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Dr Paddy Slator
(he/him)
- Available for postgraduate supervision
Teams and roles for Paddy Slator
Overview
I joined Cardiff University as a Lecturer in 2023. I am based in the School of Computer Science and Informatics (COMSC) and the Cardiff University Brain Research Imaging Centre (CUBRIC).
My research aims to deliver imaging techniques that enable improved diagnosis, prognosis and monitoring of disease and hence positively impact patient care. I develop magnetic resonance imaging (MRI) analysis and acquisition methods that can non-invasively characterise tissue structure and function in-vivo. I utilise a range of machine learning, Bayesian statistics and biophysical modelling approaches to develop new medical image computing techniques.
I co-organise MicroPhysics meetings in CUBRIC, where our primary focus lies in advancing microstructure imaging techniques, and Medical Image Computing Group meetings in COMSC. Additionally, I am an active member of the Visual Computing Section within COMSC.
Publication
2025
- Powell, E. et al., 2025. Hierarchical Bayesian modelling improves microstructural parameter mapping in diffusion and exchange MRI data. bioRxiv (10.1101/2025.09.04.674046)
- Slator, P. J. et al. 2025. Low field combined diffusion-relaxation MRI for mapping placenta structure and function. Placenta 172 , pp.73-82. (10.1016/j.placenta.2025.10.014)
2024
- Blumberg, S. B. , Slator, P. J. and Alexander, D. C. 2024. Experimental design for multi-channel imaging via task-driven feature selection. Presented at: The International Conference on Learning Representations (ICLR) 2024 Vienna, Austria 7-11 May 2024. Proceedings of 12th International Conference on Learning Representations. ICLR.
- Cromb, D. et al., 2024. Advanced magnetic resonance imaging detects altered placental development in pregnancies affected by congenital heart disease. Scientific Reports 14 (1) 12357. (10.1038/s41598-024-63087-8)
- de Oliveira, D. C. et al., 2024. A flexible generative algorithm for growing in silico placentas. PLoS Computational Biology 20 (10) e1012470. (10.1371/journal.pcbi.1012470)
- Hall, M. et al., 2024. Placental multimodal MRI prior to spontaneous preterm birth <32 weeks' gestation: An observational study. BJOG: An International Journal of Obstetrics and Gynaecology 131 (13), pp.1782-1792. (10.1111/1471-0528.17901)
- Khubrani, Y. H. et al. 2024. Detection of periodontal bone loss and periodontitis from 2D dental radiographs via machine learning and deep learning: Systematic Review employing APPRAISE-AI and meta-analysis. Dentomaxillofacial Radiology twae070. (10.1093/dmfr/twae070)
- Sen, S. et al., 2024. ssVERDICT: Self‐supervised VERDICT‐MRI for enhanced prostate tumor characterization. Magnetic Resonance in Medicine 92 (5), pp.2181-2192. (10.1002/mrm.30186)
2023
- Aja-Fernández, S. et al., 2023. Validation of Deep Learning techniques for quality augmentation in diffusion MRI for clinical studies. NeuroImage: Clinical 39 103483. (10.1016/j.nicl.2023.103483)
- Cromb, D. et al., 2023. Assessing within-subject rates of change of placental MRI diffusion metrics in normal pregnancy. Magnetic Resonance in Medicine 90 (3), pp.1137-1150. (10.1002/mrm.29665)
- Hutter, J. et al., 2023. Multi-modal MRI reveals changes in placental function following preterm premature rupture of membranes. Magnetic Resonance in Medicine 89 (3), pp.1151-1159. (10.1002/mrm.29483)
- Slator, P. J. et al. 2023. Low-field combined diffusion-relaxation MRI for mapping placenta structure and function. [Online].medRxiv. (10.1101/2023.06.06.23290983)Available at: https://doi.org/10.1101/2023.06.06.23290983.
