Career
- 09/18-today: Prize Fellow, University of Bath, UK
- 01/16-09/18: Research Associate, University of Cambridge, UK
- 05/15-12/15: Research Associate, University College London, UK
- 05/12-04/15: PhD student, University College London, UK
Research
Matthias is a member of the Department of Applied Mathematics and Theoretical Physics as part of the Cambirdge Image Analysis research group under supervision of Dr Carola-Bibiane Schönlieb. His current research interests are inverse problems, optimization, medical imaging and image processing. He is particularly interested in multi-modality imaging such as combined Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET).
Selected Publications
- A. Chambolle, M. J. Ehrhardt, P. Richtárik and C.-B. Schönlieb, Stochastic Primal-Dual Hybrid Gradient Algorithm with Arbitrary Sampling and Imaging Applications, accepted in SIAM Optimization, 2018
- M. J. Ehrhardt, E. S. Riis, T. Ringholm, C.-B. Schönlieb, A geometric integration approach to smooth optimisation: Foundations of the discrete gradient method, preprint, 2018
- E. S. Riis, M. J. Ehrhardt, G. R. W. Quispel, C.-B. Schönlieb, A geometric integration approach to nonsmooth, nonconvex optimisation, preprint, 2018
- V. Corona, M. Benning, M. J. Ehrhardt, L. F. Gladden, R. Mair, A. Reci, A. J. Sederman, S. Reichelt, C.-B. Schönlieb, Enhancing joint reconstruction and segmentation with non-convex Bregman iteration, preprint, 2018
- L. Bungert, D. A. Coomes, M. J. Ehrhardt, J. Rasch, R. Reisenhofer and C.-B. Schönlieb, Blind Image Fusion for Hyperspectral Imaging with the Directional Total Variation, Inverse Problems 34(4), 044003, 2018
- P. J. Markiewicz, M. J. Ehrhardt, K. Erlandsson, P. J. Noonan, A. Barnes, J. M Schott, D. Atkinson, S. R. Arridge, B. F. Hutton and S. Ourselin, NiftyPET: A high-throughput software platform for high quantitative accuracy and precision PET imaging and analysis, Neuroinformatics 16(1), pp. 95–115, 2018
- Y.-J. Tsai, A. Bousse, M. J. Ehrhardt, C. W. Stearns, S. Ahn, B. F. Hutton, S. R. Arridge and K. Thielemans, Fast Quasi-Newton Algorithms for Penalized Reconstruction in Emission Tomography and Further Improvements via Preconditioning, IEEE Transactions on Medical Imaging 37(4), pp. 1000-1010, 2018
- M. Benning, M. M. Betcke, M. J. Ehrhardt, C.-B. Schönlieb, Choose Your Path Wisely: Gradient Descent in a Bregman Distance Framework, preprint, 2017
- M. J. Ehrhardt, P. Markiewicz, A. Chambolle, P. RichtaÌrik, J. Schott, C.-B. Schönlieb, Faster PET Reconstruction with a Stochastic Primal-Dual Hybrid Gradient Method, Proceedings of SPIE, 2017
- M. J. Ehrhardt, P. J. Markiewicz, M. Liljeroth, A. Barnes, V. Kolehmainen, J. S. Duncan, L. Pizarro, D. Atkinson, S. Ourselin, B. F. Hutton, K. Thielemans and S. R. Arridge, PET Reconstruction with an Anatomical MRI Prior using Parallel Level Sets, IEEE Transactions on Medical Imaging 35(9), pp. 2189–219, 2016
- M. J. Ehrhardt and M. M. Betcke, Multi-Contrast MRI Reconstruction with Structure-Guided Total Variation, SIAM Journal on Imaging Sciences 9(3), pp. 1084–1106, 2016
- M. J. Ehrhardt, K. Thielemans, L. Pizarro, D. Atkinson, S. Ourselin, B. F. Hutton and S. R. Arridge, Joint reconstruction of PET-MRI by exploiting structural similarity, Inverse Problems 31(1), 015001, 2015 (selected as Highlight of 2015)
- M. J. Ehrhardt and S. R. Arridge, Vector-Valued Image Processing by Parallel Level Sets, IEEE Transactions on Image Processing 23(1), pp. 9-18, 2014
Publications
Faster PET reconstruction with non-smooth priors by randomization and preconditioning
– Physics in medicine and biology
(2019)
64,
225019
(DOI: 10.1088/1361-6560/ab3d07)
Enhancing joint reconstruction and segmentation with non-convex Bregman iteration
– Inverse Problems
(2019)
35,
055001
(DOI: 10.1088/1361-6420/ab0b77)
Blind image fusion for hyperspectral imaging with the directional total variation
– Inverse Problems
(2018)
34,
044003
(DOI: 10.1088/1361-6420/aaaf63)
Stochastic Primal-Dual Hybrid Gradient Algorithm with Arbitrary Sampling and Imaging Applications
– SIAM Journal on Optimization
(2018)
28,
2783
(DOI: 10.1137/17M1134834)
NiftyPET: a High-throughput Software Platform for High Quantitative Accuracy and Precision PET Imaging and Analysis
– Neuroinformatics
(2018)
16,
95
(DOI: 10.1007/s12021-017-9352-y)
Fast Quasi-Newton Algorithms for Penalized Reconstruction in Emission Tomography and Further Improvements via Preconditioning
– IEEE transactions on medical imaging
(2018)
37,
1000
(DOI: 10.1109/tmi.2017.2786865)
Supporting Material for Blind Image Fusion for Hyperspectral Imaging with the Directional Total Variation
Uniform acquisition modelling across PET imaging systems: unified scatter modelling
– 2016 IEEE Nuclear Science Symposium, Medical Imaging Conference and Room-Temperature Semiconductor Detector Workshop, NSS/MIC/RTSD 2016
(2017)
2017-January,
(DOI: 10.1109/NSSMIC.2016.08069584)
Faster PET reconstruction with a stochastic primal-dual hybrid gradient method
– Proceedings of SPIE - The International Society for Optical Engineering
(2017)
10394,
103941o
(DOI: 10.1117/12.2272946)
PET Reconstruction With an Anatomical MRI Prior Using Parallel Level Sets
– IEEE transactions on medical imaging
(2016)
35,
2189
(DOI: 10.1109/tmi.2016.2549601)
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