* denotes alphabetical order of authors

2023 | * D. Chen, M. Davies, M. J. Ehrhardt, C.-B. Schönlieb, F. Sherry and J. Tachella, Imaging with Equivariant Deep Learning: From unrolled network design to fully unsupervised learning, IEEE Signal Processing Magazine 40(1), 134-147, [print] [preprint] |
---|---|

2022 | M. J. Ehrhardt, F. A. Gallagher, M. A. McLean and C.-B. Schönlieb, Enhancing the spatial resolution of hyperpolarized carbon‐13 MRI of human brain metabolism using structure guidance, Magnetic Resonance in Medicine 87(3), 1301–1312 (selected as No 1 Pick of March 2022), [print] [software@zenodo] |

2021 | * E. Celledoni, M. J. Ehrhardt, C. Etmann, B. Owren, C.-B. Schönlieb, F. Sherry, Equivariant neural networks for inverse problems, Inverse Problems 37(8), 085006 [print] [preprint] [software@github] |

E. S. Riis, M. J. Ehrhardt, G. R. W. Quispel, C.-B. Schönlieb, A geometric integration approach to nonsmooth, nonconvex optimisation, Foundations of Computational Mathematics, [print] [preprint] [video by Erlend] | |

E. Cueva, A. Meaney, S. Siltanen, M. J. Ehrhardt, Synergistic Multi-spectral CT Reconstruction with Directional Total Variation, Philosophical Transactions of the Royal Society A 379, 20200198 [print] [preprint] | |

R. Brown, C. Kolbitsch, C. Delplancke, E. Papoutsellis, J. Mayer, E. Ovtchinnikov, E. Pasca, R. Neji, C. da Costa-Luis, A. G. Gillman, M. J. Ehrhardt, J. R. McClelland, B. Eiben and K. Thielemans Motion estimation and correction for simultaneous PET/MR using SIRF and CIL, Philosophical Transactions of the Royal Society A 379, 20200208 [print] | |

* S. R. Arridge, M. J. Ehrhardt, K. Thielemans, (An overview of) Synergistic reconstruction for multimodality/multichannel imaging methods, Philosophical Transactions of the Royal Society A 379, 20200205 [print] | |

* E. Celledoni, M. J. Ehrhardt, C. Etmann, R. I. McLachlan, B. Owren, C.-B. Schönlieb, F. Sherry, Structure-preserving deep learning, European Journal of Applied Mathematics 32(5), 888-936 (John Ockendon Prize 2022) [print] [preprint] [video] | |

* M. Benning, M. M. Betcke, M. J. Ehrhardt, C.-B. Schönlieb, Choose Your Path Wisely: Gradient Descent in a Bregman Distance Framework, SIAM Journal on Imaging Sciences 14(2), 814-843 [print] [preprint] | |

* M. J. Ehrhardt, L. Roberts, Inexact Derivative-Free Optimization for Bilevel Learning, Journal of Mathematical Imaging and Vision 63(5), 580-600, [print] [preprint] [software@github] | |

2020 | * L. Bungert, M. J. Ehrhardt, Robust Image Reconstruction with Misaligned Structural Information, IEEE Access 8, 222944-222955, [print] [preprint] [software@github] |

* D. Driggs, M. J. Ehrhardt, C.-B. Schönlieb, Accelerating Variance-Reduced Stochastic Gradient Methods, Mathematical Programming [print] [preprint] | |

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, IEEE Transactions on Medical Imaging 39(12), 4310–4321 [print] [preprint] [software@zenodo] | |

E. Ovtchinnikov, R. Brown, C. Kolbitsch, E. Pasca, C. da Costa-Luis, A. G. Gillman, B. A. Thomas, N. Efthimiou, J. Mayer, P. Wadhwa, M. J. Ehrhardt, S. Ellis, J. S. Jørgensen, J. Matthews, C. Prieto, A. J. Reader, C. Tsoumpas, M. Turner, D. Atkinson, K. Thielemans, SIRF: Synergistic Image Reconstruction Framework, Computer Physics Communication 249, 107087 [print] | |

2019 | * M. Benning, E. Celledoni, M. J. Ehrhardt, B. Owren, C.-B. Schönlieb, Deep learning as optimal control problems: models and numerical methods, Journal of Computational Dynamics 6(2), 171–198 [print] [preprint] |

M. J. Ehrhardt, P. J. Markiewicz, C.-B. Schönlieb, Faster PET Reconstruction with Non-Smooth Priors by Randomization and Preconditioning, Physics in Medicine & Biology 64(22), 225019 [print] [preprint] [slides (8 MB)] | |

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 [print] [preprint] | |

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 [print] | |

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] [preprint] [slides (1 MB)] [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] [preprint] [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] | |

