EPSRC Centre for Mathematical and Statistical Analysis of Multimodal Clinical Imaging

Lead Research Organisation: University of Cambridge
Department Name: Pure Maths and Mathematical Statistics

Abstract

Applied Mathematics and Statistics are routinely seen as separate disciplines. However, many of the methodological challenges in image analysis, particularly from the types of multiscale multimodal images available from Neurological, Cardiovascular and Oncology imaging, illustrate that a combined approach, dissolving intradisciplinary mathematical boundaries, is the only possible way forward if a step-change in image analysis of combined data is to occur. This Centre will foster links between applied maths and statistics, particularly high dimensional and functional statistical analysis with applied and computational numerical analysis, through the focus on multimodal imaging data. The Centre proposes to provide a research focus on bringing state-of-the-art mathematical tools to clinical end users through collaborations both within mathematics and between mathematics and healthcare professionals, particularly those in oncology, cardiovascular medicine and neurology. This will ultimately lead to new mathematical frontiers, joining statistics and computational mathematics, as well as a move away from individual image analysis to a holistic approach to all available imaging, from the cellular to systems scale, for clinical diagnosis, prognosis and treatment planning.

Planned Impact

The Centre will focus on making an impact within mathematical sciences as well as delivering breakthroughs in image analysis for a number of critical healthcare areas. We will channel efforts of researchers in applied mathematics and statistics to build a common understanding of their (currently disparate) disciplines. Such a shift requires a common focus, and by concentrating efforts on clinical image analysis, such a goal can be realised. The advances generated by the Centre will go well beyond mathematical sciences, to directly influence clinical practice. Mathematicians, statisticians, clinicians and scientists with track records in turning theoretical advances into clinical practice will incorporate the most modern analysis techniques into clinical protocols, taking a multimodal, holistic approach to all available imaging data. By combining imaging data with other clinical covariates such as digitised medical records, clinicians will come to better informed decisions regarding diagnosis, prognosis and treatment options.

We will concentrate on three specific medical areas containing some of the most pressing needs in medicine. Oncology imaging ranges from tumour cell images, to histopathology, to whole organ MRI and PET scans, and we will build models which integrate such disparate data into a single diagnosis or prognosis framework. Cardiovascular imaging has particular issues due to the inherent movement of the heart. We will generate considerably enhanced real time imaging solutions for cardiac imaging, with potential advances, particularly in diagnosis. We will improve on the use of neurological imaging data for diseases such as Alzheimer's and dementia. Moreover, in clinical populations with traumatic brain injury or disorders of consciousness, in-vivo neurological imaging goes beyond simple diagnosis and becomes part of the "treatment", to interact directly with patients who have little to no other form of communication available. For this we will develop accurate statistical prediction and classification models, along with computational methods, allowing them to be used in real time.

The model of engagement at the heart of the centre will clearly demonstrate the benefits to researchers of working together on problems of mutual interest. Successful collaborations between the physical and clinical sciences require that academic rigour and clinical relevance are well-aligned, with projects selected that offer the potential to develop truly exciting and cutting-edge mathematics, while addressing areas of medicine in which these advances are most needed (rather than only offering incremental clinical benefit). Our model for the co-creation of projects with mathematics and clinical leads is, we believe, fundamental to the development of genuinely challenging and relevant outputs, and will provide a model for these types of engagement in the future.

Developments in healthcare technology have widespread impacts beyond academic research. Improvements in diagnostic and prognostic tools, and the prospect of integrating real-time image analysis into treatments, can provide a step change in care, improving outcomes and reducing cost. Beneficiaries will include patients, clinicians, hospitals, the NHS (and other healthcare services worldwide) and ultimately taxpayers. Technology companies will benefit from our innovations - both our immediate partners via IP developed as part of the Centre, but also the wider healthcare tech community, through our publications, IP licensing and the open-source software tools we are committed to developing. Our outputs will deliver further academic and economic impact by training high-calibre researchers with expertise and skills relevant to the new cross-disciplinary data science challenges of the 21st century. We will engage early and often with policymakers (e.g. in the Department of Health and NICE), and with the public, demonstrating the deep relevance of mathematics to health.

Organisations

Publications

10 25 50
 
Title Creative Reactions 
Description Inspired by our research, a local artist produced a creative reaction to our Pint of Science presentation 
Type Of Art Artwork 
Year Produced 2017 
Impact Artist was featured on local TV 
 
Description This research is primarily about linking mathematics and statistics to modern healthcare problems. We have seen that mathematical challenges arise in almost all areas of medical and healthcare imaging, and combining data in all its forms together to make the most of a patient's own data, is one of the premier research challenges of our time. We have looked at biomechanical models for imaging the heart, statistical models for understanding the brain, and combined maths and statistics models for getting the most out of the whole imaging process.

We feel it is increasingly understood that medical data really needs both doctors and quantitative scientists to be involved in the analysis, to really help patients get the best diagnosis and prognosis possible. Various projects within the centre have shown the benefits of linking applied mathematics with statistics to address these challenges, and the work of the centre to date has highlighted the importance of both specialties working alongside both clinicians and industry in this field.
Exploitation Route We feel that we have the opportunity to allow health professionals right across the board make use of some of the most cutting edge data analytics procedures, and we are excited for the engagements we have both now and those we are now planning for the future.
Sectors Healthcare,Manufacturing, including Industrial Biotechology,Pharmaceuticals and Medical Biotechnology

URL http://www.cmih.maths.cam.ac.uk
 
Description Now 3 years into its funding cycle the CMIH is establishing itself as a focal point for engagement between academia, clinicians and companies. Our events are now routinely attended by all of these groups. These events have provided a much needed networking space to bring together these usually very separate groups, enabling a better understanding of the challenges within the field, and a better appreciation of the relevant academic research. Working with industry we have assisted them in connecting with both academics and healthcare professionals, providing indirect benefits to them as companies. As the work of the centre grows, the benefits to both named industry partners, and to other industry contacts who connect with the centre will continue to grow. The benefits the centre can provide in the field of healthcare benefits to the UK as a whole, through improved health services, as well as economically through the associated benefits to industry.
Sector Healthcare,Pharmaceuticals and Medical Biotechnology
Impact Types Societal

 
Description PhD student training within the Cantab Capital Institute of the Mathematics of Information
Geographic Reach Multiple continents/international 
Policy Influence Type Influenced training of practitioners or researchers
URL http://www.damtp.cam.ac.uk/user/cbs31/MoI/Welcome.html
 
Description EPSRC Healthcare Impact Partnership: PET++: Improving Localisation, Diagnosis and Quantification in Clinical and Medical PET Imaging with Randomised Optimisation
Amount £1,000,000 (GBP)
Funding ID EP/S026045/1 
Organisation Engineering and Physical Sciences Research Council (EPSRC) 
Sector Academic/University
Country United Kingdom
Start 09/2019 
End 08/2022
 
Description EPSRC project EP/R008272/1 Microscopy with neutral helium atoms: A wide-ranging new technique for delicate samples
Amount £1,100,000 (GBP)
Funding ID EPSRC project EP/R008272/1 
Organisation Engineering and Physical Sciences Research Council (EPSRC) 
Sector Academic/University
Country United Kingdom
Start 11/2017 
End 10/2020
 
Description Industrial funding
Amount £61,000 (GBP)
Organisation PreXion 
Sector Private
Country United States
Start 03/2017 
End 12/2017
 
Description Isaac Newton Institute research programme on Variational methods and effective algorithms for imaging and vision
Amount £80,000 (GBP)
Organisation Isaac Newton Institute for Mathematical Sciences 
Sector Academic/University
Country United Kingdom
Start 08/2017 
End 12/2017
 
Description Isaac Newton Programme on Statistical Scalability
Amount £180,000 (GBP)
Funding ID Statistical Scalability 
Organisation Isaac Newton Institute for Mathematical Sciences 
Sector Academic/University
Country United Kingdom
Start 01/2018 
End 06/2018
 
Description LMS Undergraduate Research Bursary for Bilevel optimisation for learning the sampling pattern in Magnetic Resonance Tomography
Amount £1,400 (GBP)
Organisation London Mathematical Society 
Sector Learned Society
Country United Kingdom
Start 07/2016 
End 09/2016
 
Description LMS Undergraduate Research Bursary for Bilevel optimisation for learning the sampling pattern in Magnetic Resonance Tomography, summer 2016
Amount £1,400 (GBP)
Organisation London Mathematical Society 
Sector Learned Society
Country United Kingdom
Start 07/2016 
End 09/2016
 
Description MRC MB PhD funding for Elizabeth Le
Amount £30,000 (GBP)
Organisation Medical Research Council (MRC) 
Sector Academic/University
Country United Kingdom
Start 10/2017 
End 10/2020
 
Description MSCA-RISE-2015 - Marie Sklodowska-Curie Research and Innovation Staff Exchange (RISE): CHiPS CHallenges in Preservation of Structure
Amount € 387,000 (EUR)
Funding ID 691070 
Organisation European Commission H2020 
Sector Public
Country Belgium
Start 01/2016 
End 12/2019
 
Description MSCA-RISE-2017 - Research and Innovation Staff Exchange: NoMADS: Nonlocal Methods for Arbitrary Data Sources
Amount € 1,111,500 (EUR)
Funding ID 777826 
Organisation European Commission H2020 
Sector Public
Country Belgium
Start 03/2018 
End 02/2022
 
Description NPL postdoctoral fellowship grant for The mathematics of measurement
Amount £250,000 (GBP)
Organisation National Physical Laboratory 
Sector Academic/University
Country United Kingdom
Start 04/2018 
End 03/2021
 
Description Parke Davis Travelling Fellowship
Amount £30,000 (GBP)
Organisation Pfizer Ltd 
Department Parke Davis
Sector Private
Country Global
Start 11/2017 
End 11/2020
 
Description Programme Grant
Amount £2,750,890 (GBP)
Funding ID EP/N031938/1 
Organisation Engineering and Physical Sciences Research Council (EPSRC) 
Sector Academic/University
Country United Kingdom
Start 06/2016 
End 05/2022
 
Description The PILL Study - PET/MR vascular intervention
Amount £44,920 (GEL)
Organisation General Electric 
Sector Private
Country United States
Start 03/2019 
End 03/2020
 
Description Turing seed funding for Personalized Breast Cancer Screening
Amount £24,000 (CLF)
Organisation Alan Turing Institute 
Sector Academic/University
Country Unknown
Start 10/2017 
End 04/2018
 
Description Wellcome Trust Clinical Research Career Development Fellowship for Jason Tarkin
Amount £672,549 (GBP)
Funding ID 211100/Z/18/Z 
Organisation Wellcome Trust 
Sector Charity/Non Profit
Country United Kingdom
Start 04/2019 
End 04/2024
 
Title Research data supporting 'An Anisotropic Interaction Model for Simulating Fingerprints' 
Description This data contains the code and data necessary to reproduce the computational results published in 'An Anisotropic Interaction Model for Simulating Fingerprints'. 
Type Of Material Database/Collection of data 
Year Produced 2017 
Provided To Others? Yes  
 
Title Research data supporting 'Pattern formation of a nonlocal, anisotropic interaction model' 
Description This data contains the code and data necessary to reproduce the computational results published in 'Pattern formation of a nonlocal, anisotropic interaction model'. 
Type Of Material Database/Collection of data 
Year Produced 2017 
Provided To Others? Yes  
 
Title Research data supporting the publication "Inverse Scale Space Decomposition". 
Description This dataset contains MATLAB© code for the numerical computation of the numerical examples described in Section 5.1 and Section 5.2 of the publication "Inverse Scale Space Decomposition". 
Type Of Material Database/Collection of data 
Year Produced 2017 
Provided To Others? Yes  
 
Title Research data supporting the publication 'Nonlinear Spectral Image Fusion' 
Description This is Matlab code for the creation of image fusions based on the nonlinear spectral TV transform. The method of spectral image fusion is explained in the corresponding SSVM publication 'Nonlinear Spectral Image Fusion'. In order to run the automatic image fusion pipeline with the Obama/Reagan example as visualised in the paper, please follow the instructions in the readme.txt file in the folder 'spectralImageFusionOfFaces'. If you want to compute the spectral image fusions of Gauß and Newton in the supplementary files, please follow the instructions in the Matlab live scripts 'gaussnewton.mlx' or 'newtongauss.mlx' in the folder 'Banknote examples'. 
Type Of Material Database/Collection of data 
Year Produced 2017 
Provided To Others? Yes  
 
Description Anisotropic variational models and PDEs for inverse imaging problems 
Organisation Luebeck University of Applied Sciences
PI Contribution In this project, we introduce a new higher-order total directional variation (TDV) regulariser for inverse imaging problems by taking into account the image gradient weighted by the structural content. Theoretical and numerical details are provided for different applications: the reconstruction of noisy images and videos, the image zooming and the interpolation of scattered surface data. The idea of using directional gradients for imaging applications is also used for the generalisation of the osmosis equation, introduced by Weickert and collaborators in 2013, to its anisotropic counter-part. Anisotropic osmosis is applied to the shadow removal problem thus improving upon the isotropic approach by avoiding the blurring artefact due to the isotropic diffusion. The main idea came from CB Schönlieb in Cambridge, and was part of Dr Simone Parisotto's PhD thesis (PhD student in Schönlieb's group). All the research work was done in collaboration with everyone involved.
Collaborator Contribution Equal contribution between: Simon Masnou (Lyon), Jean-Michel Morel (Cachan), Jan Lellmann (Luebeck), Luca Calatroni (Ecole Polytechnique, Paris), and Joachim Weickert (Saarland).
Impact [1] Parisotto S, Lellmann J, Masnou S, Schönlieb C-B. Higher-Order Total Directional Variation. Part I: Imaging Applications. ArXiv e-print (2018) https://arxiv.org/abs/1812.05023 [2] Parisotto S, Masnou S, Schönlieb C-B. Higher-Order Total Directional Variation. Part II: Analysis. ArXiv e-print (2018) https://arxiv.org/abs/1812.05061 [3] Parisotto S, Schönlieb C-B. Total Directional Variation for Video Denoising. ArXiv e-print (2018) https://arxiv.org/abs/1812.05063 [4] Parisotto S, Calatroni L, Caliari M, Schönlieb C-B, Weickert J. Anisotropic osmosis filtering for shadow removal in images. Inverse Problems (2019) https://doi.org/10.1088/1361-6420/ab08d2
Start Year 2014
 
