Dr Matthias Joachim Ehrhardt
Matthias Joachim Ehrhardt is an applied mathematician with a background in medical imaging. He is best known for his pioneering work on joint image reconstruction for multimodality medical imaging.
Matthias Joachim Ehrhardt's academic interests include:
- Inverse problems
- Medical imaging
- Image processing
- Doctor of Philosophy in Medical Imaging, University College London, UK, 2015.
- Master’s degree with honors in Industrial Mathematics, University of Bremen, Germany, 2011.
Awards and prizes
- Paper selected as a Highlight of 2015, IOP Inverse Problems, 2015.
- Best Student Paper Finalist, IEEE Medical Imaging Conference (NSS-MIC), Seattle, USA, 2014.
- Travel Award, SIAM Conference on Imaging Science, Hong Kong, 2014.
- LMS Best Poster Award, Sparse Regularisation for Inverse Problems, Cambridge, UK, 2014.
Matthias Joachim Ehrhardt received his Master's degree with honours in industrial mathematics from the University of Bremen, Germany, in 2011 and his PhD in medical imaging from the University College London, UK, in 2015.
In 2016 he joined the Cambridge Image Analysis group at the Department for Applied Mathematics and Theoretical Physics as a postdoctoral research associate. Before joining the University of Cambridge he was a postdoctoral research associate at the Centre for Inverse Problems and the Centre for Medical Image Computing at the University College London.
His research is on inverse problems in medical imaging, from models to algorithms. One particular problem is joint image reconstruction that naturally arises in modern medical imaging. State of the art PET-MRI (positron emission tomography and magnetic resonance imaging) scanners simultaneously acquire functional PET and anatomical MRI data. As function follows structure, both images are likely to show similar structures.
A general aim of his research is to develop new methods that can exploit such expected correlation when these inverse problems are solved jointly. Besides, he is interested in stochastic optimisation techniques for faster image reconstruction with application to PET imaging.
His general research interests comprise inverse problems, optimization, and signal and image processing, in particular application of these techniques to medical imaging.
Traveling, football, squash.
Publications, links and resources
- Ehrhardt, M.J. and Betcke, M.M. (2016) Multi-Contrast MRI Reconstruction with Structure-Guided Total Variation. SIAM Journal on Imaging Science.
- Ehrhardt, M.J., Markiewicz, P.J., Liljeroth, M., Barnes, A., Kolehmainen, V., Duncan, J.S, Pizarro, L., Atkinson, D., Ourselin, S., Hutton, B.F., Thielemans, K., and Arridge, S.R. (2016) PET Reconstruction with an Anatomical MRI Prior using Parallel Level Sets. IEEE Transaction on Medical Imaging.
- Ehrhardt, M.J., Thielemans, K., Pizarro, L., Atkinson, D., Ourselin, S., Hutton, B. F., and Arridge, S.R. Joint Reconstruction of PET-MRI by Exploiting Structural Similarity. Inverse Problems 31(1) (2015), 015001.
- Ehrhardt, M.J. and Arridge, S.R. Vector-Valued Image Processing by Parallel Level Sets. IEEE Transactions on Image Processing 23(1) (2014), pp 9–18.
- Ehrhardt, M.J., Villinger, H., and Schiffler, S. Evaluation of Decomposition Tools for Sea Floor Pressure Data: A Practical Comparison of Modern and Classical Approaches. Computers & Geosciences 45 (2012), pp 4–12.