- Slator, P. J. et al. 2023. Non-invasive mapping of human placenta microenvironments throughout pregnancy with diffusion-relaxation MRI. Placenta 144 , pp.29-37. (10.1016/j.placenta.2023.11.002)
2022
- Lim, J. P. et al., 2022. Fitting a directional microstructure model to diffusion-relaxation mri data with self-supervised machine learning. Lecture Notes in Computer Science 13722 , pp.77-88. (10.1007/978-3-031-21206-2_7)
- Sen, S. et al., 2022. Differentiating false positive lesions from clinically significant cancer and normal prostate tissue using VERDICT MRI and other diffusion models. Diagnostics 12 (7) 1631. (10.3390/diagnostics12071631)
2021
- Hutter, J. et al., 2021. An efficient and combined placental T1-ADC acquisition in pregnancies with and without pre-eclampsia. Magnetic Resonance in Medicine 86 (5), pp.2684-2691. (10.1002/mrm.28809)
- Lin, H. et al., 2021. Generalised super resolution for quantitative MRI using self-supervised mixture of experts. Presented at: International Conference on Medical Image Computing and Computer-Assisted Intervention Strasbourg September 27–October 1, 2021. Published in: de Bruijne, M. et al., Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. Vol. 12906.Lecture Notes in Computer Science Cham, Switzerland: Springer. , pp.44-54. (10.1007/978-3-030-87231-1_5)
- Powell, E. et al., 2021. Generalised hierarchical bayesian microstructure modelling for diffusion MRI. Presented at: International Workshop on Computational Diffusion MRI 01 October 2021. Published in: Cetin-Karayumak, S. ed. Computational Diffusion MRI. CDMRI 2021. Vol. 13006.Lecture Notes in Computer Science Springer Nature. , pp.36–47. (10.1007/978-3-030-87615-9_4)
- Slator, P. et al. 2021. Combined diffusion-relaxometry microstructure imaging: Current status and future prospects. Magnetic Resonance in Medicine 86 (6), pp.2987-3011. (10.1002/mrm.28963)
- Slator, P. J. et al. 2021. Anisotropy in the human placenta in pregnancies complicated by fetal growth restriction. In: Özarslan, E. et al., Anisotropy Across Fields and Scales. Mathematics and Visualization Springer. , pp.263–276. (10.1007/978-3-030-56215-1_13)
- Slator, P. et al. 2021. Data-driven multi-contrast spectral microstructure imaging with InSpect: INtegrated SPECTral component estimation and mapping. Medical Image Analysis 71 102045. (10.1016/j.media.2021.102045)
2020
- Ho, A. et al., 2020. Placental magnetic resonance imaging in chronic hypertension: A case-control study. Placenta 104 , pp.138-145. (10.1016/j.placenta.2020.12.006)
- Pizzolato, M. et al., 2020. Acquiring and predicting multidimensional diffusion (MUDI) data: an open challenge. Presented at: MICCAI Workshop Shenzhen, China Oct 2019. Published in: Bonet-Carne, E. et al., Computational Diffusion MRI. Mathematics and Visualization Springer. , pp.195-208. (10.1007/978-3-030-52893-5_17)
- Slator, P. J. et al. 2020. Data-driven multi-contrast spectral microstructure imaging with InSpect. Presented at: MICCAI: International Conference on Medical Image Computing and Computer-Assisted Intervention Lima, Peru 4–8 October, 2020. Published in: Martel, A. et al., Medical Image Computing and Computer Assisted Intervention – MICCAI 2020.. Vol. 12266.Cham: Springer. , pp.375-385. (10.1007/978-3-030-59725-2_36)
2019
- Christiaens, D. et al., 2019. In utero diffusion MRI. Topics in Magnetic Resonance Imaging 28 (5), pp.255-264. (10.1097/RMR.0000000000000211)
- Jackson, L. H. et al., 2019. Respiration resolved imaging with continuous stable state 2D acquisition using linear frequency SWEEP. Magnetic Resonance in Medicine 82 (5), pp.1631-1645. (10.1002/mrm.27834)
- Slator, P. et al. 2019. Placenta Imaging Workshop 2018 report: Multiscale and multimodal approaches. Placenta 79 , pp.78-82. (10.1016/j.placenta.2018.10.010)
- Slator, P. et al. 2019. Combined diffusion-relaxometry MRI to identify dysfunction in the human placenta. Magnetic Resonance in Medicine 82 (1), pp.95-106. (10.1002/mrm.27733)