Description Anisotropic variational models and PDEs for inverse imaging problems 
Organisation Saarland University
Country Germany 
Sector Academic/University 
PI Contribution In this project, we introduce a new higher-order total directional variation (TDV) regulariser for inverse imaging problems by taking into account the image gradient weighted by the structural content. Theoretical and numerical details are provided for different applications: the reconstruction of noisy images and videos, the image zooming and the interpolation of scattered surface data. The idea of using directional gradients for imaging applications is also used for the generalisation of the osmosis equation, introduced by Weickert and collaborators in 2013, to its anisotropic counter-part. Anisotropic osmosis is applied to the shadow removal problem thus improving upon the isotropic approach by avoiding the blurring artefact due to the isotropic diffusion. The main idea came from CB Schönlieb in Cambridge, and was part of Dr Simone Parisotto's PhD thesis (PhD student in Schönlieb's group). All the research work was done in collaboration with everyone involved.
Collaborator Contribution Equal contribution between: Simon Masnou (Lyon), Jean-Michel Morel (Cachan), Jan Lellmann (Luebeck), Luca Calatroni (Ecole Polytechnique, Paris), and Joachim Weickert (Saarland).
Impact [1] Parisotto S, Lellmann J, Masnou S, Schönlieb C-B. Higher-Order Total Directional Variation. Part I: Imaging Applications. ArXiv e-print (2018) https://arxiv.org/abs/1812.05023 [2] Parisotto S, Masnou S, Schönlieb C-B. Higher-Order Total Directional Variation. Part II: Analysis. ArXiv e-print (2018) https://arxiv.org/abs/1812.05061 [3] Parisotto S, Schönlieb C-B. Total Directional Variation for Video Denoising. ArXiv e-print (2018) https://arxiv.org/abs/1812.05063 [4] Parisotto S, Calatroni L, Caliari M, Schönlieb C-B, Weickert J. Anisotropic osmosis filtering for shadow removal in images. Inverse Problems (2019) https://doi.org/10.1088/1361-6420/ab08d2
Start Year 2014
 
Description Anisotropic variational models and PDEs for inverse imaging problems 
Organisation University of Lyon
Country France 
Sector Academic/University 
PI Contribution In this project, we introduce a new higher-order total directional variation (TDV) regulariser for inverse imaging problems by taking into account the image gradient weighted by the structural content. Theoretical and numerical details are provided for different applications: the reconstruction of noisy images and videos, the image zooming and the interpolation of scattered surface data. The idea of using directional gradients for imaging applications is also used for the generalisation of the osmosis equation, introduced by Weickert and collaborators in 2013, to its anisotropic counter-part. Anisotropic osmosis is applied to the shadow removal problem thus improving upon the isotropic approach by avoiding the blurring artefact due to the isotropic diffusion. The main idea came from CB Schönlieb in Cambridge, and was part of Dr Simone Parisotto's PhD thesis (PhD student in Schönlieb's group). All the research work was done in collaboration with everyone involved.
Collaborator Contribution Equal contribution between: Simon Masnou (Lyon), Jean-Michel Morel (Cachan), Jan Lellmann (Luebeck), Luca Calatroni (Ecole Polytechnique, Paris), and Joachim Weickert (Saarland).
Impact [1] Parisotto S, Lellmann J, Masnou S, Schönlieb C-B. Higher-Order Total Directional Variation. Part I: Imaging Applications. ArXiv e-print (2018) https://arxiv.org/abs/1812.05023 [2] Parisotto S, Masnou S, Schönlieb C-B. Higher-Order Total Directional Variation. Part II: Analysis. ArXiv e-print (2018) https://arxiv.org/abs/1812.05061 [3] Parisotto S, Schönlieb C-B. Total Directional Variation for Video Denoising. ArXiv e-print (2018) https://arxiv.org/abs/1812.05063 [4] Parisotto S, Calatroni L, Caliari M, Schönlieb C-B, Weickert J. Anisotropic osmosis filtering for shadow removal in images. Inverse Problems (2019) https://doi.org/10.1088/1361-6420/ab08d2
Start Year 2014
 
Description Artificial intelligence tools for screening mammography 
Organisation DeepMind Health
Country United Kingdom 
Sector Private 
PI Contribution Using my expertise as a breast screening radiologist to advise on development of machine reading tools for mammography.
Collaborator Contribution DeepMInd developing machine reading tools for use in breast screening. These can then be tested by the partners in the NHS breast screening programme.
Impact no outputs as yet... abstracts and publications expected.
Start Year 2017
 
Description Cambridge big data 
Organisation Cambridge Carbon Capture Ltd
Country United Kingdom 
Sector Private 
PI Contribution We have assisted in putting together research bids and a conference, as well as networking with others, and creating new collaborative projects
Collaborator Contribution Assisted in putting together research bids and a conference, as well as networking with others, and creating new collaborative projects. The Big data consortium has provided huge expertise and knowledge for us.
Impact A grant application, to enable better access and analysis of healthcare data has been submitted. A conference on big data in medicine is being planned for July 4th 2017. Academics have been put in contact with each other, enabling transfer of knowledge.
Start Year 2016
 
Description Collaboration with Imperial College EPSRC maths centre 
Organisation Imperial College London
Country United Kingdom 
Sector Academic/University 
PI Contribution New collaboration to work on machine learning in medical images. Domain knowledge, imaging data,
Collaborator Contribution New collaboration to work on machine learning in medical images. ML expertise.
Impact None yet.
Start Year 2018
 
Description Developing a Predictive Model Integrating Machine Learning and Image Inpainting to Delineate Tumour Invasion in Glioblastoma 
Organisation Fudan University
Department Huashan Hospital
Country China 
Sector Hospitals 
PI Contribution image processing of dataset, mathematical analysis of MRI, development of mathematical model
Collaborator Contribution Fudan University: provision of expertise in neuroradiology Neurosciences (Cambridge): provision of data-set and annotations
Impact Too early in the project for outcomes to be delivered
Start Year 2018
 
Description Developing a Predictive Model Integrating Machine Learning and Image Inpainting to Delineate Tumour Invasion in Glioblastoma 
Organisation University of Cambridge
Country United Kingdom 
Sector Academic/University 
PI Contribution image processing of dataset, mathematical analysis of MRI, development of mathematical model
Collaborator Contribution Fudan University: provision of expertise in neuroradiology Neurosciences (Cambridge): provision of data-set and annotations
Impact Too early in the project for outcomes to be delivered
Start Year 2018
 
Description Duke - MRI microscopy 
Organisation Duke University
Country United States 
Sector Academic/University 
PI Contribution Provide expertise on image reconstruction from undersampled data.
Collaborator Contribution Partern is applied their expertise in high resolution MRI and microscopic scale to improve reconstruction quality.
Impact Journal submission is underway.
Start Year 2016
 
Description Faster PET Reconstruction by Stochastic Optimisation 
Organisation Ecole Polytechnique
Country France 
Sector Academic/University 
PI Contribution This project is concerned with the efficient reconstruction of positron emission tomography by means of stochastic optimisation. In the last decade, many mathematical tools have been developed that have the ability to enhance clinical imaging in various ways. On the forefront of this wave are non-smooth priors that allow the reconstruction of a smooth image but do not prohibit jumps across meaningful areas like organs in medical imaging. Beside this these new tools also allow the incorporation of a-prior structual knowledge about the solution at hand. However, most of this progress has not been translated into clinical practice as most modern algorithms are too demanding for the huge data sizes encountered. In the past, algorithms have been made "applicable" to clinical practices by only considering a subset if the data at a time. While for some models this leads to satisfactory results, in general this ad-hoc strategy may yield to spurious artefacts. Motivated by the success of similar techniques in machine learning, in this project we extend modern algorithms for imaging that can handle non-smooth priors in a rigorous way to the subset setting by means of "randomisation". While the algorithm and thus its iterates are random, the variances of these are low and converge quickly to the desired deterministic solution. The Cambridge group has contributed to the algorithm development, and the design of the PET++ project (see outputs below).
Collaborator Contribution Matthias Ehrhardt at the University of Bath has lead the algorithm development for stochastic optimisation for PET, and is a co-Lead on the PET++ project. Antonin Chambolle (Ecole Polytechnique) and Peter Richtárik (KAUST) have contributed with their expertise in convex optimisation and subspace decomposition approaches. Pawel Markiewicz (UCL) has contributed with his expertise on PET and associated PET data and reconstruction codes.
Impact [1] Ehrhardt, M. J., Markiewicz, P. J., Schönlieb, C.-B. (2018). Faster PET Reconstruction with Non-Smooth Priors by Randomization and Preconditioning, https://arxiv.org/abs/1808.07150 [2] Ehrhardt, M. J., Markiewicz, P. J., Richtárik, P., Schott, J., Chambolle, A. & Schönlieb, C.-B. (2017). Faster PET Reconstruction with a Stochastic Primal-Dual Hybrid Gradient Method. In Proceedings of SPIE (Vol. 10394, pp. 1-12). San Diego. http://doi.org/10.1117/12.2272946. [3] Chambolle, A., Ehrhardt, M. J., Richtárik, P., & Schönlieb, C.-B. (2017). Stochastic Primal-Dual Hybrid Gradient Algorithm with Arbitrary Sampling and Imaging Applications. to appear in SIAM Journal on Optimization. http://arxiv.org/abs/1706.04957.
Start Year 2016
 
Description Faster PET Reconstruction by Stochastic Optimisation 
Organisation GE Healthcare Limited
Country United Kingdom 
Sector Private 
PI Contribution This project is concerned with the efficient reconstruction of positron emission tomography by means of stochastic optimisation. In the last decade, many mathematical tools have been developed that have the ability to enhance clinical imaging in various ways. On the forefront of this wave are non-smooth priors that allow the reconstruction of a smooth image but do not prohibit jumps across meaningful areas like organs in medical imaging. Beside this these new tools also allow the incorporation of a-prior structual knowledge about the solution at hand. However, most of this progress has not been translated into clinical practice as most modern algorithms are too demanding for the huge data sizes encountered. In the past, algorithms have been made "applicable" to clinical practices by only considering a subset if the data at a time. While for some models this leads to satisfactory results, in general this ad-hoc strategy may yield to spurious artefacts. Motivated by the success of similar techniques in machine learning, in this project we extend modern algorithms for imaging that can handle non-smooth priors in a rigorous way to the subset setting by means of "randomisation". While the algorithm and thus its iterates are random, the variances of these are low and converge quickly to the desired deterministic solution. The Cambridge group has contributed to the algorithm development, and the design of the PET++ project (see outputs below).
Collaborator Contribution Matthias Ehrhardt at the University of Bath has lead the algorithm development for stochastic optimisation for PET, and is a co-Lead on the PET++ project. Antonin Chambolle (Ecole Polytechnique) and Peter Richtárik (KAUST) have contributed with their expertise in convex optimisation and subspace decomposition approaches. Pawel Markiewicz (UCL) has contributed with his expertise on PET and associated PET data and reconstruction codes.
Impact [1] Ehrhardt, M. J., Markiewicz, P. J., Schönlieb, C.-B. (2018). Faster PET Reconstruction with Non-Smooth Priors by Randomization and Preconditioning, https://arxiv.org/abs/1808.07150 [2] Ehrhardt, M. J., Markiewicz, P. J., Richtárik, P., Schott, J., Chambolle, A. & Schönlieb, C.-B. (2017). Faster PET Reconstruction with a Stochastic Primal-Dual Hybrid Gradient Method. In Proceedings of SPIE (Vol. 10394, pp. 1-12). San Diego. http://doi.org/10.1117/12.2272946. [3] Chambolle, A., Ehrhardt, M. J., Richtárik, P., & Schönlieb, C.-B. (2017). Stochastic Primal-Dual Hybrid Gradient Algorithm with Arbitrary Sampling and Imaging Applications. to appear in SIAM Journal on Optimization. http://arxiv.org/abs/1706.04957.
Start Year 2016
 
