Matthias J. Ehrhardt
Matthias J. Ehrhardt
Institute for Mathematical Innovation, University of Bath
Verified email at bath.ac.uk - Homepage
TitleCited byYear
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
752015
Vector-valued image processing by parallel level sets
MJ Ehrhardt, SR Arridge
IEEE Transactions on Image Processing 23 (1), 9-18, 2014
492014
Multicontrast MRI Reconstruction with Structure-Guided Total Variation
MJ Ehrhardt, MM Betcke
SIAM Journal on Imaging Sciences 9 (3), 1084-1106, 2016
482016
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
412018
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
352016
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
102018
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
102017
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
82012
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
72018
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
72017
Joint Reconstruction for Multi-Modality Imaging with Common Structure
MJ Ehrhardt
UCL (University College London), 2015
72015
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
62018
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
52015
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
42019
Gradient descent in a generalised Bregman distance framework
M Benning, MM Betcke, MJ Ehrhardt, CB Schönlieb
arXiv preprint arXiv:1612.02506, 2016
42016
Joint Reconstruction of PET-MRI by Parallel Level Sets
MJ Ehrhardt, K Thielemans, S Arridge
LMS Inverse Day: Sparse Regularisation for Inverse Problems, 2014
42014
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
42014
A geometric integration approach to nonsmooth, nonconvex optimisation
ES Riis, MJ Ehrhardt, GRW Quispel, CB Schönlieb
arXiv preprint arXiv:1807.07554, 2018
32018
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
32018
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
22019
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Articles 1–20