- Slator, P. et al. 2019. InSpect: INtegrated SPECTral component estimation and mapping for multi-contrast microstructural MRI. Presented at: IPMI 2019: International Conference on Information Processing in Medical Imaging 2-7 June 2019. Published in: Chung, A. C. S. et al., Information Processing in Medical Imaging: 26th International Conference, IPMI 2019, Hong Kong, China, June 2–7, 2019, Proceedings. Springer. , pp.755-766. (10.1007/978-3-030-20351-1_59)
- Slator, P. J. et al. 2019. A framework for calculating time-efficient diffusion MRI protocols for anisotropic IVIM and an application in the placenta. Presented at: MICCAI 2018 Granada, Spain 16-20 September 2018. Published in: Bonet-Carne, E. et al., Computational Diffusion MRI: International MICCAI Workshop, Granada, Spain, September 2018. Mathematics and Visualization Springer, Cham. , pp.251-263. (10.1007/978-3-030-05831-9_20)
2018
- Hutter, J. et al., 2018. Slice-level diffusion encoding for motion and distortion correction. Medical Image Analysis 48 , pp.214-229. (10.1016/j.media.2018.06.008)
- Hutter, J. et al., 2018. Integrated and efficient diffusion-relaxometry using ZEBRA. Scientific Reports 8 15138. (10.1038/s41598-018-33463-2)
- Hutter, J. et al., 2018. Multi-modal functional MRI to explore placental function over gestation. Magnetic Resonance in Medicine 81 (2), pp.1191-1204. (10.1002/mrm.27447)
- Slator, P. J. and Burroughs, N. J. 2018. A hidden Markov model for detecting confinement in single-particle tracking trajectories. Biophysical Journal 115 (9), pp.1741-1754. (10.1016/j.bpj.2018.09.005)
- Slator, P. J. et al. 2018. IVIM MRI of the Placenta. In: Le Bihan, D. et al., Intravoxel Incoherent Motion (IVIM) MRI. New York: Pan Stanford
2017
- Hutter, J. et al., 2017. Dynamic field mapping and motion correction using interleaved double spin-echo diffusion MRI. Presented at: International Conference on Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2017 Quebec City 11 - 13 September 2017. Medical Image Computing and Computer Assisted Intervention − MICCAI 2017. Vol. 10433.Springer International Publishing AG. , pp.523-531. (10.1007/978-3-319-66182-7_60)
- Konstantopoulou, M. et al., 2017. Variation in susceptibility to microbial lignin oxidation in a set of wheat straw cultivars: influence of genetic, seasonal and environmental factors. Nordic Pulp & Paper Research Journal 32 (4), pp.493-507. (10.3183/npprj-2017-32-04_p493-507_bugg)
- Slator, P. J. et al. 2017. Placenta microstructure and microcirculation imaging with diffusion MRI. Magnetic Resonance in Medicine 80 (2), pp.756-766. (10.1002/mrm.27036)
2015
- Slator, P. , Cairo, C. and Burroughs, N. 2015. Detection of diffusion heterogeneity in single particle tracking trajectories using a hidden Markov Model with measurement noise propagation.. PLOS ONE (10.1371/journal.pone.0140759)
Articles
- Aja-Fernández, S. et al., 2023. Validation of Deep Learning techniques for quality augmentation in diffusion MRI for clinical studies. NeuroImage: Clinical 39 103483. (10.1016/j.nicl.2023.103483)
- Christiaens, D. et al., 2019. In utero diffusion MRI. Topics in Magnetic Resonance Imaging 28 (5), pp.255-264. (10.1097/RMR.0000000000000211)
- Cromb, D. et al., 2023. Assessing within-subject rates of change of placental MRI diffusion metrics in normal pregnancy. Magnetic Resonance in Medicine 90 (3), pp.1137-1150. (10.1002/mrm.29665)
- Cromb, D. et al., 2024. Advanced magnetic resonance imaging detects altered placental development in pregnancies affected by congenital heart disease. Scientific Reports 14 (1) 12357. (10.1038/s41598-024-63087-8)
- de Oliveira, D. C. et al., 2024. A flexible generative algorithm for growing in silico placentas. PLoS Computational Biology 20 (10) e1012470. (10.1371/journal.pcbi.1012470)
- Hall, M. et al., 2024. Placental multimodal MRI prior to spontaneous preterm birth <32 weeks' gestation: An observational study. BJOG: An International Journal of Obstetrics and Gynaecology 131 (13), pp.1782-1792. (10.1111/1471-0528.17901)
- Ho, A. et al., 2020. Placental magnetic resonance imaging in chronic hypertension: A case-control study. Placenta 104 , pp.138-145. (10.1016/j.placenta.2020.12.006)