Description Faster PET Reconstruction by Stochastic Optimisation 
Organisation King Abdullah University of Science and Technology (KAUST)
Country Saudi Arabia 
Sector Academic/University 
PI Contribution This project is concerned with the efficient reconstruction of positron emission tomography by means of stochastic optimisation. In the last decade, many mathematical tools have been developed that have the ability to enhance clinical imaging in various ways. On the forefront of this wave are non-smooth priors that allow the reconstruction of a smooth image but do not prohibit jumps across meaningful areas like organs in medical imaging. Beside this these new tools also allow the incorporation of a-prior structual knowledge about the solution at hand. However, most of this progress has not been translated into clinical practice as most modern algorithms are too demanding for the huge data sizes encountered. In the past, algorithms have been made "applicable" to clinical practices by only considering a subset if the data at a time. While for some models this leads to satisfactory results, in general this ad-hoc strategy may yield to spurious artefacts. Motivated by the success of similar techniques in machine learning, in this project we extend modern algorithms for imaging that can handle non-smooth priors in a rigorous way to the subset setting by means of "randomisation". While the algorithm and thus its iterates are random, the variances of these are low and converge quickly to the desired deterministic solution. The Cambridge group has contributed to the algorithm development, and the design of the PET++ project (see outputs below).
Collaborator Contribution Matthias Ehrhardt at the University of Bath has lead the algorithm development for stochastic optimisation for PET, and is a co-Lead on the PET++ project. Antonin Chambolle (Ecole Polytechnique) and Peter Richtárik (KAUST) have contributed with their expertise in convex optimisation and subspace decomposition approaches. Pawel Markiewicz (UCL) has contributed with his expertise on PET and associated PET data and reconstruction codes.
Impact [1] Ehrhardt, M. J., Markiewicz, P. J., Schönlieb, C.-B. (2018). Faster PET Reconstruction with Non-Smooth Priors by Randomization and Preconditioning, https://arxiv.org/abs/1808.07150 [2] Ehrhardt, M. J., Markiewicz, P. J., Richtárik, P., Schott, J., Chambolle, A. & Schönlieb, C.-B. (2017). Faster PET Reconstruction with a Stochastic Primal-Dual Hybrid Gradient Method. In Proceedings of SPIE (Vol. 10394, pp. 1-12). San Diego. http://doi.org/10.1117/12.2272946. [3] Chambolle, A., Ehrhardt, M. J., Richtárik, P., & Schönlieb, C.-B. (2017). Stochastic Primal-Dual Hybrid Gradient Algorithm with Arbitrary Sampling and Imaging Applications. to appear in SIAM Journal on Optimization. http://arxiv.org/abs/1706.04957.
Start Year 2016
 
Description Faster PET Reconstruction by Stochastic Optimisation 
Organisation University College London
Country United Kingdom 
Sector Academic/University 
PI Contribution This project is concerned with the efficient reconstruction of positron emission tomography by means of stochastic optimisation. In the last decade, many mathematical tools have been developed that have the ability to enhance clinical imaging in various ways. On the forefront of this wave are non-smooth priors that allow the reconstruction of a smooth image but do not prohibit jumps across meaningful areas like organs in medical imaging. Beside this these new tools also allow the incorporation of a-prior structual knowledge about the solution at hand. However, most of this progress has not been translated into clinical practice as most modern algorithms are too demanding for the huge data sizes encountered. In the past, algorithms have been made "applicable" to clinical practices by only considering a subset if the data at a time. While for some models this leads to satisfactory results, in general this ad-hoc strategy may yield to spurious artefacts. Motivated by the success of similar techniques in machine learning, in this project we extend modern algorithms for imaging that can handle non-smooth priors in a rigorous way to the subset setting by means of "randomisation". While the algorithm and thus its iterates are random, the variances of these are low and converge quickly to the desired deterministic solution. The Cambridge group has contributed to the algorithm development, and the design of the PET++ project (see outputs below).
Collaborator Contribution Matthias Ehrhardt at the University of Bath has lead the algorithm development for stochastic optimisation for PET, and is a co-Lead on the PET++ project. Antonin Chambolle (Ecole Polytechnique) and Peter Richtárik (KAUST) have contributed with their expertise in convex optimisation and subspace decomposition approaches. Pawel Markiewicz (UCL) has contributed with his expertise on PET and associated PET data and reconstruction codes.
Impact [1] Ehrhardt, M. J., Markiewicz, P. J., Schönlieb, C.-B. (2018). Faster PET Reconstruction with Non-Smooth Priors by Randomization and Preconditioning, https://arxiv.org/abs/1808.07150 [2] Ehrhardt, M. J., Markiewicz, P. J., Richtárik, P., Schott, J., Chambolle, A. & Schönlieb, C.-B. (2017). Faster PET Reconstruction with a Stochastic Primal-Dual Hybrid Gradient Method. In Proceedings of SPIE (Vol. 10394, pp. 1-12). San Diego. http://doi.org/10.1117/12.2272946. [3] Chambolle, A., Ehrhardt, M. J., Richtárik, P., & Schönlieb, C.-B. (2017). Stochastic Primal-Dual Hybrid Gradient Algorithm with Arbitrary Sampling and Imaging Applications. to appear in SIAM Journal on Optimization. http://arxiv.org/abs/1706.04957.
Start Year 2016
 
Description Faster PET Reconstruction by Stochastic Optimisation 
Organisation University of Bath
Country United Kingdom 
Sector Academic/University 
PI Contribution This project is concerned with the efficient reconstruction of positron emission tomography by means of stochastic optimisation. In the last decade, many mathematical tools have been developed that have the ability to enhance clinical imaging in various ways. On the forefront of this wave are non-smooth priors that allow the reconstruction of a smooth image but do not prohibit jumps across meaningful areas like organs in medical imaging. Beside this these new tools also allow the incorporation of a-prior structual knowledge about the solution at hand. However, most of this progress has not been translated into clinical practice as most modern algorithms are too demanding for the huge data sizes encountered. In the past, algorithms have been made "applicable" to clinical practices by only considering a subset if the data at a time. While for some models this leads to satisfactory results, in general this ad-hoc strategy may yield to spurious artefacts. Motivated by the success of similar techniques in machine learning, in this project we extend modern algorithms for imaging that can handle non-smooth priors in a rigorous way to the subset setting by means of "randomisation". While the algorithm and thus its iterates are random, the variances of these are low and converge quickly to the desired deterministic solution. The Cambridge group has contributed to the algorithm development, and the design of the PET++ project (see outputs below).
Collaborator Contribution Matthias Ehrhardt at the University of Bath has lead the algorithm development for stochastic optimisation for PET, and is a co-Lead on the PET++ project. Antonin Chambolle (Ecole Polytechnique) and Peter Richtárik (KAUST) have contributed with their expertise in convex optimisation and subspace decomposition approaches. Pawel Markiewicz (UCL) has contributed with his expertise on PET and associated PET data and reconstruction codes.
Impact [1] Ehrhardt, M. J., Markiewicz, P. J., Schönlieb, C.-B. (2018). Faster PET Reconstruction with Non-Smooth Priors by Randomization and Preconditioning, https://arxiv.org/abs/1808.07150 [2] Ehrhardt, M. J., Markiewicz, P. J., Richtárik, P., Schott, J., Chambolle, A. & Schönlieb, C.-B. (2017). Faster PET Reconstruction with a Stochastic Primal-Dual Hybrid Gradient Method. In Proceedings of SPIE (Vol. 10394, pp. 1-12). San Diego. http://doi.org/10.1117/12.2272946. [3] Chambolle, A., Ehrhardt, M. J., Richtárik, P., & Schönlieb, C.-B. (2017). Stochastic Primal-Dual Hybrid Gradient Algorithm with Arbitrary Sampling and Imaging Applications. to appear in SIAM Journal on Optimization. http://arxiv.org/abs/1706.04957.
Start Year 2016
 
Description Flow of Microtubules in the Drosophila Oocyte 
Organisation Friedrich-Alexander University Erlangen-Nuremberg
Country Germany 
Sector Academic/University 
PI Contribution The focus of this project is to characterise directionality of plus ends of microtubules in confocal microscopy images of Drosophilia embryos. This goal is particularly challenging due to the high noise level in such data, making it almost impossible to distinguish EB1 fluorescently labelled comets from randomly distributed noise. To overcome this problem, we employ recently developed methods for joint motion estimation and image reconstruction. As a result we are able to estimate motion in image sequences where other state of the art methods fail. My group in Cambridge (in particular my PostDoc Dr Lukas Lang) has contributed the algorithm development for the motion analysis (optical flow).
Collaborator Contribution Our collaborators Isabel Palacios (Queen Mary London) and Mail Drechsler (University of Osnabrück) have contributed with the biological question and the imaging data. Our collaborators Martin Burger (University of Erlangen) and Hendrik Dirks (CLK GmbH) have contributed to initial ideas for the motion estimation algorithm.
Impact M. Drechsler, L. F. Lang, H. Dirks, M. Burger, C.-B. Schönlieb, I. M. Palacios. Optical flow analysis reveals that Kinesin-mediated advection impacts on the orientation of microtubules, submitted, 2019. Preprint: https://www.biorxiv.org/content/10.1101/556043v2 Code: https://zenodo.org/record/2573254#.XIduWS2cZ0s
Start Year 2015
 
Description Flow of Microtubules in the Drosophila Oocyte 
Organisation Queen Mary University of London
Country United Kingdom 
Sector Academic/University 
PI Contribution The focus of this project is to characterise directionality of plus ends of microtubules in confocal microscopy images of Drosophilia embryos. This goal is particularly challenging due to the high noise level in such data, making it almost impossible to distinguish EB1 fluorescently labelled comets from randomly distributed noise. To overcome this problem, we employ recently developed methods for joint motion estimation and image reconstruction. As a result we are able to estimate motion in image sequences where other state of the art methods fail. My group in Cambridge (in particular my PostDoc Dr Lukas Lang) has contributed the algorithm development for the motion analysis (optical flow).
Collaborator Contribution Our collaborators Isabel Palacios (Queen Mary London) and Mail Drechsler (University of Osnabrück) have contributed with the biological question and the imaging data. Our collaborators Martin Burger (University of Erlangen) and Hendrik Dirks (CLK GmbH) have contributed to initial ideas for the motion estimation algorithm.
Impact M. Drechsler, L. F. Lang, H. Dirks, M. Burger, C.-B. Schönlieb, I. M. Palacios. Optical flow analysis reveals that Kinesin-mediated advection impacts on the orientation of microtubules, submitted, 2019. Preprint: https://www.biorxiv.org/content/10.1101/556043v2 Code: https://zenodo.org/record/2573254#.XIduWS2cZ0s
Start Year 2015
 
Description Flow of Microtubules in the Drosophila Oocyte 
Organisation University of Osnabrück
Country Germany 
Sector Academic/University 
PI Contribution The focus of this project is to characterise directionality of plus ends of microtubules in confocal microscopy images of Drosophilia embryos. This goal is particularly challenging due to the high noise level in such data, making it almost impossible to distinguish EB1 fluorescently labelled comets from randomly distributed noise. To overcome this problem, we employ recently developed methods for joint motion estimation and image reconstruction. As a result we are able to estimate motion in image sequences where other state of the art methods fail. My group in Cambridge (in particular my PostDoc Dr Lukas Lang) has contributed the algorithm development for the motion analysis (optical flow).
Collaborator Contribution Our collaborators Isabel Palacios (Queen Mary London) and Mail Drechsler (University of Osnabrück) have contributed with the biological question and the imaging data. Our collaborators Martin Burger (University of Erlangen) and Hendrik Dirks (CLK GmbH) have contributed to initial ideas for the motion estimation algorithm.
Impact M. Drechsler, L. F. Lang, H. Dirks, M. Burger, C.-B. Schönlieb, I. M. Palacios. Optical flow analysis reveals that Kinesin-mediated advection impacts on the orientation of microtubules, submitted, 2019. Preprint: https://www.biorxiv.org/content/10.1101/556043v2 Code: https://zenodo.org/record/2573254#.XIduWS2cZ0s
Start Year 2015
 
Description Geometric Integration Methods for Optimisation 
Organisation La Trobe University
Country Australia 
Sector Academic/University 
PI Contribution This project is concerned with the development and analysis of optimisation schemes based on geometric numerical integration methods. Discrete gradient methods are popular numerical schemes for solving systems of ODEs, and are known for preserving structures of the continuous system such as energy dissipation/conservation. Applying discrete gradients to dissipative ODEs/PDEs yields optimisation schemes that preserve the dissipative structure. For example, we consider a derivative-free discrete gradient method for optimising nonsmooth, nonconvex problems in a blackbox setting. This method has been shown to converge to optimal points of the objective function in a general, nonsmooth setting, while retaining favourable properties of gradient flow. This blackbox optimisation framework is useful, for instance, for bilevel optimisation of regularisation parameters in image processing. We (my PostDoc Matthias Ehrhardt and myself) have developed the idea and this topic in Cambridge, and my PhD student Erlend Riis has done all the analysis and numerical tests that are contained in thee papers (see below).
Collaborator Contribution Our collaborator Reinout Quispel (La Trobe, Melbourne, Australia) has contributed expertise in geometric integration. Our collaborator Torbjørn Ringholm (NTNU, Trondheim, Norway) has contributed to the convergence analysis in the smooth case, and was the lead author on the Euler elastica paper. Our collaborator Jasmina Lazic (former MathWorks Cambridge) has contributed to the parallelisation of the discrete gradient method.
Impact E. S. Riis, M. J. Ehrhardt, G. R. W. Quispel, and C.-B. Schönlieb, A geometric integration approach to nonsmooth, nonconvex optimisation, arXiv:1807.07554, 2018. M. Ehrhardt, E. Riis, T. Ringholm, and C.-B. Schönlieb, A geometric integration approach to smooth optimisation: Foundations of the discrete gradient method, arXiv:1805.06444, 2018. T. Ringholm, J. Lazic, C.-B. Schönlieb, Variational image regularization with Euler's elastica using a discrete gradient scheme, SIAM J. Imaging Sci., 11(4), 2665-2691, 2018. Exhibition at MATLAB Expo in Silverstone in 2017.
Start Year 2017
 
