Matthias J. Ehrhardt

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Research

* denotes alphabetical order of authors

Peer-Reviewed Journal Publications

2019M. J. Ehrhardt, P. J. Markiewicz, C.-B. Schönlieb, Faster PET Reconstruction with Non-Smooth Priors by Randomization and Preconditioning, accepted in Physics in Medicine & Biology [arXiv] [IOP] [slides (8MB)]
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, Inverse Problems 35(5), 055001 [IOP] [arXiv]
V. Kolehmainen, M. J. Ehrhardt, S. R. Arridge. Incorporating Structural Prior Information and Sparsity into EIT using Parallel Level Sets, Inverse Problems and Imaging, 13(2), 285–307
2018* A. Chambolle, M. J. Ehrhardt, P. Richtárik, C.-B. Schönlieb, Stochastic Primal-Dual Hybrid Gradient Algorithm with Arbitrary Sampling and Imaging Applications, SIAM Journal on Optimization 28(4), 2783-2808 [print version] [arXiv] [slides (<1MB)] [poster (2 MB)] [software@github] [software@ODL]
* L. Bungert, D. A. Coomes, M. J. Ehrhardt, J. Rasch, R. Reisenhofer, C.-B. Schönlieb, Blind Image Fusion for Hyperspectral Imaging with the Directional Total Variation, Inverse Problems 34(4), 044003 [print version] [arXiv] [software@github] [software@gitcam]
Y.-J. Tsai, A. Bousse, M. J. Ehrhardt, C. W. Stearns, S. Ahn, B. F. Hutton, S. R. Arridge, K. Thielemans, Fast Quasi-Newton Algorithms for Penalized Reconstruction in Emission Tomography and Further Improvements via Preconditioning, IEEE Transactions on Medical Imaging 37(4), 1000-1010 [print version]
P. J. Markiewicz, M. J. Ehrhardt, K. Erlandsson, P. J. Noonan, A. Barnes, J. M Schott, D. Atkinson, S. R. Arridge, B. F. Hutton, S. Ourselin, NiftyPET: A high-throughput software platform for high quantitative accuracy and precision PET imaging and analysis, Neuroinformatics 16(1), 95–115 [print version] [software]
2016M. J. Ehrhardt, M. M. Betcke, Multi-Contrast MRI Reconstruction with Structure-Guided Total Variation, SIAM Journal on Imaging Sciences 9(3), 1084–1106 [print version] [arXiv] [poster (2 MB)] [software]
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, S. R. Arridge, PET Reconstruction with an Anatomical MRI Prior using Parallel Level Sets, IEEE Transactions on Medical Imaging 35(9), 2189–2199 [print version]
2015M. J. Ehrhardt, K. Thielemans, L. Pizarro, D. Atkinson, S. Ourselin, B. F. Hutton, S. R. Arridge, Joint reconstruction of PET-MRI by exploiting structural similarity, Inverse Problems 31(1), 015001 (selected as Highlight of 2015) [print version] [software]
2014M. J. Ehrhardt, S. R. Arridge, Vector-Valued Image Processing by Parallel Level Sets, IEEE Transactions on Image Processing 23(1), 9-18 [print version] [preprint (11MB)] [software]
2012M. J. Ehrhardt, H. Villinger, S. Schiffler, Evaluation of Decomposition Tools for Sea Floor Pressure Data: A Practical Comparison of Modern and Classical Approaches, Computers & Geosciences 45, 4-12 [print version] [arXiv]

Preprints

2019 F. Sherry, M. Benning, J. C. De los Reyes, M. J. Graves, G. Maierhofer, G. Williams, C.-B. Schönlieb, M. J. Ehrhardt, Learning the Sampling Pattern for MRI, [arXiv]
* M. Benning, E. Celledoni, M. J. Ehrhardt, B. Owren, C.-B. Schönlieb, Deep learning as optimal control problems: models and numerical methods, [arXiv]
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, [arXiv]
E. S. Riis, M. J. Ehrhardt, G. R. W. Quispel, C.-B. Schönlieb, A geometric integration approach to nonsmooth, nonconvex optimisation, [arXiv]
2017* M. Benning, M. M. Betcke, M. J. Ehrhardt, C.-B. Schönlieb, Choose Your Path Wisely: Gradient Descent in a Bregman Distance Framework, [arXiv]

Peer-Reviewed Conference Proceedings

2016P. Markiewicz, M. J. Ehrhardt, N. Burgos, D. Atkinson, S. R. Arridge, B. F. Hutton, S. Ourselin, Unified acquisition modelling across PET imaging systems: unified scatter modelling, IEEE Nuclear Science Symposium & Medical Imaging Conference [UCL discovery]
2015Y.-J. Tsai, A. Bousse, M. J. Ehrhardt, B. F. Hutton, S. R. Arridge, K. Thielemans, Performance Evaluation of MAP Algorithms with Different Penalties, Object Geometries and Noise Levels, IEEE Nuclear Science Symposium & Medical Imaging Conference [print version]
2014 P. Markiewicz, K. Thielemans, M. J. Ehrhardt, J. Jiao, N. Burgos, D. Atkinson, S. R. Arridge, B. F. Hutton, S. Ourselin, High Throughput CUDA Implementation of Accurate Geometric Modelling for Iterative Reconstruction of PET Data , IEEE Nuclear Science Symposium & Medical Imaging Conference [print version]
M. J. Ehrhardt, K. Thielemans, L. Pizarro, P. Markiewicz, D. Atkinson, S. Ourselin, B. F. Hutton, S. R. Arridge, Joint Reconstruction of PET-MRI by Parallel Level Sets , IEEE Nuclear Science Symposium & Medical Imaging Conference (best student paper finalist) [print version]