- Hutter, J. et al., 2018. Slice-level diffusion encoding for motion and distortion correction. Medical Image Analysis 48 , pp.214-229. (10.1016/j.media.2018.06.008)
- Hutter, J. et al., 2021. An efficient and combined placental T1-ADC acquisition in pregnancies with and without pre-eclampsia. Magnetic Resonance in Medicine 86 (5), pp.2684-2691. (10.1002/mrm.28809)
- Hutter, J. et al., 2023. Multi-modal MRI reveals changes in placental function following preterm premature rupture of membranes. Magnetic Resonance in Medicine 89 (3), pp.1151-1159. (10.1002/mrm.29483)
- Hutter, J. et al., 2018. Integrated and efficient diffusion-relaxometry using ZEBRA. Scientific Reports 8 15138. (10.1038/s41598-018-33463-2)
- Hutter, J. et al., 2018. Multi-modal functional MRI to explore placental function over gestation. Magnetic Resonance in Medicine 81 (2), pp.1191-1204. (10.1002/mrm.27447)
- Jackson, L. H. et al., 2019. Respiration resolved imaging with continuous stable state 2D acquisition using linear frequency SWEEP. Magnetic Resonance in Medicine 82 (5), pp.1631-1645. (10.1002/mrm.27834)
- Khubrani, Y. H. et al. 2024. Detection of periodontal bone loss and periodontitis from 2D dental radiographs via machine learning and deep learning: Systematic Review employing APPRAISE-AI and meta-analysis. Dentomaxillofacial Radiology twae070. (10.1093/dmfr/twae070)
- Konstantopoulou, M. et al., 2017. Variation in susceptibility to microbial lignin oxidation in a set of wheat straw cultivars: influence of genetic, seasonal and environmental factors. Nordic Pulp & Paper Research Journal 32 (4), pp.493-507. (10.3183/npprj-2017-32-04_p493-507_bugg)
- Lim, J. P. et al., 2022. Fitting a directional microstructure model to diffusion-relaxation mri data with self-supervised machine learning. Lecture Notes in Computer Science 13722 , pp.77-88. (10.1007/978-3-031-21206-2_7)
- Powell, E. et al., 2025. Hierarchical Bayesian modelling improves microstructural parameter mapping in diffusion and exchange MRI data. bioRxiv (10.1101/2025.09.04.674046)
- Sen, S. et al., 2024. ssVERDICT: Self‐supervised VERDICT‐MRI for enhanced prostate tumor characterization. Magnetic Resonance in Medicine 92 (5), pp.2181-2192. (10.1002/mrm.30186)
- Sen, S. et al., 2022. Differentiating false positive lesions from clinically significant cancer and normal prostate tissue using VERDICT MRI and other diffusion models. Diagnostics 12 (7) 1631. (10.3390/diagnostics12071631)
- Slator, P. et al. 2021. Combined diffusion-relaxometry microstructure imaging: Current status and future prospects. Magnetic Resonance in Medicine 86 (6), pp.2987-3011. (10.1002/mrm.28963)
- Slator, P. et al. 2019. Placenta Imaging Workshop 2018 report: Multiscale and multimodal approaches. Placenta 79 , pp.78-82. (10.1016/j.placenta.2018.10.010)
- Slator, P. et al. 2019. Combined diffusion-relaxometry MRI to identify dysfunction in the human placenta. Magnetic Resonance in Medicine 82 (1), pp.95-106. (10.1002/mrm.27733)
- Slator, P. J. and Burroughs, N. J. 2018. A hidden Markov model for detecting confinement in single-particle tracking trajectories. Biophysical Journal 115 (9), pp.1741-1754. (10.1016/j.bpj.2018.09.005)
- Slator, P. J. et al. 2023. Non-invasive mapping of human placenta microenvironments throughout pregnancy with diffusion-relaxation MRI. Placenta 144 , pp.29-37. (10.1016/j.placenta.2023.11.002)
- Slator, P. J. et al. 2017. Placenta microstructure and microcirculation imaging with diffusion MRI. Magnetic Resonance in Medicine 80 (2), pp.756-766. (10.1002/mrm.27036)
- Slator, P. J. et al. 2025. Low field combined diffusion-relaxation MRI for mapping placenta structure and function. Placenta 172 , pp.73-82. (10.1016/j.placenta.2025.10.014)
- Slator, P. , Cairo, C. and Burroughs, N. 2015. Detection of diffusion heterogeneity in single particle tracking trajectories using a hidden Markov Model with measurement noise propagation.. PLOS ONE (10.1371/journal.pone.0140759)
- Slator, P. et al. 2021. Data-driven multi-contrast spectral microstructure imaging with InSpect: INtegrated SPECTral component estimation and mapping. Medical Image Analysis 71 102045. (10.1016/j.media.2021.102045)