Description Geometric Integration Methods for Optimisation 
Organisation Norwegian University of Science and Technology (NTNU)
Country Norway 
Sector Academic/University 
PI Contribution This project is concerned with the development and analysis of optimisation schemes based on geometric numerical integration methods. Discrete gradient methods are popular numerical schemes for solving systems of ODEs, and are known for preserving structures of the continuous system such as energy dissipation/conservation. Applying discrete gradients to dissipative ODEs/PDEs yields optimisation schemes that preserve the dissipative structure. For example, we consider a derivative-free discrete gradient method for optimising nonsmooth, nonconvex problems in a blackbox setting. This method has been shown to converge to optimal points of the objective function in a general, nonsmooth setting, while retaining favourable properties of gradient flow. This blackbox optimisation framework is useful, for instance, for bilevel optimisation of regularisation parameters in image processing. We (my PostDoc Matthias Ehrhardt and myself) have developed the idea and this topic in Cambridge, and my PhD student Erlend Riis has done all the analysis and numerical tests that are contained in thee papers (see below).
Collaborator Contribution Our collaborator Reinout Quispel (La Trobe, Melbourne, Australia) has contributed expertise in geometric integration. Our collaborator Torbjørn Ringholm (NTNU, Trondheim, Norway) has contributed to the convergence analysis in the smooth case, and was the lead author on the Euler elastica paper. Our collaborator Jasmina Lazic (former MathWorks Cambridge) has contributed to the parallelisation of the discrete gradient method.
Impact E. S. Riis, M. J. Ehrhardt, G. R. W. Quispel, and C.-B. Schönlieb, A geometric integration approach to nonsmooth, nonconvex optimisation, arXiv:1807.07554, 2018. M. Ehrhardt, E. Riis, T. Ringholm, and C.-B. Schönlieb, A geometric integration approach to smooth optimisation: Foundations of the discrete gradient method, arXiv:1805.06444, 2018. T. Ringholm, J. Lazic, C.-B. Schönlieb, Variational image regularization with Euler's elastica using a discrete gradient scheme, SIAM J. Imaging Sci., 11(4), 2665-2691, 2018. Exhibition at MATLAB Expo in Silverstone in 2017.
Start Year 2017
 
Description Integrative Cancer Medicine Collaboration 
Organisation Cancer Research UK Cambridge Institute
Country United Kingdom 
Sector Academic/University 
PI Contribution Development of all-in-one cancer imaging pipeline: from raw tomographic measurements to personalised cancer diagnosis and treatment prediction.
Collaborator Contribution Integrative cancer medicine idea; provision of problem and objective; provision of clinical data and expertise. Collaborators: Prof. Evis Sala (Radiology, Cambridge), Prof. Ozan Öktem (KTH, Stockholm), Dr Mireia Crispin-Ortuzar (CRUK CI), Dr Ramona Woitek (Radiology, Cambridge)
Impact Wellcome Trust Application for All-in-one cancer imaging project under the Digital Innovator Award call. Outcome to be known in April 2019.
Start Year 2018
 
Description Integrative Cancer Medicine Collaboration 
Organisation Royal Institute of Technology
Country Sweden 
Sector Academic/University 
PI Contribution Development of all-in-one cancer imaging pipeline: from raw tomographic measurements to personalised cancer diagnosis and treatment prediction.
Collaborator Contribution Integrative cancer medicine idea; provision of problem and objective; provision of clinical data and expertise. Collaborators: Prof. Evis Sala (Radiology, Cambridge), Prof. Ozan Öktem (KTH, Stockholm), Dr Mireia Crispin-Ortuzar (CRUK CI), Dr Ramona Woitek (Radiology, Cambridge)
Impact Wellcome Trust Application for All-in-one cancer imaging project under the Digital Innovator Award call. Outcome to be known in April 2019.
Start Year 2018
 
Description Longitudinal assessment of the progression or stability of disease in patients with Idiopathic Pulmonary Fibrosis - Machine vs Human 
Organisation Papworth Hospital
PI Contribution Collaboration in the project outcomes Jointly funding the research project Provision of high performance computing facilities
Collaborator Contribution Papworth: provision of anonymised data set Qureight: provision of annotated scans and clinical data Liverpool: jointly funded the research project
Impact Outcomes awaited
Start Year 2019
 
Description Longitudinal assessment of the progression or stability of disease in patients with Idiopathic Pulmonary Fibrosis - Machine vs Human 
Organisation University of Cambridge
Country United Kingdom 
Sector Academic/University 
PI Contribution Collaboration in the project outcomes Jointly funding the research project Provision of high performance computing facilities
Collaborator Contribution Papworth: provision of anonymised data set Qureight: provision of annotated scans and clinical data Liverpool: jointly funded the research project
Impact Outcomes awaited
Start Year 2019
 
Description Longitudinal assessment of the progression or stability of disease in patients with Idiopathic Pulmonary Fibrosis - Machine vs Human 
Organisation University of Liverpool
Country United Kingdom 
Sector Academic/University 
PI Contribution Collaboration in the project outcomes Jointly funding the research project Provision of high performance computing facilities
Collaborator Contribution Papworth: provision of anonymised data set Qureight: provision of annotated scans and clinical data Liverpool: jointly funded the research project
Impact Outcomes awaited
Start Year 2019
 
Description MSSM 
Organisation Icahn School of Medicine at Mount Sinai
Country United States 
Sector Academic/University 
PI Contribution Long collaboration with Prof Zahi Fayad.
Collaborator Contribution Exchange of students between Cambridge, Edinburgh and Mount Sinai.
Impact Many papers and joint grant applications.
Start Year 2008
 
Description Mathematical Challenges in Electron Tomography 
Organisation University of Manchester
Country United Kingdom 
Sector Academic/University 
PI Contribution Electron microscopy is a powerful tool in the physical, biological, and industrial sciences advancing areas from nanotechnology to drug discovery. Tomography is a mathematical technique used to recover full 3D information from a sequence of 2D images. One of the classical challenges here is to get the best quality reconstruction from the smallest amount of data. Some of our work has been to introduce novel and customised regularisation techniques to address such problems. Recent hardware advances have also extended this to spectral images, where pixel-wise values of the 2D images are vectors rather than greyscale. This data is also very slow to acquire so we need new methods to reconstruct from little and very noisy data. I have a joint PhD student, Mr Robert Tovey, with Paul Midgley in Material Sciences in Cambridge. Together with Rob my group has contributed the development of novel mathematical reconstruction algorithms for electron tomography reconstruction. Our latest project is the derivation of a sound mathematical model and associated numerical algorithm for strain tensor tomography (in collaboration with Bill Lionheart from Manchester).
Collaborator Contribution Our collaborators in the group of Paul Midgley in Cambridge are the problem owners and have contributed with their expert knowledge on electron tomography and associated tomographic imaging data. Our collaborator Bill Lionheart from Manchester has contributed with his expertise on tensor tomography.
Impact Liquid phase blending of metal-organic frameworks L Longley, SM Collins, C Zhou, GJ Smales, SE Norman, NJ Brownbill, ... Nature communications 9 (1), 2135 Entropic comparison of atomic-resolution electron tomography of crystals and amorphous materials SM Collins, RK Leary, PA Midgley, R Tovey, M Benning, CB Schönlieb, ... Physical review letters 119 (16), 166101 Directional sinogram inpainting for limited angle tomography R Tovey, M Benning, C Brune, MJ Lagerwerf, SM Collins, R Leary, ... Inverse Problems Automated Textural Classification of Osteoarthritis Magnetic Resonance Images JD Kaggie, R Tovey, JW MacKay, F Gilbert, FA Gallagher, A McCaskie, ... International Society for Magnetic Resonance in Medicine
Start Year 2017
 
Description Mechanical modelling of coronary atherosclerosis 
Organisation Monash University
Country Australia 
Sector Academic/University 
PI Contribution Image processing, numerical modelling, statistical analysis and results interpretation.
Collaborator Contribution Study design, data collection and results interpretation.
Impact Not yet.
Start Year 2017
 
Description Multi-Task Transfer Learning For Deep Convolutional Neural Network In Automatic Classification Of Mammography 
Organisation University of Cambridge
Country United Kingdom 
Sector Academic/University 
PI Contribution Image processing, machine learning expertise,
Collaborator Contribution Provision of mammography dataset and domain-expertise for cancer grading
Impact Outcomes are still awaited.
Start Year 2018
 
Description PreXion - LED photoacoustics 
Organisation PreXion Corporation
Country Japan 
Sector Private 
PI Contribution We will apply our technical instrument validation methods to the Prexion imaging system
Collaborator Contribution They have provided funding for a postdoc for 6 months to carry out the research and also for the installation of an imaging system to test.
Impact Not yet.
Start Year 2016
 
Description Research collaboration with University of Edinburgh 
Organisation University of Edinburgh
Department Clinical Research Imaging Centre
Country United Kingdom 
Sector Academic/University 
PI Contribution I have provided input into setting up and maintaining the FDG and NaF PET program for imaging atherosclerosis and aortic valve disease. I have co-supervised a PhD student with Professor Newby
Collaborator Contribution Edinburgh has recruited patients for the aortic aneurysm imaging study. The have also shared imaging protocols with us in Cambridge, and we have had exchange sessions at both locations to share and develop ideas for future work and grant applications.
Impact Several grants - see grants section. Several high impact papers Successful co-supervision of PhD student
Start Year 2008
 
Description Schoenlieb - Quantitative photoacoustics 
Organisation University of Cambridge
Country United Kingdom 
Sector Academic/University 
PI Contribution We have provided expertise in photoacoustic imaging and common reconstruction challenges / artifacts.
Collaborator Contribution Our partner is applying their expertise in image reconstruction to try and overcome common reconstruction challenges / artifacts.
Impact Conference submission has been made based on initial results
Start Year 2016
 
Description Sparse regularisation for seismic imaging 
Organisation Schlumberger Limited
Department Schlumberger Cambridge Research
Country United Kingdom 
Sector Academic/University 
PI Contribution Ongoing collaboration with Dr Evren Yarman on sparse regularisation and optimisation with applications in seismic imaging. We have co-supervised two summer intern students together in the past and will be collaborating with a visiting student from Ecole Polytechnique in 2017.
Collaborator Contribution Know how and funding.
Impact Joint publication in preparation; joint supervision of mathematics students; industrial talk by Evren Yarman to graduate students in mathematics in Cambridge in February 2017.
Start Year 2015
 
Description Unveiling the invisible: mathematical methods for cultural heritage 
Organisation University of Cambridge
Department The Fitzwilliam Museum
Country United Kingdom 
Sector Academic/University 
PI Contribution Hypotheses to be tested: In-depth mathematical analysis of imaging data, developed through our collaboration, could transform the ways in which hypotheses in the study of material culture are tested by searching through algorithmically examined data collected by researchers, revealing hidden patterns in paintings, manuscripts and archaeological objects. Project objectives: We will bring cutting-edge mathematical research to the arts and humanities by focusing on three challenging problems: Textural analysis of cross-sections of paint; Virtual restoration of illuminated manuscripts; Classification of Roman pottery. We will also develop an intuitive software package that will make our methodology accessible to a wide range of arts and humanities scholars. My Cambridge group - in particular my PostDocs Dr Kasia Torgonska and Dr Simone Parisotto are developing the mathematical algorithms for all three cultural heritage applications. They will also be the main developers of the modular toolkit.
Collaborator Contribution Dr Launaro (Faculty of Classics, Cambridge) collaborates on the pottery classification part of the project. With his extensive experience in Roman pottery, their excavation, their historical context, their classification and curation, he is key in defining the shape characteristics in pottery (crucial for their automated classification), in formulating the relevant questions in the different stages of the work, and in critically evaluating the results. He is also responsible for steering the shape analysis and classification capabilities of the modular toolkit and making it user-friendly for a wider use by pottery specialists. Dr Spike Bucklow (Hamilton Kerr Institute (HKI), Cambridge) collaborates on the paint cross section classification, the manuscript restoration and provides input to the modular toolkit, helping define user requirements. He provides the conservation and restoration expertise, in particular on the significance of historic pigments, paints and artists' methods etc. for the manuscript restoration and the art historic significance of technical imagery for the paint cross sections. He has curated the existing database of paint cross-section images at HKI, oversees its development in support the automated cross section classification approaches and supervises its use as a test-bed for the modular toolkit. He is driving all the research questions asked of automated analysis in the manuscript restoration, identifying key visual features of interest in digital images of cross-sections and assessing the relevance and efficiency of software-based recognition of those visual features. As specific discriminatory criteria evolve, he advises on their employment, prioritizing sequences of queries and assessing navigation of the data set. He also advises on how the developed clustering techniques for the paint cross section should be integrated in the modular toolkit. Dr Panayotova (Fitzwilliam Museum) provides the expertise on the illuminated manuscripts through her research on over 4000 illuminated manuscripts at the Fitzwilliam Museum and the Cambridge Colleges. At the start of the project, she identified damaged images and prioritised the most important examples in dated and localised manuscripts for mathematical reconstructions. She selects and provides access to c. 200 from the over 35,000 digital images available from manuscripts in the Fitzwilliam Museum and the Colleges, and c. 100 images acquired with multispectral imaging techniques by the Museum's Research Scientist (at no cost to this project). In the course of the project, she also collaborates on the cross section classification, advising on the historical and artistic background of the selected manuscripts, and providing information on the circumstances of the images' original production and subsequent damage. This represents the human-expert knowledge that will be integrated in the automated restoration. She is giving feedback on the art restoration results, advising on the optimal extent of restoration required to maximise the research potential of the original images. Moreover, Dr Panayotova is co-developing the modular-toolkit, in particular influencing its functionality and design.
Impact Calatroni, Luca; Marie d'Autume and, Rob Hocking ; Panayotova, Stella; Parisotto, Simone; Ricciardi, Paola; Schönlieb, Carola-Bibiane Unveiling the invisible: mathematical methods for restoring and interpreting illuminated manuscripts Journal Article In: Heritage Science, 6 (1), pp. 56, 2018. Parisotto, Simone; Calatroni, Luca; Daffara, Claudia Digital Cultural Heritage imaging via osmosis filtering Inproceedings In: Mansouri A. El Moataz A., Nouboud Mammass F D (Ed.): ICISP 2018: Image and Signal Processing, pp. Springer, 2018. Daffara, Claudia; Parisotto, Simone; Ambrosini, Dario A multipurpose, dual-mode imaging in the MWIR range for artwork diagnostic: a systematic approach Journal Article In: Optics and Lasers in Engineering, 2017. Daffara, Claudia; Parisotto, Simone; Mariotti, Paola Ilaria Mid-infrared thermal imaging for an effective mapping of surface materials and sub-surface detachments in mural paintings: integration of thermography and thermal quasi-reflectography Inproceedings In: Optics for Arts, Architecture, and Archaeology, International Society for Optics and Photonics 2015. Leverhulme Trust project on Unveiling the Invisible - 3years from January 2019; GBP 250K.
Start Year 2013
 
