2018 | 41 | 2018 |
| PET Reconstruction with an Anatomical MRI Prior using Parallel Level Sets. MJ Ehrhardt, P Markiewicz, M Liljeroth, A Barnes, V Kolehmainen, ... IEEE Transactions on Medical Imaging, 2016 | 35 | 2016 |
| NiftyPET: A high-throughput software platform for high quantitative accuracy and precision PET imaging and analysis PJ Markiewicz, MJ Ehrhardt, K Erlandsson, PJ Noonan, A Barnes, ... Neuroinformatics 16 (1), 95-115, 2018 | 10 | 2018 |
| Choose your path wisely: gradient descent in a Bregman distance framework M Benning, MM Betcke, MJ Ehrhardt, CB Schönlieb arXiv preprint arXiv:1712.04045, 2017 | 10 | 2017 |
| Evaluation of decomposition tools for sea floor pressure data: a practical comparison of modern and classical approaches MJ Ehrhardt, H Villinger, S Schiffler Computers & Geosciences 45, 4-12, 2012 | 8 | 2012 |
| Blind image fusion for hyperspectral imaging with the directional total variation L Bungert, DA Coomes, MJ Ehrhardt, J Rasch, R Reisenhofer, ... Inverse Problems 34 (4), 044003, 2018 | 7 | 2018 |
| Faster PET Reconstruction with a Stochastic Primal-Dual Hybrid Gradient Method MJ Ehrhardt, P Markiewicz, A Chambolle, P Richtárik, J Schott, ... Wavelets and Sparsity XVII 10394, 103941O, 2017 | 7 | 2017 |
| Joint Reconstruction for Multi-Modality Imaging with Common Structure MJ Ehrhardt UCL (University College London), 2015 | 7 | 2015 |
| Fast Quasi-Newton Algorithms for Penalized Reconstruction in Emission Tomography and Further Improvements via Preconditioning YJ Tsai, A Bousse, MJ Ehrhardt, CW Stearns, S Ahn, BF Hutton, S Arridge, ... IEEE transactions on medical imaging 37 (4), 1000-1010, 2018 | 6 | 2018 |
| Performance evaluation of MAP algorithms with different penalties, object geometries and noise levels YJ Tsai, A Bousse, MJ Ehrhardt, BF Hutton, S Arridge, K Thielemans 2015 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC …, 2015 | 5 | 2015 |
| Incorporating structural prior information and sparsity into EIT using parallel level sets V Kolehmainen, MJ Ehrhardt, SR Arridge Inverse Problems & Imaging 13 (2), 285-307, 2019 | 4 | 2019 |
| Gradient descent in a generalised Bregman distance framework M Benning, MM Betcke, MJ Ehrhardt, CB Schönlieb arXiv preprint arXiv:1612.02506, 2016 | 4 | 2016 |
| Joint Reconstruction of PET-MRI by Parallel Level Sets MJ Ehrhardt, K Thielemans, S Arridge LMS Inverse Day: Sparse Regularisation for Inverse Problems, 2014 | 4 | 2014 |
| Joint Reconstruction of PET-MRI by Parallel Level Sets MJ Ehrhardt, K Thielemans, S Arridge 2014 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 2014 | 4 | 2014 |
| A geometric integration approach to nonsmooth, nonconvex optimisation ES Riis, MJ Ehrhardt, GRW Quispel, CB Schönlieb arXiv preprint arXiv:1807.07554, 2018 | 3 | 2018 |
| A geometric integration approach to smooth optimisation: Foundations of the discrete gradient method MJ Ehrhardt, ES Riis, T Ringholm, CB Schönlieb arXiv preprint arXiv:1805.06444, 2018 | 3 | 2018 |
| Enhancing joint reconstruction and segmentation with non-convex Bregman iteration V Corona, M Benning, MJ Ehrhardt, LF Gladden, R Mair, A Reci, ... Inverse Problems 35 (5), 055001, 2019 | 2 | 2019 |
Simon ARRIDGEUniversity College LondonVerified email at cs.ucl.ac.uk