Book sections
- Slator, P. J. et al. 2021. Anisotropy in the human placenta in pregnancies complicated by fetal growth restriction. In: Özarslan, E. et al., Anisotropy Across Fields and Scales. Mathematics and Visualization Springer. , pp.263–276. (10.1007/978-3-030-56215-1_13)
- Slator, P. J. et al. 2018. IVIM MRI of the Placenta. In: Le Bihan, D. et al., Intravoxel Incoherent Motion (IVIM) MRI. New York: Pan Stanford
Conferences
- Blumberg, S. B. , Slator, P. J. and Alexander, D. C. 2024. Experimental design for multi-channel imaging via task-driven feature selection. Presented at: The International Conference on Learning Representations (ICLR) 2024 Vienna, Austria 7-11 May 2024. Proceedings of 12th International Conference on Learning Representations. ICLR.
- Hutter, J. et al., 2017. Dynamic field mapping and motion correction using interleaved double spin-echo diffusion MRI. Presented at: International Conference on Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2017 Quebec City 11 - 13 September 2017. Medical Image Computing and Computer Assisted Intervention − MICCAI 2017. Vol. 10433.Springer International Publishing AG. , pp.523-531. (10.1007/978-3-319-66182-7_60)
- Lin, H. et al., 2021. Generalised super resolution for quantitative MRI using self-supervised mixture of experts. Presented at: International Conference on Medical Image Computing and Computer-Assisted Intervention Strasbourg September 27–October 1, 2021. Published in: de Bruijne, M. et al., Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. Vol. 12906.Lecture Notes in Computer Science Cham, Switzerland: Springer. , pp.44-54. (10.1007/978-3-030-87231-1_5)
- Pizzolato, M. et al., 2020. Acquiring and predicting multidimensional diffusion (MUDI) data: an open challenge. Presented at: MICCAI Workshop Shenzhen, China Oct 2019. Published in: Bonet-Carne, E. et al., Computational Diffusion MRI. Mathematics and Visualization Springer. , pp.195-208. (10.1007/978-3-030-52893-5_17)
- Powell, E. et al., 2021. Generalised hierarchical bayesian microstructure modelling for diffusion MRI. Presented at: International Workshop on Computational Diffusion MRI 01 October 2021. Published in: Cetin-Karayumak, S. ed. Computational Diffusion MRI. CDMRI 2021. Vol. 13006.Lecture Notes in Computer Science Springer Nature. , pp.36–47. (10.1007/978-3-030-87615-9_4)
- Slator, P. et al. 2019. InSpect: INtegrated SPECTral component estimation and mapping for multi-contrast microstructural MRI. Presented at: IPMI 2019: International Conference on Information Processing in Medical Imaging 2-7 June 2019. Published in: Chung, A. C. S. et al., Information Processing in Medical Imaging: 26th International Conference, IPMI 2019, Hong Kong, China, June 2–7, 2019, Proceedings. Springer. , pp.755-766. (10.1007/978-3-030-20351-1_59)
- Slator, P. J. et al. 2019. A framework for calculating time-efficient diffusion MRI protocols for anisotropic IVIM and an application in the placenta. Presented at: MICCAI 2018 Granada, Spain 16-20 September 2018. Published in: Bonet-Carne, E. et al., Computational Diffusion MRI: International MICCAI Workshop, Granada, Spain, September 2018. Mathematics and Visualization Springer, Cham. , pp.251-263. (10.1007/978-3-030-05831-9_20)
- Slator, P. J. et al. 2020. Data-driven multi-contrast spectral microstructure imaging with InSpect. Presented at: MICCAI: International Conference on Medical Image Computing and Computer-Assisted Intervention Lima, Peru 4–8 October, 2020. Published in: Martel, A. et al., Medical Image Computing and Computer Assisted Intervention – MICCAI 2020.. Vol. 12266.Cham: Springer. , pp.375-385. (10.1007/978-3-030-59725-2_36)
Websites
Research
My overarching aim is to positively impact patient care by delivering new medical image computing techniques that enable improved diagnosis, prognosis and monitoring of disease. I am active in a wide range of application areas. I have worked extensively on placental MRI for assessment of pregnancy complications such as fetal growth restriction and pre-eclampsia, and have also worked on brain and prostate imaging.