Description member of HDAN 
Organisation University of Manchester
Department UK Health Data Analytics Network (UK-HDAN)
Country United Kingdom 
Sector Academic/University 
PI Contribution By belonging to this community we are better able to increase awareness of our centre, and benefit from finding out more about ongoing work within the community. Our presence within this enables others to be aware of our work, events etc and possibilities for collaboration.
Collaborator Contribution Thus far we have only just joined, our contribution so far is awareness for others of the work being done within the centre.
Impact Awareness of other work within this field.
Start Year 2017
 
Title An Object-Oriented Matlab-Framework for Inverse Problems (OOMFIP) - Version 0.5 
Description Citation Benning, M. An Object-Oriented Matlab-Framework for Inverse Problems (OOMFIP) - Version 0.5 [dataset]. https://doi.org/10.17863/CAM.281 Description This is an early version of a Matlab toolbox for the easier realisation of first-order splitting methods for the solution of non-smooth variational regularisation methods. Software Matlab (R2014a) Subjects Matlab, inverse problems, first-order methods, regularisation 
Type Of Technology Software 
Year Produced 2016 
Open Source License? Yes  
Impact General framework for optimisation in inverse problems. 
URL https://www.repository.cam.ac.uk/handle/1810/256338
 
Title An R package called SPCAvRP 
Description An R package to implement a method for Sparse Principal Component Analysis proposed in Gataric, Wang and Samworth (2017) 
Type Of Technology Software 
Year Produced 2017 
Impact Too early to say. 
URL https://cran.r-project.org/web/packages/SPCAvRP/index.html
 
Title Anisotropic osmosis filtering for shadow removal in images 
Description We present an anisotropic extension of the isotropic osmosis model that has been introduced by Weickert et al. for visual computing applications, and we adapt it specifically to shadow removal applications. We show that in the integrable setting, linear anisotropic osmosis minimises an energy that involves a suitable quadratic form which models local directional structures. In our shadow removal applications we estimate the local structure via a modified tensor voting approach and use this information within an anisotropic diffusion inpainting that resembles edge-enhancing anisotropic diffusion inpainting. Our numerical scheme combines the nonnegativity preserving stencil of Fehrenbach and Mirebeau with an exact time stepping based on highly accurate polynomial approximations of the matrix exponential. The resulting anisotropic model is tested on several synthetic and natural images corrupted by constant shadows. We show that it outperforms isotropic osmosis, since it does not suffer from blurring artefacts at the shadow boundaries. 
Type Of Technology Software 
Year Produced 2018 
Open Source License? Yes  
Impact Significantly improved shadow removal in digital images; reduces artefacts. 
URL https://iopscience.iop.org/article/10.1088/1361-6420/ab08d2
 
Title Blind Image Fusion for Hyperspectral Imaging with the Directional Total Variation 
Description This code allows to reproduce the results of Blind Image Fusion for Hyperspectral Imaging with the Directional Total Variation 
Type Of Technology Software 
Year Produced 2017 
Open Source License? Yes  
Impact Superresolution of spectral imaging data by using structure from high-res aerial photograph; the same principle can be applied to super resolution in medical imaging (follow on projects related to this are underway). 
URL http://iopscience.iop.org/0266-5611/34/4/044003/
 
Title Gradient descent in a generalised Bregman distance framework 
Description Research data supporting the paper https://arxiv.org/abs/1612.02506 Martin Benning, Marta M. Betcke, Matthias J. Ehrhardt, Carola-Bibiane Schönlieb "Gradient descent in a generalised Bregman distance framework" [dataset]. https://doi.org/10.17863/CAM.6489 This data contains the corresponding MATLAB©-code for the numerical examples in the conference proceedings paper 'Gradient descent in a generalised Bregman distance framework'. Download the zip-file and extract it to a folder of your choice. Execute the 'setpath.m' file to add all relevant files to the MATLAB© path, and switch to the folder 'Examples'. This folder contains a script named 'phasereconstruction.m' that will compute the numerical examples as presented in the paper. A detailed explanation of the script can be found in terms of the HTML-file 'phasereconstruction.html' in the sub-folder 'Manual'. 
Type Of Technology Software 
Year Produced 2016 
Open Source License? Yes  
Impact Bregman iteration framework for non-convex and non-smooth optimisation. 
URL https://www.repository.cam.ac.uk/handle/1810/261315
 
Title Mathematical Imaging Methods for Mitosis Analysis in Live-Cell Phase Contrast Microscopy 
Description We propose a workflow to detect and track mitotic cells in time-lapse microscopy image sequences. In order to avoid the requirement for cell lines expressing fluorescent markers and the associated phototoxicity, phase contrast microscopy is often preferred over fluorescence microscopy in live-cell imaging. However, common specific image characteristics complicate image processing and impede use of standard methods. Nevertheless, automated analysis is desirable due to manual analysis being subjective, biased and extremely time-consuming for large data sets. Here, we present the following workflow based on mathematical imaging methods. In the first step, mitosis detection is performed by means of the circular Hough transform. The obtained circular contour subsequently serves as an initialisation for the tracking algorithm based on variational methods. It is sub-divided into two parts: in order to determine the beginning of the whole mitosis cycle, a backwards tracking procedure is performed. After that, the cell is tracked forwards in time until the end of mitosis. As a result, the average of mitosis duration and ratios of different cell fates (cell death, no division, division into two or more daughter cells) can be measured and statistics on cell morphologies can be obtained. All of the tools are featured in the user-friendly MATLAB® Graphical User Interface MitosisAnalyser. Reference: Joana Sarah Grah, Jennifer Alison Harrington, Siang Boon Koh, Jeremy Andrew Pike, Alexander Schreiner, Martin Burger, Carola-Bibiane Schönlieb, Stefanie Reichelt. "Mathematical imaging methods for mitosis analysis in live-cell phase contrast microscopy." Methods 115 (2017): 91-99. 
Type Of Technology Software 
Year Produced 2017 
Open Source License? Yes  
Impact MitosisAnalyser is a software tool with an easy to use graphical user interface for cancer researchers without prior knowledge on image analysis to use. 
URL https://github.com/JoanaGrah/MitosisAnalyser
 
Title Multi-Contrast MRI Reconstruction with Structure-Guided Total Variation 
Description Paper: [SIAM.org][arxiv] Authors: M. Betcke and M. Ehrhardt Abstract: Magnetic resonance imaging (MRI) is a versatile imaging technique that allows different contrasts depending on the acquisition parameters. Many clinical imaging studies acquire MRI data for more than one of these contrasts---such as for instance T1 and T2 weighted images---which makes the overall scanning procedure very time consuming. As all of these images show the same underlying anatomy one can try to omit unnecessary measurements by taking the similarity into account during reconstruction. We will discuss two modifications of total variation---based on i) location and ii) direction---that take structural a priori knowledge into account and reduce to total variation in the degenerate case when no structural knowledge is available. We solve the resulting convex minimization problem with the alternating direction method of multipliers that separates the forward operator from the prior. For both priors the corresponding proximal operator can be implemented as an extension of the fast gradient projection method on the dual problem for total variation. We tested the priors on six data sets that are based on phantoms and real MRI images. In all test cases exploiting the structural information from the other contrast yields better results than separate reconstruction with total variation in terms of standard metrics like peak signal-to-noise ratio and structural similarity index. Furthermore, we found that exploiting the two dimensional directional information results in images with well defined edges, superior to those reconstructed solely using a priori information about the edge location. 
Type Of Technology Software 
Year Produced 2016 
Open Source License? Yes  
Impact Software for joint-reconstruction for multi contrast MRI 
URL http://www.damtp.cam.ac.uk/research/cia/software/
 
Title Preconditioned ADMM with nonlinear operator constraint 
Description Abstract: We are presenting a modification of the well-known Alternating Direction Method of Multipliers (ADMM) algorithm with additional preconditioning that aims at solving convex optimisation problems with nonlinear operator constraints. Connections to the recently developed Nonlinear Primal-Dual Hybrid Gradient Method (NL-PDHGM) are presented, and the algorithm is demonstrated to handle the nonlinear inverse problem of parallel Magnetic Resonance Imaging (MRI). Reference: Martin Benning, Florian Knoll, Carola-Bibiana Schönlieb und Tuomo Valkonen, Preconditioned ADMM with nonlinear operator constraint, IFIP conference proceedings 2015. 
Type Of Technology Software 
Year Produced 2015 
Open Source License? Yes  
Impact Robust method for optimisation for nonlinear inverse problems. 
URL https://www.repository.cam.ac.uk/handle/1810/256221
 
Title Stochastic PDHG with Arbitrary Sampling and Imaging Applications 
Description This package contains an ODL compatible implementation of the Stochastic Primal-Dual Hybrid Gradient algorithm (SPDHG) proposed and analyzed in our associated publication. It is useful to solve inverse problems with non-smooth regularisation and large-scale data. SPDHG is a direct generalization of the popular Primal-Dual Hybrid Gradient algorithm (PDHG) also known as the Chambolle-Pock algorithm. 
Type Of Technology Software 
Year Produced 2018 
Open Source License? Yes  
Impact New EPSRC project on Clinical PET reconstruction. 
URL https://epubs.siam.org/doi/10.1137/17M1134834
 
Description Conference: Developments in Healthcare Imaging - Connecting with Industry 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Industry/Business
Results and Impact A user engagement event was held, showcasing the work of the CMIH, with numerous industrial contacts present. The event gave a great opportunity for clinicians, academics and industrial partners to network. Many participants provided feedback on how useful this opportunity was, and how they had hugely beneficial conversations within the networking opportunities (lunch and coffee breaks, and a poster reception), as well as learning more about the centres work through the talks. Some potential industrial collaborators attended, and are looking to further their connections with the centre.
Year(s) Of Engagement Activity 2016
URL https://www.turing-gateway.cam.ac.uk/event/tgmw37
 
Description Artificial Intelligence and Machine Learning in Clinical Imaging Research: Progress and Promise 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Professional Practitioners
Results and Impact The National Institute for Health Research (NIHR) and the EPSRC Centre for Mathematical Imaging in Healthcare (CMIH) held this event to consider how to accelerate translation of artificial intelligence into clinical practice.
Year(s) Of Engagement Activity 2018
URL https://gateway.newton.ac.uk/event/tgmw64
 
Description BIRS Workshop on Optimal Transport meets Probability, Statistics and Machine Learning, 30 April - 5 May 5, 2017, Oaxaca, Mexico. 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Professional Practitioners
Results and Impact Organizers

Guillaume Carlier (Université Paris Dauphine)

marco cuturi (ENSAE)

Brendan Pass (University of Alberta)

Carola-Bibiane Schönlieb (University of Cambridge)

Objectives

1) Optimal transport has long standing connections to probability, which have been amplified in recent years. For example, variants of the classical optimal transport problem have arisen in connection to applications in financial mathematics (including transport problems with additional dependence constraints and martingale optimal transport, where versions with several, or even infinitely many, prescribed marginals are also of interest) and Schrödinger's problem of minimizing the relative entropy of stochastic processes with fixed initial and final laws. In addition, Wasserstein barycenters have recently been developed as a natural tool to average or interpolate among several probability measures, against the background geometry of optimal transport. This extends the celebrated notion of displacement interpolation between two measures, and has recently found many fruitful applications, in image processing, economics and statistics. Each of these problems brings forth significant new challenges, both theoretical and computational; in addition to addressing these, we have strong reasons to believe that bringing together leaders in optimal transport with experts in probability might uncover even more connections and will stimulate research on both sides.

2) The interest in multi-marginal optimal transport problems is also rapidly growing, driven in particular by its connection with density functional theory in quantum chemistry and fluid dynamics (Brenier's generalized solutions of incompressibe Euler). Understanding the structure, regularity and sparsity properties of optimal plans for multi-marginal transport problems is a very active and challenging area of research. Fast numerical solvers yet are still to be found to address these typically very high-dimensional problems. One of our goals in gathering specialists of optimal transport (both theoretical and computational), probability and statistics in a broad sense is to better understand how, for instance, Markov Chain Monte Carlo methods could help overcome such a computational bottleneck.

3) As outlined above, OT methods are rapidly developing in statistics and econometrics, one reason-among many others being for instance that the Brenier's map may be viewed as one of the most natural multivariate extensions of the notion of quantile. Statistical inference based on Wasserstein distances is therefore becoming more and more popular. However, there are few rigorous limit theorems (apart from the real line case) which fully justify its use and one aim of this workshop is to make progress on such delicate issues which intimately connect probability, analysis and the geometry of Wasserstein spaces.

