Matthias J. Ehrhardt

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Simon ARRIDGEUniversity College LondonVerified email at cs.ucl.ac.uk
Carola-Bibiane SchönliebDAMTP, University of CambridgeVerified email at cam.ac.uk
Kris ThielemansProfessor in Medical Imaging Physics, UCLVerified email at ucl.ac.uk
David AtkinsonProfessor, University College LondonVerified email at ucl.ac.uk
Sebastien OurselinProfessor of Healthcare Engineering, King's College LondonVerified email at kcl.ac.uk
Pawel J MarkiewiczUniversity College LondonVerified email at ucl.ac.uk
Luis PizarroResearch Associate, University College LondonVerified email at ucl.ac.uk
Martin BenningQueen Mary University of LondonVerified email at qmul.ac.uk
Marta M. BetckeUniversity College LondonVerified email at ucl.ac.uk
Antonin ChambolleCEREMADE, CNRS & Université Paris-Dauphine, PSL Research UniversityVerified email at ceremade.dauphine.fr
Peter RichtarikProfessor, KAUSTVerified email at kaust.edu.sa
Anna BarnesPrincipal Clinical Scientist, Inst. Nuclear Medicine, UCLHVerified email at nhs.net
Elena CelledoniProfessor of mathematics at the Norwegian University of Science and TechnologyVerified email at math.ntnu.no
Brynjulf OwrenProfessor of Mathematics, NTNU, NorwayVerified email at math.ntnu.no
Leon BungertFriedrich-Alexander-University Erlangen-NürnbergVerified email at fau.de
Rafael ReisenhoferUniversity of ViennaVerified email at univie.ac.at
Prof Jonathan M SchottInstitute of Neurology, UCLVerified email at ucl.ac.uk
David CoomesProfessor of Forest Ecology and Conservation, University of CambridgeVerified email at cam.ac.uk
Alexandre BousseLaTIM, INSERM, UMR 1101, Université de Bretagne OccidentaleVerified email at univ-brest.fr
Yu-Jung TsaiYale UniversityVerified email at yale.edu

Matthias J. Ehrhardt

Mathematical Sciences, University of Bath

Verified email at bath.ac.uk - Homepage

Mathematics Inverse Problems Medical Imaging Optimization Machine Learning


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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	115	2015
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	96	2018
Multicontrast MRI Reconstruction with Structure-Guided Total Variation MJ Ehrhardt, MM Betcke SIAM Journal on Imaging Sciences 9 (3), 1084-1106, 2016	75	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	74	2016
Vector-valued image processing by parallel level sets MJ Ehrhardt, SR Arridge IEEE Transactions on Image Processing 23 (1), 9-18, 2014	62	2014
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	28	2019
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	26	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	20	2017
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	19	2018
Learning the sampling pattern for MRI F Sherry, M Benning, JC De los Reyes, MJ Graves, G Maierhofer, ... IEEE Transactions on Medical Imaging, 2020	17	2020
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	16	2018
Sirf: Synergistic image reconstruction framework E Ovtchinnikov, R Brown, C Kolbitsch, E Pasca, C da Costa-Luis, ... Computer Physics Communications 249, 107087, 2020	13	2020
Incorporating structural prior information and sparsity into EIT using parallel level sets V Kolehmainen, MJ Ehrhardt, SR Arridge Inverse Problems & Imaging 13 (2), 285, 2019	13	2019
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	11	2019
Faster PET reconstruction with non-smooth priors by randomization and preconditioning MJ Ehrhardt, PJ Markiewicz, CB Schönlieb Physics in Medicine & Biology, 2019	10	2019
Joint Reconstruction for Multi-Modality Imaging with Common Structure MJ Ehrhardt UCL (University College London), 2015	9	2015
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	8	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	8	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
Structure preserving deep learning E Celledoni, MJ Ehrhardt, C Etmann, RI McLachlan, B Owren, ... arXiv preprint arXiv:2006.03364, 2020	7	2020

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