My work uses a range of mathematical and computational tools, including biophysical modelling and machine learning. I work with multiple quantitative MRI techniques including diffusion and relaxometry. Recently, I have focused on approaches that can improve quantification of tissue features by simultaneously measuring multiple MRI properties, and data-driven unsupervised learning tools for analysing such images.
Funding
- Development and pilot evaluation of an artificial intelligence-based rhythmic auditory stimulation system for personalized train of finger movements in Parkinson's and Huntington's disease (DRUM-AI) [co-principal investigator]
- Jacques und Gloria Gossweiler Foundation Neurology Research Grant, £310,562
- Multi-model Studies to Understand Pregnancy and Prevent Stillbirth [co-principal investigator]
- Wellcome Leap In Utero Program, $3,500,000
- Development of a Multidimensional Microstructural MRI Method for Non-Invasive Skeletal Muscle Fibre Type Assessment [principal investigator]
- Royal Society International Exchanges, £10,728
- Assessing Placental Structure and Function by Unified Fluid Mechanical Modelling and in-vivo MRI [researcher co-investigator, lead grant writer]
- EPSRC Standard Grant, £1,124,022
Supervision and Mentorship
Academic Staff:
- Stefano Zappala, Cardiff University 2024 –
Postdoctoral Researchers:
- ZhuangJian Yang, UCL 2024 –
- Diana Marta Cruz De Oliveira, UCL 2022 –
PhD Students:
- Finnlay Gough, Cardiff University 2025 –
- Yahia Khubrani, Cardiff University 2023 –
- Snigdha Sen, UCL 2022 – 2025
Biography
Employment
2023-present: Lecturer, School of Computer Science and Informatics, Cardiff University.
2020-2023: Senior Research Fellow, Centre for Medical Image Computing, University College London.
2016-2020: Research Associate, Centre for Medical Image Computing, University College London.
2016: Research Assistant, Systems Biology Centre, University of Warwick.
Education
2011-2015: MSc + PhD Systems Biology, Systems Biology Centre, University of Warwick.
2007-2011: BSc Mathematics, University of Edinburgh.
Honours and awards
2022: Higher Education Academy Fellowship
2019, 2022: Magnetic Resonance in Medicine distinguished reviewer
2019, 2021: Magna cum laude award at International Society of Magnetic Resonance in Medicine (ISMRM) annual meeting
2017: Harold Fox new investigator award at International Federation of Placenta Associations (IFPA) Meeting
Professional memberships
2023-present: EPSRC peer review college member
Committees and reviewing
Peer reviewer for multiple journals:
- Magnetic Resonance in Medicine
- Medical Image Analysis
- NMR in Biomedicine
- Placenta
- IEEE Transactions on Medical Imaging
- Journal of Maternal-Fetal & Neonatal Medicine
- Physical Biology
- NeuroImage
- Journal of Magnetic Resonance
- Frontiers in Physics
- The Journal of Machine Learning for Biomedical Imaging (MELBA)
- International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)
- Brain Communications
- European Radiology
Contact Details
[email protected]
Cardiff University Brain Research Imaging Centre, Room 1.014, Maindy Road, Cardiff, CF24 4HQ
Cardiff University Brain Research Imaging Centre, Room 1.014, Maindy Road, Cardiff, CF24 4HQ
Research themes
Specialisms
- Biomedical imaging
- Computational imaging
- Modelling and simulation