4)Representation of datasets, classification and measurement of similarities/disparities between complex data or objects such as images or collection of histograms are ubiquitous problems in machine learning. Optimal transport based distances are used more and more frequently to address these questions. For instance in principal component analysis (PCA) one aims to approximate in the most accurate way a large cloud of points by a small dimensional manifold and geodesic Wasserstein PCA is becoming a popular tool for large collections of histograms. Another important problem is metric learning which can somehow be viewed as a sort of inverse transport problem: what can be infered on the distance between objects given an observed coupling between them? Bringing specialists of questions using optimal transport methods, it is our hope to have a clearer picture and better geometric and analytic understanding on the performances and complexity of computational OT based methods in machine learning.
Year(s) Of Engagement Activity 2017
URL http://www.birs.ca/events/2017/5-day-workshops/17w5093
 
Description Big Data in Medicine Conference 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Third sector organisations
Results and Impact A Big data in Medicine conference was co-hosted by the CMIH, attended by a variety of academics, industrial partners, students, and clinicians.
Year(s) Of Engagement Activity 2017
URL https://www.bigdata.cam.ac.uk/events/events-archive/2017-events/copy_of_big-data-in-medicine-confere...
 
Description Big Data, Multimodality & Dynamic Models in Biomedical Imaging 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Other audiences
Results and Impact The CMIH were heavily involved in a conference bringing together big data and medicine, both centre directors were involved in the planning and organisation. The conference bought together potential partners and industry specialist, leading to future engagement.
Year(s) Of Engagement Activity 2016
URL https://www.turing-gateway.cam.ac.uk/event/tgmw32
 
Description British Science Association Media Fellowship 
Form Of Engagement Activity A magazine, newsletter or online publication
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Public/other audiences
Results and Impact In 2017, I was awarded a prestigious British Science Association Media Fellowship. I spent 4 weeks working as a full-time science correspondent at The Guardian newspaper. This experience sharpened my writing and given me valuable insights into how a leading media organisation reports science to the public.
Year(s) Of Engagement Activity 2017
URL https://www.theguardian.com/profile/james-rudd
 
Description Building Machine learning models for expensive Forward Problems in Medical Imaging. 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Professional Practitioners
Results and Impact I presented a poster with the title "Surrogate Model for The Forward Problem Associated with EEG".
Year(s) Of Engagement Activity 2018
 
Description CCIMI New Directions in the Mathematics of Information, Thursday 10th November 2016 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Professional Practitioners
Results and Impact Thursday 10th November 2016
Background

Launched in May this year, the Cantab Capital Institute for the Mathematics of Information (CCIMI) is now fully operational with research studentships and a number of exciting collaborative projects underway. Established through philanthropic support of £5 million from Cantab Capital Partners, the Institute accommodates research activity on fundamental mathematical problems and methodology for understanding, analysing, processing and simulating data. The data science research performed in the Institute is at the highest international level, with a key aim to extract relevant information from large and high-dimensional data with a predictable certainty.

The advance of data science and the solution of big data questions heavily relies on fundamental mathematical techniques and in particular, their intra-disciplinary engagement. This is at the heart of the Institute, involving mathematical expertise ranging from statistics, applied and computational analysis, to topology and discrete geometry - all with the common goal of advancing data science questions.

This event provided an opportunity for a more detailed update on current research taking place at the Institute, associated challenges and other potential collaborative opportunities.



Aims and Objectives

Current research projects being undertaken by the Institute encompass a variety of activity, from the development of mathematical and statistical tools and techniques for high-dimensional data analysis to hybrid image reconstruction and analysis models. As well as the development of rigorous machine learning methodologies that are accessible by mathematical and statistical analysis techniques. Applications of interest include adaptive image analysis, such as image classification, segmentation and enhancement, all the way to inverse problems in industrial and medical imaging.

This event provided an update to the original launch in May, providing more detailed information on research projects and collaborations, as well as highlighting potential new ones. It featured presentations of some of the current project collaborations.

The Institute currently has 11 projects:

Formation and Adaptation of Biological Transportation Networks
Statistical and Computational Aspects of Aggregating Data Summaries
Mathematics of Machine Learning: Mathematical Learning Methods for Adaptive and Robust Data Analysis
Mixing Times
Denoising Geodesic Ray Transforms
Mathematical challenges in Electron Tomography
Mathematical Challenges of Large Environmental Data Sets
Computational and Statistical Joint Image Analysis
Statistical Applications of Persistent Homology
Bayesian Inference for Discretely Sampled Diffusions - Solving the Nonlinear Inverse Problem
To Create a Semantic Search Engine for Mathematical Literature
For more information on these projects please visit the CCIMI Website.

The Institute's research and projects cover a diverse range of areas across Big Data and so we expect this event to be of interest to individuals from multiple researcher, industry and public communities.

Presentations were followed by a drinks and networking reception and the afternoon finished with a Public Lecture by David Spiegelhalter. A copy of the programme can be found here.
Year(s) Of Engagement Activity 2016
URL http://www.turing-gateway.cam.ac.uk/event/tgmw38
 
Description CMIH Imaging Clinic 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach Local
Primary Audience Postgraduate students
Results and Impact The CMIH hosts an Imaging Clinic, here members of the University, Clinicians and Industrial partners can drop in for advice on imaging related problems and to discuss potential collaborations
Year(s) Of Engagement Activity 2018,2019
URL https://www.cmih.maths.cam.ac.uk/imaging-clinic/
 
Description CMIH Imaging Clinic 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach Local
Primary Audience Postgraduate students
Results and Impact The CMIH hosts an imaging clinic, where member of the University and Industrial partners can drop in for advice on imaging related problems, and discuss potential collaborations.
Year(s) Of Engagement Activity 2017
URL https://www.cmih.maths.cam.ac.uk/imaging-clinic/
 
Description CMIH Quarterly newsletter 
Form Of Engagement Activity A magazine, newsletter or online publication
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Supporters
Results and Impact A quarterly newsletter is sent to all who have signed up via our website (currently just over 130), this gives a large spectrum of engagement, including those in industry, the nhs, academia and beyond. The newsletter led to further followers of or twitter feed, and diagnostics show links to larger articles on our activities, and to future events were accessed via the newsletter.
Year(s) Of Engagement Activity 2018,2019
URL https://mailchi.mp/2afeebd9dbc1/cmih-autumn-2017-newsletter-1580093
 
Description CMIH Twitter feed 
Form Of Engagement Activity Engagement focused website, blog or social media channel
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Other audiences
Results and Impact A twitter account has been established and maintained, which reports on the work of the centre, advertises future events, showcases news items, and creates a social media presence for the centre. Posts seem to provide further sign ups to the website/newsletter (as well as vice versa), and produce more followers and retweets. Currently (March 2019) there are 237 followers, from a variety of countries
Year(s) Of Engagement Activity 2018,2019
URL https://twitter.com/CambridgeCMIH
 
Description CMIH website 
Form Of Engagement Activity Engagement focused website, blog or social media channel
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Public/other audiences
Results and Impact Launch and continual updating of Centre Website, to showcase current and future projects, add news items on acitvities within (and beyond) centre, and provide information on centre to those interested. During 2017 there were over 4500 visits to the website, 58% of which were new visitors.
Year(s) Of Engagement Activity 2016,2017
URL http://cmih.maths.cam.ac.uk/
 
Description CMIH website 
Form Of Engagement Activity Engagement focused website, blog or social media channel
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Public/other audiences
Results and Impact The CMIH website an online permanent presence of the centre and is continually updated. It showcases current and future projects, news items on activities from within - and beyond - the centre and provides information on the centre to interested parties/individuals. During 2018 there were over 5800 visitors to the website over the last 12 months.
Year(s) Of Engagement Activity 2018,2019
URL http://cmih.maths.cam.ac.uk/
 
Description Cambridge Research Magazine 
Form Of Engagement Activity A magazine, newsletter or online publication
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Public/other audiences
Results and Impact The Cambridge Research Magazine - Research Horizons - ran an article on our research. This focused on the use of statistics and linguistics to recreate ancient languages. This was picked up by international media and the Daily Mail ran a long article on the ideas (see other entry).
Year(s) Of Engagement Activity 2016
URL https://issuu.com/uni_cambridge/docs/issue_30_research_horizons/1?e=1892280/36314892
 
Description Cambridge Science Festival 
Form Of Engagement Activity Participation in an open day or visit at my research institution
Part Of Official Scheme? Yes
Type Of Presentation Poster Presentation
Geographic Reach National
Primary Audience Schools
Results and Impact We demonstrated several aspects of our research, through video, posters and direct discussion.

High level of interest in participating in medical research and science generally.
Year(s) Of Engagement Activity 2012
 
Description Cambridge Science Festival 
Form Of Engagement Activity Participation in an open day or visit at my research institution
Part Of Official Scheme? No
Geographic Reach Regional
Primary Audience Public/other audiences
Results and Impact The Cambridge Science Festival is a very well attended event each year. We presented a linguistic demonstration based on speaking ancient languages and showed how this related to mathematics. We also mentioned how this could be related to other mathematical problems such as those arising in Imaging.
Year(s) Of Engagement Activity 2016
 
Description Cantab Capital Institute for the Mathematics of Information -- Launch Event, 9 May 2016, Isaac Newton Institute, Cambridge, UK. 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Professional Practitioners
Results and Impact This event celebrated the launch of an exciting new research Institute which is a collaboration between the Cantab Capital Partners LLP and the University of Cambridge. Hosted within the Faculty of Mathematics of the University of Cambridge, the Cantab Capital Institute for the Mathematics of Information will push the boundaries of information science.

Established through philanthropic support of £5 million from Cantab Capital Partners, the Institute will accommodate research activity on fundamental mathematical problems and methodology for understanding, analysing, processing and simulating data. Data science research performed in the Institute will be on the highest international level, aiming to extract the relevant information from large-and high-dimensional data with a predictable certainty.

At the heart of the Institute, will be the fundamental mathematical techniques and their intra-disciplinary engagement, upon which, the solution of big data questions so heavily relies. This is crucial in order to ensure advancements in data science.

Aims and Objectives

This launch event provided an opportunity to learn more about the work of the Institute, such as the specific questions that feed into fundamental methodology development. It is anticipated that the research will focus on various applications across a number of interdisciplinary engagements. These could include for instance, economists and social scientists on questions about financial markets and the internet, or with physicists and engineers on software and hardware development questions in the context of security.

Presentations at the event introduced areas of mathematical expertise represented in the Institute and outline how fundamental techniques can be drawn on to meet the challenge of deciphering meaning in the ever growing volumes of data. Academic expertise at the Institute includes:

Statistics
Applied and Computational Analysis
Stochastic Analysis and Probability
Inverse Problems
Convex Analysis
Stochastic and Sparse Optimisation
Compressed Sensing and Sampling Theory
Partial Differential Equations
Number Theory
Quantum Computing, Cryptography and Communication
The highlight of the launch was the inaugural lecture of the Institute given by Professor Ronald DeVore from Texas A&M University. Ron is one of the key figures of modern applied mathematics and made substantial contributions to approximation theory, numerical analysis of partial differential equations, wavelet transforms, machine learning algorithms and the theory of compressive sensing.
Year(s) Of Engagement Activity 2016
URL http://www.turing-gateway.cam.ac.uk/event/tgmw34
 
Description Daily Mail toothpaste interview 
Form Of Engagement Activity A press release, press conference or response to a media enquiry/interview
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Public/other audiences
Results and Impact Long interview about NaF PET imaging with the Daily Mail.
Year(s) Of Engagement Activity 2015
URL http://www.dailymail.co.uk/health/article-3158692/Cheap-easy-toothpaste-test-spots-risk-stroke-heart...
 
Description Developments in Healthcare Imaging - Connecting with Academia 2017 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Postgraduate students
Results and Impact An annual workshop was hosted to share the latest academic insights and work in the field.
Year(s) Of Engagement Activity 2017
URL https://www.turing-gateway.cam.ac.uk/event/tgmw42
 
Description Developments in Healthcare Imaging - Connecting with Academia 2018 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Postgraduate students
Results and Impact An annual event that brought together academics working on advances in imaging technology with researchers who investigate new image analysis methods, to help address current challenges. This event presented the opportunity to hear in detail about some of the current project collaborations, and focused on the academic interactions taking place in the field of medical imaging and especially across the EPSRC Centres for Mathematical Sciences in Healthcare.
Year(s) Of Engagement Activity 2018
URL https://gateway.newton.ac.uk/event/tgmw58
 
Description Developments in Healthcare Imaging - Connecting with Industry 2017 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Industry/Business
Results and Impact The CMIH hosted an annual engagement workshop, attended by industrial partners, clinicians, and academics working in the field
Year(s) Of Engagement Activity 2017
URL https://www.turing-gateway.cam.ac.uk/event/tgmw48
 
Description Developments in Healthcare Imaging - Connecting with Industry 2018 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Industry/Business
Results and Impact The CMIH hosted an annual engagement workshop, attended by industrial partners, clinicians, and academics working in the field. This user engagement day provided an update on some of the research projects and collaborations taking place in the CMIH. It featured presentations from CMIH researchers and Industry Partners and a number of industry challenges and potential new collaborations were highlighted in an elevator pitch session.
Year(s) Of Engagement Activity 2018
URL https://gateway.newton.ac.uk/event/tgmw61
 
Description Digital Media Editor, Heart (BMJ) 
Form Of Engagement Activity Engagement focused website, blog or social media channel
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Professional Practitioners
Results and Impact Digital Media Editor at Heart, running podcasts and social media to engage practitioners.
Year(s) Of Engagement Activity 2015,2016,2017,2018
URL http://heart.bmj.com/
 
Description EPSRC Five Centres Maths In Healthcare Meeting 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Postgraduate students
Results and Impact The event was jointly hosted by the Liverpool and Glasgow Centres and brought together each of the five EPSRC funded groups - Cambridge, Exeter, Glasgow, Liverpool and London. Participants including researchers, clinicians and industry stakeholders engaged and discussed their research, continuing the theme of collaboration in addressing current healthcare challenges. Representatives from the EPSRC attended and provided their perspective on the mid-term reviews and discussed the way forward.
Year(s) Of Engagement Activity 2018
URL https://www.cmih.maths.cam.ac.uk/epsrc-five-centres-maths-healthcare-meeting/
 