Miscellaneous

2018 * L. Bungert, M. J. Ehrhardt, R. Reisenhofer, Robust Blind Image Fusion for Misaligned Hyperspectral Imaging Data, Proceedings in Applied Mathematics & Mechanics, Vol. 18, e201800033 [print version]
2017M. J. Ehrhardt, P. Markiewicz, A. Chambolle, P. Richtárik, J. Schott, C.-B. Schönlieb, Faster PET Reconstruction with a Stochastic Primal-Dual Hybrid Gradient Method, Proceedings of SPIE [SPIE library] [preprint (2 MB)] [poster (2 MB)]
2016 * M. Benning, M. M. Betcke, M. J. Ehrhardt, C.-B. Schönlieb, Gradient descent in a generalised Bregman distance framework, MI Lecture Notes series of Kyushu University, Vol. 74 [print version] [arXiv] [code]
2015 M. J. Ehrhardt, Joint Reconstruction for Multi-Modality Imaging with Common Structure, Ph.D. Thesis, University College London, UK [pdf@UCL]
2011 M. J. Ehrhardt, Sparsity in Geosciences: Sparse Decomposition for Analysis of Sea Floor Pressure Data, Diploma Thesis, University of Bremen, Germany [pdf (5 MB)]

Communication

# denotes invited presentations

Oral Presentations at Conferences, Workshops and Seminars

2019 2nd IMA Conference On Inverse Problems From Theory To Application, London, UK. Faster PET Reconstruction with Non-Smooth Priors by Randomization [slides (7MB)] [paper, IOP] [preprint]
# Applied Inverse Problems, Grenoble, France. Faster PET Reconstruction with Non-Smooth Priors by Randomization and Preconditioning [slides (8MB)] [preprint]
# Center for Inverse Problems Seminar, UCL, London, UK. A Randomized Algorithm for Convex Optimization and Medical Imaging Applications [slides (<1MB)]
# SAMBa's 9th Integrative Think Tank, Bath, UK. Regularisation of inverse problems [slides (<1MB)]
# Bath/RAL Numerical Analysis Day, Bath, UK. A Randomized Algorithm for Convex Optimization and Medical Imaging Applications
2018 # Numerical Analysis Seminar, Bath, UK. A story of modern challenges in inverse problems in imaging
# ISMP 2018: International Symposium on Mathematical Programming, Bordeaux, France. Stochastic PDHG with Arbitrary Sampling and Applications to Medical Imaging
SIAM Conference on Imaging Science, Bologna, Italy. Faster PET-MR Image Reconstruction by Stochastic Optimization
# Applied and Interdisciplenary Mathematics Seminar, University of Bath, UK. A Randomized Algorithm for Convex Optimization and Medical Imaging Applications
# Scientific Computing Seminar, DTU, Denmark. A Randomized Algorithm for Convex Optimization and Medical Imaging Applications
# Optimization and Big Data, KAUST, Saudi Arabia. Stochastic PDHG with Non-Uniform Sampling and Applications to Medical Imaging
2017 # Mathemematics and Applications Seminar, University of Sussex, UK. Stochastic Optimization for Non-Smooth Imaging Applications
5th Heidelberg Laureate Forum, Heidelberg, Germany. Stable Architectures for Deep Neural Networks
IMA Conference on Inverse Problems from Theory to Application, Cambridge, UK. Stochastic Primal-Dual Hybrid Gradient Method
# SPIE Optics+Photonics: Wavelets and Sparsity XVII, San Diego, USA. Faster PET Reconstruction with a Stochastic Primal-Dual Hybrid Gradient Method [preprint] [pdf] [video]
# 27th Biennial NA Conference in Strathclyde, Glasgow, UK. Accelerated Stochastic PDHG by Non-Uniform Sampling
Applied Inverse Problems, Hangzhou, China. # Faster PET Reconstruction with a Stochastic Primal-Dual Hybrid Gradient Method, Accelerated Stochastic PDHG by Non-Uniform Sampling
# Mini Workshop on Bayesian Inverse Problems and Imaging, Jiao Tong University, Shanghai, China. Faster PET Reconstruction with a Stochastic Primal-Dual Hybrid Gradient Method
British Applied Mathematics Colloquium, Guildford, UK. Faster PET Reconstruction with a Stochastic Primal-Dual Hybrid Gradient Method
# 100 Years of the Radon Transform, Linz, Austria. Faster PET Reconstruction with a Stochastic Primal-Dual Hybrid Gradient Method, Multi-Contrast MRI Reconstruction with Structure-Guided Total Variation
# Mathematical imaging with partially unknown models, Cambridge, UK. Discrete Gradients for Non-Smooth Bi-Level Learning
2016 # Numerical Analysis Seminar, KTH Stockholm, Sweden. Combined Image Reconstruction for Combined Medical Imaging
# SIAM Conference on Imaging Science, Albuquerque, USA. Multi-Contrast MRI Reconstruction with Structure-Guided Total Variation [video]
# Big Data, Multimodality & Dynamic Models in Biomedical Imaging, Cambridge, UK. Combined Image Reconstruction for Combined PET-MR Imaging [video]
# Edinburgh Research Group in Optimization, Edinburgh, UK. Optimization based Image Reconstruction in Medical Imaging: Models and Challenges
# UCL PET/MR Symposium, London, UK. Combined Image Reconstruction for Combined PET-MR Imaging
2015Applied Inverse Problems Conference, Helsinki, Finland. Joint Reconstruction of PET-MRI by Exploiting Structural Similarity
# Data Processing Challenges in PET-MR, London, UK. Joint Reconstruction of Simultaneously Acquired PET-MRI by Exploiting Structural Similarity
# The 4th Joint British Mathematical Colloquium and British Applied Mathematics Colloquium, University of Cambridge, UK. Joint Reconstruction of Simultaneously Acquired PET-MRI by Structural Similarity
2014 # UCL Centre for Medical Image Computing Seminar, London, UK. Joint Reconstruction of PET-MRI by Exploiting Structural Similarity
IEEE Nuclear Science Symposium & Medical Imaging Conference, Seattle, USA. Joint Reconstruction of PET-MRI by Parallel Level Sets (best student paper finalist)
# STIR User Meeting at IEEE NSS/MIC, Seattle, USA. STIR in MATLAB: More tools for Emission Tomography
# UCL Institute for Nuclear Medicine Seminar, London, UK. Joint reconstruction of PET-MRI by Parallel Level Sets
# Oberseminar Angewandte Mathematik / Seminar AG Imaging, Westfaelische Wilhelms-Universitaet Muenster, Germany. Joint Reconstruction of PET-MRI by Exploiting Structural Similarity [abstract]
Imaging with Modulated/Incomplete Data, Graz, Austria. Correct A Priori Information Modelling for Sparse MRI Reconstruction [abstract (<1MB)]
Inverse Problems: Modelling and Simulation, Fethiye, Turkey. Joint Reconstruction of PET-MRI by Parallel Level Sets [abstract (<1MB)]
SIAM Conference on Imaging Science, Hong Kong Baptist University, Hong Kong. Parallel Level Set Prior for Joint PET/MRI Reconstruction
2013Inverse Days, Inari, Finland. Multi-Modality Image Reconstruction with Parallel Level Sets
Image Reconstruction in Emission Tomography and Hybrid Imaging, University College London, UK. Joint Reconstruction - Exploiting Similar Structures in Multi-Modal Imaging.