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] [software] | |

2016 | M. J. Ehrhardt, M. M. Betcke, Multi-Contrast MRI Reconstruction with Structure-Guided Total Variation, SIAM Journal on Imaging Sciences 9(3), 1084–1106 [print] [preprint] [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] | |

2015 | M. 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] [software (59 MB)] |

2014 | M. J. Ehrhardt, S. R. Arridge, Vector-Valued Image Processing by Parallel Level Sets, IEEE Transactions on Image Processing 23(1), 9-18 [print] [preprint (11 MB)] [software] |

2012 | M. 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] [preprint] |

2021 | C. Delplancke, K. Thielemans, M. J. Ehrhardt, ‘Accelerated Convergent
Motion Compensated Image Reconstruction', IEEE Nuclear Science Symposium and Medical Imaging Conference |
---|---|

R. Brown, C. Kolbitsch, E. Ovtchinnikov, J. Mayer, A. G. Gillman, E. Pasca, C. Delplancke, E. Papoutsellis, G. Fardell, R. Neji, C. da Costa-Luis, J. Mcclelland, B. Eiben, M. J. Ehrhardt, K. Thielemans, ‘Status update on the Synergistic Image Reconstruction Framework: Version 3.0', 16th Int. Meet. Fully 3D Image Reconstr. Radiol. Nucl. Med., pp. 440–443. | |

E. B. Gutierrez, C. Delplancke, M. J. Ehrhardt, Convergence Properties of a Randomized Primal-Dual Algorithm with Applications to Parallel MRI, Elmoataz A., Fadili J., Quéau Y., Rabin J., Simon L. (eds) Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, vol 12679. Springer. [print] [preprint] | |

2020 | * M. J. Ehrhardt, L. Roberts, Efficient Hyperparameter Tuning with Dynamic Accuracy Derivative-Free Optimization, NeurIPS 2020 workshop OPT2020: Optimization for Machine Learning [preprint] [print] [poster] |

C. Delplancke, M. Gurnell, J. Latz, P. J. Markiewicz, C. Schönlieb, and M. J. Ehrhardt, Improving a Stochastic Algorithm for Regularized PET Image Reconstruction, IEEE Nuclear Science Symposium and Medical Imaging Conference [print] | |

2019 | D. Kazantsev, E. Pasca, M. Basham, M. Turner, M. J. Ehrhardt, K. Thielemans, B. A. Thomas, E. Ovtchinnikov, P. J. Withers, A. W. Ashton, Versatile regularisation toolkit for iterative image reconstruction with proximal splitting algorithms, Proceedings of SPIE 11072 Fully 3D, 2019 [print] |

2016 | P. 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 [print] [preprint] |

2015 | Y.-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] |

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] |

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] |

2021 | S. Cortinhas, M. Golbabaee, M. J. Ehrhardt, A temporal multiscale approach for tissue quantification using MR Fingerprinting acquisitions, presented at ISBI 2021 [preprint] |
---|---|

M. J. Ehrhardt, Multi-modality imaging with structure-promoting regularisers, Springer Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging [print] [preprint] [software@github] | |

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] |

2017 | M. 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 10394 Wavelets and Sparsity XVII [print] [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] [preprint] [software] |

2015 | M. J. Ehrhardt, Joint Reconstruction for Multi-Modality Imaging with Common Structure, Ph.D. Thesis, University College London, UK [pdf] |

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)] |

# denotes invited presentations

2023 | # Deep learning for Industry workshop, Bath, UK. Structure-Preserving Deep Learning [slides (4 MB)] |
---|---|

2022 | # Data-Enabled Science Seminar, Houston, US (virtual). Equivariant Neural Networks for Inverse Problems [slides (11 MB)] |

# Advanced Image Reconstruction Methods, UCL, UK. Randomized Image Reconstruction [slides (2 MB)] [video] | |

# Analytic and Geometric Approaches to Machine Learning, Bath, UK. Bilevel Learning for Inverse Problems [slides (7 MB)] | |

# HCM Workshop: Synergies between Data Science and PDE Analysis, Bonn, Germany. Bilevel Learning for Inverse Problems [slides (7 MB)] | |

# SIAM Imaging Science (virtual). Robust Image Reconstruction with Misaligned Structural Information [slides (5 MB)] | |

# Computing Seminar, University of Buckingham, UK (virtual). Bilevel Learning for Inverse Problems [slides (8 MB)] | |

# Mathematics of Data Science Seminar, University of Graz, Austria (virtual). A Randomized Algorithm for Convex Optimization and Medical Imaging Applications [slides (2 MB)] | |

# Inverse Problems Methods, Applications and Synergies, Pontificia Universidad Católica de Chile, Chile (virtual). Equivariant Neural Networks
for Inverse Problems [slides (10 MB)] | |