Description Emerging Analytical Professionals 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Professional Practitioners
Results and Impact Talk to approximately 100 individuals from a diverse background, including chemistry and materials science.
Year(s) Of Engagement Activity 2017
 
Description Featured speaker at Cambridge Data Science Summit 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Professional Practitioners
Results and Impact The Cambridge Data Science Summit is a multi-disciplinary event for researchers, developers and data professionals. Join us for inspiring keynotes, panel debates, practical data science training and numerous networking opportunities to connect, develop new skills and gain insight into the constantly evolving field of data science.Gain new knowledge and insights from a range of sectors, from research and bioinformatics to business and finance. Network and connect with researchers, data scientists and technologists from different domains and industries.
Year(s) Of Engagement Activity 2017
URL https://www.cambridgenetwork.co.uk/events/data-science-summit/
 
Description Francis Crick Institute IVI Seminar 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach Local
Primary Audience Professional Practitioners
Results and Impact Follow up visits
Year(s) Of Engagement Activity 2018
 
Description General meeting of the European Women in Mathematics Association, 3-7 September 2018, Graz, Austria. Co-organisers: K. Baur, K. Hess, E. Resmerita and S. Terracini. 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Postgraduate students
Results and Impact General Meeting of the European Women in Mathematics Association
Year(s) Of Engagement Activity 2018
URL https://sites.google.com/site/ewmgm18/
 
Description Hosting of Forum for Royal College of Radiologists 
Form Of Engagement Activity Engagement focused website, blog or social media channel
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Professional Practitioners
Results and Impact A forum space for the Royal College of Radiologists has recently been launched within the CMIH website, allowing members (and those outside of the RCR) to discuss various radiology related matters.
Year(s) Of Engagement Activity 2017
URL http://cmih.maths.cam.ac.uk/community/
 
Description ICFO L4H Seminar, Spain 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Professional Practitioners
Results and Impact Departmental seminar
Year(s) Of Engagement Activity 2016
 
Description IMA Conference on Inverse Problems 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Professional Practitioners
Results and Impact Tuesday 19th - Thursday 21st September 2017

An inverse problem denotes the task of computing an unknown physical quantity from indirect measurements. The corresponding forward problem maps the physical quantity to the measurements. In most realistic situations the solution of the inverse problem is challenging, complicated by incomplete and noisy measurements, as well as non-invertible forward operators which render the inverse problem ill-posed (that is lack of stability and/or uniqueness of solutions). Inverse problems appear in many practical applications in biology, medicine, weather forecasting, chemistry, engineering, physics, to name but a few, and their analysis and solution presents considerable challenges in mathematics and statistics. This conference will bring together mathematicians and statisticians, working on theoretical and numerical aspects of inverse problems, and engineers, physicists and other scientists, working on challenging inverse problem applications. We welcome industrial representatives, doctoral students, early career and established academics working in this field to attend.

Conference topics:
Imaging
Regularisation theory
Statistical inverse problems
Sampling
Data assimilation
Inverse problem applications

Confirmed Invited Speakers:
Dr Marta M. Betcke (University College London)
Professor Dan Crisan (Imperial College London)
Professor Jari Kaipio (University of Auckland, New Zealand)
Professor Dirk Lorenz (TU Braunschweig, Germany)
Professor Bill Symes (Rice University)
Dr Tanja Tarvainen (University of Eastern Finland)

Organising Committee:
CarolaBibiane Schönlieb (Cambridge University) Chair
Cristiana Sebu (Oxford Brookes) - Co-chair
Paul Ledger (Swansea University)
Bill Lionheart (University of Manchester)

Scientific Committee:
Simon Arridge (University College London)
Martin Burger (University of Münster)
Daniela Calvetti (Case Western Reserve University)
Paul Childs
Barbara Kaltenbacher (University of Klagenfurt)
Roland Potthast (University of Reading)
Samuli Siltanen (University of Helsinki)
Year(s) Of Engagement Activity 2017
URL http://www.ima.org.uk/conferences/conferences_calendar/inverse-problems.html
 
Description IoP Optics in Clinical Practice 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Professional Practitioners
Results and Impact Follow up discussions
Year(s) Of Engagement Activity 2018
 
Description KTH Departmental Seminar, Sweden 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach Local
Primary Audience Professional Practitioners
Results and Impact Departmental seminar in Medical Physics
Year(s) Of Engagement Activity 2016
 
Description Launch Event 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Industry/Business
Results and Impact A Launch event was held to celebrate the start of the centre. Talks from relevant academics and partners were given. Relevant stakeholders, from industry, academia, and the public sector (NHS) attended. The proposed work and aims of the centre were well communicated, leading to increased interest from attendees, and links for future work. Attendees gained an increased understanding of the centre, and ways in which they can benefit from its work or work alongside it.
Year(s) Of Engagement Activity 2016
URL https://www.turing-gateway.cam.ac.uk/event/tgmw31
 
Description MFO mini-workshop on Deep Learning and Inverse Problems, 4-10 March 2018, Oberwolfach, Germany. Co-organisers: S. Arridge, M. de Hoop and P. Maass. 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Other audiences
Results and Impact Oberwolfach Workshop on Inverse Problems and deep learning.
Year(s) Of Engagement Activity 2018
URL https://www.mfo.de/occasion/1810c/www_view
 
Description Model-based learning in imaging 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Professional Practitioners
Results and Impact SIAM's Annual Meeting provides a broad view of the state of the art in applied mathematics, computational science, and their applications through invited presentation, prize lectures, minisymposia, contributed papers and posters.
Year(s) Of Engagement Activity 2017
URL https://archive.siam.org/meetings/an17/
 
Description Naked Scientists 
Form Of Engagement Activity A press release, press conference or response to a media enquiry/interview
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Media (as a channel to the public)
Results and Impact Interviewed live on BBC Radio Cambridgeshire by Naked Scientists about 'using light to fight cancer'; the final programme was then published as a podcast and broadcast internationally
Year(s) Of Engagement Activity 2016
 
Description PLUS article What the eye can't see 
Form Of Engagement Activity A magazine, newsletter or online publication
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Public/other audiences
Results and Impact Pictures play a vital role in our lives. They allow us to discover the world, to understand it and to enjoy it. Our own pair of eyes is a powerful tool, but modern imaging technology goes a lot further, revealing distant galaxies and tiny cells in our bodies. Mathematics is the language that underlies this technology, which is why the Cambridge Image Analysis Group, led by Carola-Bibiane Schönlieb, is at home in the Department of Applied Mathematics and Theoretical Physics at the University of Cambridge.
Year(s) Of Engagement Activity 2016
URL https://plus.maths.org/content/what-eye-cant-see
 
Description PLUS magazine Mathematical Moments 
Form Of Engagement Activity A press release, press conference or response to a media enquiry/interview
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Public/other audiences
Results and Impact Mathematical moments: Carola-Bibiane Schönlieb

Carola-Bibiane Schönlieb has a fascinating job: she works on the mathematics behind image analysis. It finds application in all sorts of areas, from medical imaging, such as MRI scans, to forest ecology, which sees scientists trying to gain information about forests from pictures taken from the air.

In this brief interview Carola tells us why she likes doing maths, recalls some of her favourite mathematical moments, and explains why creativity is essential in mathematics.
Year(s) Of Engagement Activity 2016
URL https://plus.maths.org/content/mathematical-moments-carola-schonlieb
 
Description POEMS workshop on Big Data, Multimodality & Dynamic Models in Biomedical Imaging, 9th March 2016, Isaac Newton Institute, Cambridge, UK 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Professional Practitioners
Results and Impact Wednesday 9th March 2016
Background

We are currently experiencing many new exciting developments in imaging technology in biology and medicine. New advances in tomographic imaging, such as photoacoustic tomography, electron tomography, multicontrast magnetic resonance tomography (MRT) and combined MR with positron emission tomography (PET), as well as new technology in microscopy such as lightsheet microscopy, only mark the beginning of an era which revolutionises the extent of what we can see. New imaging technology always goes side by side with the need of mathematical models to maximise the information gain from these novel imaging techniques. For instance, previously tomographic imaging and light microscopy were separate imaging modalities, which were difficult to cross correlate. However, rapid development of new imaging hardware (light sheet, polarized PET, MRI), is now opening up new avenues for translational multimodal imaging. These developments are supported by sophisticated and rigorous mathematical models, which enhance the information in one imaging modality with information from another.

Aims and Objectives

New imaging technologies however, also bring new challenges to be overcome. In electron tomography for example, the limited angle problem is an intrinsic hardware limitation which results in viewpoint angles in which the imaged specimen cannot be resolved. Dynamic imaging techniques produce huge amounts of image data which require reliable and efficient methods for interpretation and analysis.

This one day meeting aimed to bring together those working on advances in imaging technology with researchers who investigate new image analysis methods, to help address these challenges. In particular, there was a focus on the following topics:

Big data problems and solutions
Multimodality
Dynamic imaging
The workshop facilitated the communication of both current opportunities and challenges of new imaging techniques. It also allowed for the sharing of knowledge on current approaches and solutions of mathematical modelling and analysis approaches, with presentations on industry insights and state-of-the-art mathematical techniques for Big Data Analytics.

This event was of interest to participants from the biomedical imaging industry, mathematics, engineering, computer science and physics, as well as biology and medicine.
Year(s) Of Engagement Activity 2016
URL http://www.turing-gateway.cam.ac.uk/event/tgmw32
 
Description Panel member at Women in Data Science event at the Isaac Newton Institute, Cambridge 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Professional Practitioners
Results and Impact The field of Data Science is booming, yet comparatively few women are entering it. Why? What are the obstacles and opportunities facing them if they do? The path to change is challenging, but there are women out there who can testify that it's possible.

A Women in Data Science event is being held at the Isaac Newton Institute on Wednesday 7 December within the New Developments in Data Privacy workshop, part of the current INI programme on Data Linkage and Anonymisation.

The event included a Women's Round Table Event and Wine Reception

Panelists included:

Prof Sheila Bird (Medical Research Council)
Prof Cynthia Dwork (Microsoft Research and Harvard)
Prof Sofia Olhede (UCL)
Dr Carola-Bibiane Schönlieb (Cantab Capital Institute for Mathematics of Information, University of Cambridge, and Alan Turing Institute Faculty Fellow)
Prof Natalie Shlomo (University of Manchester)
Year(s) Of Engagement Activity 2016
URL http://www.newton.ac.uk/node/1273254
 
Description Pint of Science 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach Local
Primary Audience Public/other audiences
Results and Impact Delivered a lecture paired with optics demonstrations to illustrate novel endoscopic imaging to the public
Year(s) Of Engagement Activity 2016
 
Description Podcasting for Radio Cambridge and the Naked Scientists programme 
Form Of Engagement Activity A broadcast e.g. TV/radio/film/podcast (other than news/press)
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Public/other audiences
Results and Impact Several interviews about cardiovascular research and heart matters. Widely broadcast (Radio 5, ABC in Australia, podcasted).
Year(s) Of Engagement Activity 2014,2015
URL http://www.thenakedscientists.com/HTML/interviews/interview/1001368/
 
Description Presentation at Faculty of Engineering, University of Melbourne 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach Regional
Primary Audience Postgraduate students
Results and Impact A one-hour presentation held in the Faculty of Engineering, University of Melbourne. The presentation covered the numerical studies on cardiovascular diseases in Cambridge, at particularly EPSRC Centre for Mathematical and Statistical Analysis of Multimodal Clinical Imaging. There around 60 audiences, mostly academic faculties and research students, but also include clinicians. The talk sparked lots of questions and discussions afterwards, with potential academic collaboration forged.
Year(s) Of Engagement Activity 2017
 
Description Quarterly Newsletter 
Form Of Engagement Activity A magazine, newsletter or online publication
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Supporters
Results and Impact A quarterly newsletter is sent to all who have signed up on our website (currently just over 100), this gives a large spectrum of engagement, including those in industry, the nhs, academia and beyond. The newsletter led to further followers of or twitter feed, and diagnostics show links to larger articles on our activities, and to future events were accessed via the newsletter.
Year(s) Of Engagement Activity 2016,2017,2018
URL https://us13.campaign-archive.com/home/?u=b8f3673f56646114bea617691&id=d99002a010
 
Description Seeing more in pictures - open mathematics day for Y12 girls 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Schools
Results and Impact Dr Carola Schönlieb gives an insight into some of the mathematics behind image analysis and its wide-ranging applications in fields ranging from developing cancer therapies to restoring artworks, together with some personal reflections on her own career journey through mathematical study and research.

Dr Carola Schönlieb is Reader in Applied and Computational Analysis and Head of Cambridge Image Analysis Group at the Department of Applied Mathematics and Theoretical Physics, University of Cambridge.