Poster Presentations

2019Royal United Hospital, Bath, UK. Mathematical Innovation for PET and MRI Imaging [poster (4 MB)]
2017Generative Models, Parameter Learning and Sparsity, Cambridge, UK. Stochastic Primal-Dual Hybrid Gradient Algorithm [poster (2 MB)]
Developments in Healthcare Imaging - Connecting with Industry, Cambridge, UK. Stochastic Primal-Dual Hybrid Gradient Algorithm [poster (2 MB)]
Variational Methods, New Optimisation Techniques and New Fast Numerical Algorithm, Cambridge, UK. Stochastic Primal-Dual Hybrid Gradient Algorithm [poster (2 MB)]
2016 EPSRC Centre for Mathematical & Statistical Analysis of Multimodal Clinical Imaging Launch Event, Cambridge, UK. Multi-Contrast MRI Reconstruction with Structure-Guided Total Variation [poster (2 MB)]
University of Cambridge Mathematics and Big Data Showcase, Cambridge, UK. Multi-Contrast MRI Reconstruction with Structure-Guided Total Variation [poster (2 MB)]
Cantab Capital Institute for the Mathematics of Information - Launch Event, Cambridge, UK. Multi-Contrast MRI Reconstruction with Structure-Guided Total Variation [poster (2 MB)]
LMS Inverse Day: Big Inverse Problems, Nottingham, UK. Multi-Contrast MRI Reconstruction with Structure-Guided Total Variation [poster (2 MB)]
High-dimensional Statistics, Inverse Problems and Convex Analysis, London, UK. Poster: Multi-Contrast MRI Reconstruction with Structure-Guided Total Variation [poster (2 MB)]
2014 LMS Inverse Day: Sparse Regularisation for Inverse Problems, Isaac Newton Institute for Mathematical Sciences, Cambridge, UK. Joint Reconstruction of PET/MRI by Parallel Level Sets (best poster award) [poster (1MB)]
2013 Applied Inverse Problem Conference, KAIST, Daejeon, South Korea. Joint Reconstruction with Parallel Level Sets [poster (2MB)]
© 2014-2019 Matthias J. Ehrhardt - m.ehrhardt@bath.ac.uk - last updated: Sep 2019