2021 | # International SPINlab Users Webconference, UCSF, San Francisco, US (virtual). Enhancing the Spatial Resolution of
Hyperpolarized Carbon-13 MRI of Human Brain Metabolism using Structure Guidance [slides (2 MB)] |

# Mathematical Optimization Group Research Seminar, University of Tuebingen, Germany. Bilevel Learning for Inverse Problems [slides (8 MB)] | |

# Deep Learning and Inverse Problems, Isaac Newton Institute, Cambridge, UK. Equivariant Neural Networks for Inverse Problems [slides (12 MB)] [video] | |

# One World Seminar Series on the Mathematics of Machine Learning (virtual). Bilevel Learning for Inverse Problems [slides (8 MB)] [video] | |

# Theory of Deep Learning, Isaac Newton Institute, Cambridge, UK (virtual). Structure-Preserving Deep Learning [slides (9 MB)] [video] | |

# ICMS-LMS Analytic and Geometric Approaches to Machine Learning (virtual). Equivariant Neural Networks for Inverse Problems [slides (12 MB)] | |

# Clinical Molecular Imaging Group, Cambridge, UK (virtual). Enhancing the Spatial Resolution of
Hyperpolarized Carbon-13 MRI of Human Brain Metabolism using Structure Guidance [slides (2 MB)] | |

# Signal and Image Processing Laboratory seminar, Heriot-Watt University, Edinburgh, UK (virtual). Bilevel Learning for Inverse Problems [slides (8 MB)] | |

# Differential Equations and Numerical Analysis Seminar, NTNU, Norway (virtual). Bilevel Learning for Inverse Problems [slides (9 MB)] | |

# Centre for Inverse Problems Seminar, UCL, UK (virtual). Bilevel Learning for Inverse Problems [slides (11 MB)] | |

# Mathematics of Deep Learning Seminar, FAU Erlangen-Nuremberg, Germany (virtual). Structure Preserving Deep Learning [slides (5 MB)] [video] | |

2020 | # SIAM Mathematics of Data Science (virtual). Learning the sampling for MRI [slides (9 MB)] [video] |

# Scottish Numerical Methods Network 2020: Inverse problems and optimisation for PDEs, Edinburgh, UK (virtual). Optimising MRI Sampling with Bi-Level Learning [slides (9 MB)] | |

2019 | # Synergistic Reconstruction Symposium, Chester, UK. Faster PET Reconstruction
with Non-Smooth Anatomical Priors by Randomization and Preconditioning [slides (2 MB)] |

# Applied Mathematics Seminar, University of Leicester, UK. A Randomized Algorithm for Non-Smooth Optimization and Medical Imaging Applications [slides (1 MB)] | |

# Quantitative Imaging of Electrochemical Interfaces Workshop, Diamond Light Source, Harwell Campus, UK. 1 + 1 > 2? Getting More Out of Multi-Modality Imaging [slides (7 MB)] | |

2nd IMA Conference On Inverse Problems From Theory To Application, London, UK. Faster PET Reconstruction with Non-Smooth Priors by Randomization [slides (7 MB)] [print] [preprint] | |

# Applied Inverse Problems, Grenoble, France. Faster PET Reconstruction with Non-Smooth Priors by Randomization and Preconditioning [slides (8 MB)] [preprint] | |

# Center for Inverse Problems Seminar, UCL, London, UK. A Randomized Algorithm for Convex Optimization and Medical Imaging Applications [slides (1 MB)] | |

# SAMBa's 9th Integrative Think Tank, Bath, UK. Regularisation of inverse problems [slides (1 MB)] | |

# 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 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] [print] [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 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 | |

2015 | Applied 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 (1 MB)] | |

Inverse Problems: Modelling and Simulation, Fethiye, Turkey. Joint Reconstruction of PET-MRI by Parallel Level Sets [abstract (1 MB)] | |

SIAM Imaging Science, Hong Kong Baptist University, Hong Kong. Parallel Level Set Prior for Joint PET/MRI Reconstruction | |

2013 | Inverse 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. |

2023 | # Interfacing Bayesian statistics, machine learning, applied analysis, and blind and semi-blind imaging inverse problems, ICMS, Edinburgh, UK. Generative Regularizers for Inverse Imaging Problems [poster (3 MB)] |
---|---|

2019 | Royal United Hospital, Bath, UK. Mathematical Innovation for PET and MRI Imaging [poster (4 MB)] |

2017 | Generative 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 (1 MB)] |

2013 | Applied Inverse Problem Conference, KAIST, Daejeon, South Korea. Joint Reconstruction with Parallel Level Sets [poster (2 MB)] |

© 2014-2022 Matthias J. Ehrhardt - m.ehrhardt@bath.ac.uk - last updated: 01/2023