Matthias J. Ehrhardt
Matthias J. Ehrhardt
Mathematical Sciences, University of Bath
Verified email at - Homepage
Cited by
Cited by
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
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
Multicontrast MRI Reconstruction with Structure-Guided Total Variation
MJ Ehrhardt, MM Betcke
SIAM Journal on Imaging Sciences 9 (3), 1084-1106, 2016
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
Deep learning as optimal control problems: models and numerical methods
M Benning, E Celledoni, MJ Ehrhardt, B Owren, CB Schönlieb
Journal of Computational Dynamics 6 (2), 171-198, 2019
Learning the sampling pattern for MRI
F Sherry, M Benning, JC De los Reyes, MJ Graves, G Maierhofer, ...
IEEE Transactions on Medical Imaging, 2020
Vector-valued image processing by parallel level sets
MJ Ehrhardt, SR Arridge
IEEE Transactions on Image Processing 23 (1), 9-18, 2014
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
Sirf: Synergistic image reconstruction framework
E Ovtchinnikov, R Brown, C Kolbitsch, E Pasca, C da Costa-Luis, ...
Computer Physics Communications 249, 107087, 2020
Structure-preserving deep learning
E Celledoni, MJ Ehrhardt, C Etmann, RI McLachlan, B Owren, ...
European Journal of Applied Mathematics 32 (5), 888-936, 2021
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, 95-115, 2018
Faster PET reconstruction with non-smooth priors by randomization and preconditioning
MJ Ehrhardt, PJ Markiewicz, CB Schönlieb
Physics in Medicine & Biology, 2019
Choose your path wisely: gradient descent in a Bregman distance framework
M Benning, MM Betcke, MJ Ehrhardt, CB Schönlieb
SIAM Journal on Imaging Sciences 14 (2), 814-843, 2021
Accelerating variance-reduced stochastic gradient methods
D Driggs, MJ Ehrhardt, CB Schönlieb
Mathematical Programming, 1-45, 2020
Equivariant neural networks for inverse problems
E Celledoni, MJ Ehrhardt, C Etmann, B Owren, CB Schönlieb, F Sherry
Inverse problems 37 (8), 085006, 2021
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
Regularising inverse problems with generative machine learning models
MAG Duff, NDF Campbell, MJ Ehrhardt
Journal of Mathematical Imaging and Vision 66 (1), 37-56, 2024
A geometric integration approach to nonsmooth, nonconvex optimisation
ES Riis, MJ Ehrhardt, GRW Quispel, CB Schönlieb
Foundations of Computational Mathematics, 1-44, 2021
Robust Image Reconstruction with Misaligned Structural Information
L Bungert, MJ Ehrhardt
IEEE Access 8, 222944-222955, 2020
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
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