This talk was originally given to an audience of Y12 girls (aged 16-17) at an event for students considering applying to university to study mathematics. The talk was recorded at the Centre for Mathematical Sciences, University of Cambridge, on 18 April 2016.
Year(s) Of Engagement Activity 2016
URL https://www.youtube.com/watch?v=9SPN9Ouxx7g&feature=youtu.be
 
Description Soapbox Science 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach Local
Primary Audience Public/other audiences
Results and Impact Stood on a soapbox in Cambridge Market Square explaining the concepts of wavelength and polarization, illustrating how they might be used in diagnosis of cancer.
Year(s) Of Engagement Activity 2016
 
Description Software For Computing The Forward Problem of EEG For The Case of A realistic Head Model 
Form Of Engagement Activity Engagement focused website, blog or social media channel
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Professional Practitioners
Results and Impact We used the Pseven Datadvance Platform (https://www.datadvance.net/) to train and build a surrogate model for the case of the Forward Problem Associated with EEG imaging.
We then shared the code on Github.
Here is a link to the code:
https://github.com/parham1976/Surrogate-Model-of-OpenMEEG/tree/master/surrogate_model_dir
Year(s) Of Engagement Activity 2018
URL https://github.com/parham1976/Surrogate-Model-of-OpenMEEG/tree/master/surrogate_model_dir
 
Description Southampton Bioengineering Research Seminar, UK 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach Local
Primary Audience Professional Practitioners
Results and Impact Local Departmental seminar
Year(s) Of Engagement Activity 2016
 
Description TOPIM TECH Introductory Lecture 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Postgraduate students
Results and Impact Gave a 2 hour introductory lecture to imaging biomarkers with an interactive activity on technical and biological validation of biomarkers.
Year(s) Of Engagement Activity 2017
 
Description Talk at Big Data Showcase event in Cambridge on Seeing More in Images: A Mathematical Perspective 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Industry/Business
Results and Impact Background

Mathematics underpins so many things we take for granted - smart phones, weather forecasting, architecture, 3D software, structural engineering, sensing technologies, are just a few examples. Indeed, the fruits of mathematical research - such as the resulting techniques and algorithms, affect the daily lives of everyone and the economic impact of mathematics is already well proven.

The University of Cambridge's Centre for Mathematical Sciences (CMS) and Big Data Initiative, in partnership with the Turing Gateway to Mathematics highlighted areas of research and expertise in a showcase event, that took place on Wednesday 20th April 2016 at the Centre for Mathematical Sciences in Wilberforce Road, Cambridge.

A wide range of talks were given by leading researchers, highlighting areas of mathematical and Big Data sciences. An exhibition ran during the lunch break and the afternoon session. The day ended with a drinks and networking reception between 5.00-6.00PM.

The Showcase presented a great opportunity to see what Cambridge has to offer and better understand the diversity and impact that the research in mathematics and big data at Cambridge can make on business and policy across a wide range of areas. Researchers were on hand to discuss specific areas of maths and big data and there was opportunities for delegates to take tours of the GK Batchelor Fluid Dynamics Laboratory and also see the COSMOS Computer. The exhibition included research groups, industrial case studies posters, the EPSRC Centre for Doctoral Training in Analysis, the new Cantab Capital Institute for the Mathematics of Information and details of project opportunities for industry.

Aims and Objectives

Subjects of talks included industrial areas such as materials and chemical decontamination, mathematical biology, financial maths, cosmology, communications and social sciences. The Showcase presented an excellent opportunity to bring together scientists from mathematics and other disciplines such as physics, chemistry, engineering etc, with interested parties from industry, government and public sectors.

The Showcase gave participants a great opportunity to:

Meet leading mathematicians and other scientists involved in state-of-the-art mathematical techniques and methods across multiple areas including Big Data
Learn more about the potential of mathematics to help provide solutions to real-world problems
Find out how to collaborate and partner with the University through research, projects and studentships
Network with senior researchers, industry and Government
Year(s) Of Engagement Activity 2016
URL http://www.turing-gateway.cam.ac.uk/event/tgmw33
 
Description Talk at Newton Institute 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Other audiences
Results and Impact Will talk at the workshop at Isaac Newton Institute: Statistics of geometric features and new data types (STSW02)
Year(s) Of Engagement Activity 2018
 
Description Twitter account 
Form Of Engagement Activity Engagement focused website, blog or social media channel
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Other audiences
Results and Impact A twitter account has been set up, which reports on the work of the centre, advertises future events, showcases news items, and creates a social media presence for the centre. Posts seem to provide further sign ups to the website/newsletter (as well as vice versa), and produce more followers and retweets. Currently (March 2018) has 155 followers, from a variety of countries
Year(s) Of Engagement Activity 2016
URL https://twitter.com/CambridgeCMIH
 
Description UC Davis Biomedical Engineering Seminar, USA 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Professional Practitioners
Results and Impact Local departmental seminar
Year(s) Of Engagement Activity 2016
 
Description UEG week 
Form Of Engagement Activity A press release, press conference or response to a media enquiry/interview
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Media (as a channel to the public)
Results and Impact 1. Interviewed by Pan European Networks magazine for Horizon 2020 Projects: Portal resulting in an article entitled 'The cutting edge of cancer care' (2016)
2. Featured on EurekAlert! from AAAS, Medical Device Weekly and IndiaTimes (2016).
Year(s) Of Engagement Activity 2016
 
Description USC Departmental Seminar, USA 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Professional Practitioners
Results and Impact Gave a talk to the Translational Imaging Center at USC with approximately 30 attendees, which stimulated discussions and resulted in a collaboration between our centres.
Year(s) Of Engagement Activity 2017
 
Description Uncertainty Quantification in the Mathematics of Healthcare workshop 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Postgraduate students
Results and Impact A workshop was jointly hosted with the EPSRC centre for Predictive modelling in healthcare, focused on Uncertainty Quantification in the Mathematics of Healthcare. Attendees reported better links with and awareness of the centre, as well as new contacts to discuss potential projects and further work with. Many attendees requested further similar events to discuss other joint problems in the community of maths of healthcare.
Year(s) Of Engagement Activity 2017
URL http://cmih.maths.cam.ac.uk/uncertainty-quantification-mathematics-healthcare-workshop/
 
Description Variational models and partial differential equations for mathematical imaging 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Professional Practitioners
Results and Impact Plenary lecture at the Conference on the Numerical Solution of Differential and Differential-Algebraic Equations (NUMDIFF-15).

Variational models and partial differential equations for mathematical imaging:
Images are a rich source of beautiful mathematical formalism and analysis. Associated mathematical problems arise in functional and non-smooth analysis, the theory and numerical analysis of partial differential equations, harmonic, stochastic and statistical analysis, and optimisation. Starting with a discussion on the intrinsic structure of images and their mathematical representation, in this talk we will learn about variational models for image analysis and their connection to partial differential equations, and go all the way to the challenges of their mathematical analysis as well as the hurdles for solving these - typically non-smooth - models computationally. The talk is furnished with applications of the introduced models to image de-noising, motion estimation and segmentation, as well as their use in biomedical image reconstruction such as it appears in magnetic resonance imaging.
Year(s) Of Engagement Activity 2018
URL https://sim.mathematik.uni-halle.de/numdiff/Numdiff15/index.html
 
Description Waterhouse 
Form Of Engagement Activity A press release, press conference or response to a media enquiry/interview
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Media (as a channel to the public)
Results and Impact Featured on BBC and Financial Times online, as well as numerous trade news outlets and Cambridge TV
Year(s) Of Engagement Activity 2016
 
Description Web blog 
Form Of Engagement Activity Engagement focused website, blog or social media channel
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Public/other audiences
Results and Impact We maintain a blog on our website providing openly accessible descriptions of each paper that we publish.
Year(s) Of Engagement Activity 2015,2016,2017
URL http://www.bohndieklab.org/blog/
 
Description Women in Mathematics exhibition 
Form Of Engagement Activity A magazine, newsletter or online publication
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Undergraduate students
Results and Impact As part of the European Women in Mathematics exhibition, additional portraits of Cambridge Mathematicians, including Carola-Bibiane Schönlieb, were produced. These were accompanied by interviews in Plus magazine, and a launch event, where Carola-Bibiane Schönlieb spoke, and the portraits were then displayed within the Mathematics building.
Year(s) Of Engagement Activity 2017
URL http://plus.maths.org/content/women
 
Description Workshop on Gradient flows: challenges and new directions, ICMS 2018 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Postgraduate students
Results and Impact Gradient flows: challenges and new directions

ICMS, The Bayes Centre, 47 Potterrow, Edinburgh EH8 9BT

10 - 14 September 2018

ORGANISERS

Bertram Düring, University of Sussex
Carola-Bibiane Schönlieb, University of Cambridge
Yves van Gennip, University of Nottingham
Marie-Therese Wolfram, University of Warwick
Year(s) Of Engagement Activity 2018
URL https://www.icms.org.uk/gradientflows.php
 
Description Workshop on High-dimensional Statistics, Inverse Problems and Convex Analysis, Royal Statistical Society, London, UK, 22 March 2016. 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Professional Practitioners
Results and Impact This workshop will bring together scientists from the statistics, applied mathematics, signal processing and machine learning communities around the topic of convex analysis and its application to challenging inverse problems. The workshop will feature invited talks by world-leading experts presenting cutting edge research on new theory, methodology, and computer algorithms. We aim to provide a valuable opportunity to network and to foster extensive future interaction between the these disciplines.

Co-organiser: M. Pereyra.
Year(s) Of Engagement Activity 2016
URL http://www.bristol.ac.uk/maths/events/2016/high-dimensional-statistics-inverse-problems-and-convex-a...
 
Description Workshop on Mathematical imaging with partially unknown models, Jesus College in Cambridge, 20-21 Feb. 2017. 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Professional Practitioners
Results and Impact The CCIMI and CMIH are pleased to be co-sponsoring the Cambridge - Heriot Watt interdisciplinary data science workshop on "Mathematical imaging with partially unknown models" at Jesus College in Cambridge, 20-21 Feb. 2017. More details on the programme will be confirmed at a later date.

The workshop is organised by Marcelo Pereyra (Assistant professor, School of Mathematical and Computer Sciences, Heriot Watt) and Carola-Bibiane Schönlieb (Head of the Cantab Capital Institute for the Mathematics of Information (CCIMI), Cambridge), alongside local organiser Martin Benning (Department of Applied Mathematics and Theoretical Physics, Cambridge)

The workshop aims to gather an interdisciplinary group of leading imaging experts from the applied analysis, statistics, and signal processing communities around the topic of "imaging with partially unknown models". The goal is to promote synergy and cross-fertilisation between these communities and set the basis for a multidisciplinary approach to the problem.

Mathematical imaging is at the core of modern data science, with important applications in medicine, biology, defense, agriculture and environmental sciences. This active research field studies imaging inverse problems involving the estimation of an unobserved true image from measurements that are noisy, incomplete and resolution-limited. This proposal focuses on an increasingly important and particularly challenging class of imaging inverse problems that, in addition to being ill-posed and ill-conditioned, are further complicated by inaccurate and partial knowledge of the observation system and of the properties of the underlying true image (which are essential to regularise the problem and deliver meaningful estimates). These so-called "semi-blind" and "unsupervised" problems are the focus of significant research efforts across a range of scientific communities, particularly applied analysis, Bayesian statistics, and signal processing, which have recently produced important developments in mathematical theory, methods, models and efficient algorithms.

The proposed research workshop will focus on three specific aspects of imaging with partially unknown models that will be key in future methodology: learning models from observed data, model comparison and selection in the absence of ground truth, and robust inference with approximate models.

Programme;
Monday:
9.30 - 10.20: Gabriel Peyré
Coffee break (30 minutes)
10.50 - 11.40: Yves Wiaux
11.40 - 12.30: Samuli Siltanen
Lunch & Poster session (12.30 - 14.00).

Tuesday:
9.30 - 10.20: Silvia Villa
Coffee break (30 minutes)
10.50 - 11.40: Juan Carlos de los Reyes
11.40 - 12.30: John Aston

Plenary speakers are:
Gabriel Peyré (Université Paris-Dauphine)
Silvia Villa (Istituto Italiano di Tecnologia and Massachusetts Institute of Technology)
Yves Wiaux (Heriot-Watt University)
Juan Carlos de los Reyes (Escuela Nacional Politécnica de Quito)
John Aston (University of Cambridge)
Samuli Siltanen (University of Helsinki)

Invited Participants:
Yoann Altmann (Heriot Watt, UK)
Martin Benning (University of Cambridge, UK)
Natalia Bochkina (University of Edinburgh, UK)
Matthias Ehrhardt (University of Cambridge, UK)
Teresa Klatzer (Graz University of Technology, Austria)
Felix Lucka (University College London, UK)
Marcelo Pereyra (Heriot Watt, UK)
Carola-Bibiane Schönlieb (CCIMI, University of Cambridge, UK)

Registrations:
Simon Arridge (UCL, UK)
Eva-Maria Brinkmann (University of Münster, Germany)
Tatiana Alessandra Bubba (University of Helsinki, Finland)
Luca Calatroni (Ecole Polytechnique, France)
Veronica Corona (University of Cambridge, UK)
Jonathan Dunlop (Schlumberger, UK)
Silvia Gazzola (University of Bath, UK)
Joanna Grah (University of Cambridge, UK)
Abderrahim Halimi (Heriot Watt, UK)
Karl Harrison (University of Cambridge, UK)
Andreas Hauptmann (University of Helsinki, Finland)
Jan Holland (Springer-Verlag)
Eugenie Hunsicker (Loughborough University, UK)
Abdul Jumaat (University of Liverpool, UK)
Markus Juvonen (University of Helsinki)
Lukas Lang (University of Cambridge, UK)
Nguyet Minh Mach (University of Helsinki)
Sebastian Neumayer (University of Cambridge, UK)
Simone Parisotto (University of Cambridge, UK)
Mihaela Pricop-Jeckstadt (TU-Dresden, Germany)
Shannon Seah (University of Cambridge, UK)
Ferdia Sherry (University of Cambridge, UK)
Megan Wilson (University of Cambridge, UK)
Joab Winkler (Sheffield University, UK)
Evren Yarman (Schlumberger, UK)
Year(s) Of Engagement Activity 2017
URL http://www.ccimi.maths.cam.ac.uk/events/cambridge-heriot-watt-interdisciplinary-data-science-worksho...
 
Description YouTube 
Form Of Engagement Activity Engagement focused website, blog or social media channel
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Public/other audiences
Results and Impact Post videos relating to our research
Year(s) Of Engagement Activity 2017
URL https://www.youtube.com/channel/UCgKWlKnnn1jYV_OqqFEFziA