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Dr Paddy Slator
(e/fe)
- Ar gael fel goruchwyliwr ôl-raddedig
Timau a rolau for Paddy Slator
Uwch Ddarlithydd
Trosolwyg
Ymunais â Phrifysgol Caerdydd fel Darlithydd yn 2023. Rwy'n gweithio yn Ysgol y Gwyddorau Cyfrifiadurol a Mathemategol (COMAT) a Chanolfan Delweddu Ymchwil yr Ymennydd Prifysgol Caerdydd (CUBRIC).
Nod fy ymchwil yw darparu technegau delweddu sy'n galluogi gwell diagnosis, prognosis a monitro clefyd ac felly'n cael effaith gadarnhaol ar ofal cleifion. Rwy'n datblygu dulliau dadansoddi a chaffael delweddu cyseiniant magnetig (MRI) sy'n gallu nodweddu strwythur a swyddogaeth meinwe in-vivo. Rwy'n defnyddio ystod o ddysgu peiriannau, ystadegau Bayesian a dulliau modelu bioffisegol i ddatblygu technegau cyfrifiadura delweddau meddygol newydd.
Rwy'n cyd-drefnu cyfarfodydd MicroPhysics yn CUBRIC, lle mae ein prif ffocws ar ddatblygu technegau delweddu microstrwythur, a chyfarfodydd Grŵp Cyfrifiadura Delweddau Meddygol yn COMAT. Yn ogystal, rwy'n aelod gweithgar o'r Adran Cyfrifiadura Gweledol o fewn COMAT.
Cyhoeddiad
2026
- Sen, S. et al., 2026. microTorch: A software package for fast and flexible self-supervised diffusion MRI model fitting. Journal of Open Research Software 14 (1) 59. (10.5334/jors.736)
- Yang, Z. et al., 2026. Placental blood-flow velocity quantification from diffusion MRI. Magnetic Resonance in Medicine (10.1002/mrm.70551)
- Powell, E. et al., 2026. Hierarchical Bayesian modelling improves microstructural parameter mapping in diffusion and exchange MRI data. NMR in Biomedicine 39 (6) e70277. (10.1002/nbm.70277)
2025
- 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)
- 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)
2024
- 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)
- 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)
- 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)
- 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. Published in: Kim, B. et al., Proceedings of 12th International Conference on Learning Representations. ICLR. , pp.39998-40024.
2023
- 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)
- 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)
- 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.
- 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)
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
- 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)
- Powell, E. et al., 2021. Generalised hierarchical bayesian microstructure modelling for diffusion MRI. Presented at: International Workshop on Computational Diffusion MRI Strasbourg, France (Virtual) 1 October 2021. Published in: Cetin-Karayumak, S. ed. Computational Diffusion MRI. CDMRI 2021. Vol. 13006.Lecture Notes in Computer Science Springer. , pp.36-47. (10.1007/978-3-030-87615-9_4)
- 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 27 September – 1 October 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)
- 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)
- 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)
- 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)
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)
- 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)
- 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. 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 Vol. 1. Springer, Cham. , pp.251-263. (10.1007/978-3-030-05831-9_20)
- 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 Hong Kong 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. et al. 2019. Placenta Imaging Workshop 2018 report: Multiscale and multimodal approaches. Placenta 79 , pp.78-82. (10.1016/j.placenta.2018.10.010)
2018
- 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
- 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)
- 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)
- 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)
2017
- 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)
- 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)
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)
Adrannau llyfrau
- 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
Cynadleddau
- 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. Published in: Kim, B. et al., Proceedings of 12th International Conference on Learning Representations. ICLR. , pp.39998-40024.
- Powell, E. et al., 2021. Generalised hierarchical bayesian microstructure modelling for diffusion MRI. Presented at: International Workshop on Computational Diffusion MRI Strasbourg, France (Virtual) 1 October 2021. Published in: Cetin-Karayumak, S. ed. Computational Diffusion MRI. CDMRI 2021. Vol. 13006.Lecture Notes in Computer Science Springer. , pp.36-47. (10.1007/978-3-030-87615-9_4)
- 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 27 September – 1 October 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)
- 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)
- 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 Vol. 1. Springer, Cham. , pp.251-263. (10.1007/978-3-030-05831-9_20)
- 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 Hong Kong 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)
- 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)
Erthyglau
- Sen, S. et al., 2026. microTorch: A software package for fast and flexible self-supervised diffusion MRI model fitting. Journal of Open Research Software 14 (1) 59. (10.5334/jors.736)
- Yang, Z. et al., 2026. Placental blood-flow velocity quantification from diffusion MRI. Magnetic Resonance in Medicine (10.1002/mrm.70551)
- Powell, E. et al., 2026. Hierarchical Bayesian modelling improves microstructural parameter mapping in diffusion and exchange MRI data. NMR in Biomedicine 39 (6) e70277. (10.1002/nbm.70277)
- 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)
- 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)
- 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)
- 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)
- 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)
- 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)
- 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)
- 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)
- 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. 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)
- 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)
- 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)
- Christiaens, D. et al., 2019. In utero diffusion MRI. Topics in Magnetic Resonance Imaging 28 (5), pp.255-264. (10.1097/RMR.0000000000000211)
- 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)
- 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. 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)
- 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)
- 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)
- 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)
- 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)
Gwefannau
Ymchwil
Fy nod cyffredinol yw effeithio'n gadarnhaol ar ofal cleifion trwy ddarparu technegau cyfrifiadura delweddau meddygol newydd sy'n galluogi gwell diagnosis, prognosis a monitro clefyd. Rwy'n weithgar mewn ystod eang o feysydd cymhwyso. Rwyf wedi gweithio'n helaeth ar MRI placental ar gyfer asesu cymhlethdodau beichiogrwydd fel cyfyngiad twf y ffetws a pre-eclampsia, ac rwyf hefyd wedi gweithio ar ddelweddu'r ymennydd a'r prostad .
Mae fy ngwaith yn defnyddio ystod o offer mathemategol a chyfrifiadurol, gan gynnwys modelu bioffisegol a dysgu peiriannau. Rwy'n gweithio gyda thechnegau MRI meintiol lluosog gan gynnwys trylediad a relaxometry. Yn ddiweddar, rwyf wedi canolbwyntio ar ddulliau a all wella meintioli nodweddion meinwe trwy fesur priodweddau MRI lluosog ar yr un pryd, ac offer dysgu heb oruchwyliaeth wedi'u gyrru gan ddata ar gyfer dadansoddi delweddau o'r fath.
Cyllid
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MRI Ymlacio Trylediad Cyflym gydag AI ar gyfer Graddio Canser y Prostad ar Gryfder Graddiant Ultra-Uchel [prif ymchwilydd]
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Ymchwil Canser Cymru, £110,000
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- Datblygu a gwerthusiad peilot o system ysgogi clywedol rhythmig sy'n seiliedig ar ddeallusrwydd artiffisial ar gyfer trên personol o symudiadau bysedd mewn clefyd Parkinson a Huntington (DRUM-AI) [cyd-brif ymchwilydd]
- Grant Ymchwil Niwroleg Sefydliad Jacques und Gloria Gossweiler, £310,562
- Astudiaethau aml-fodel i ddeall beichiogrwydd ac atal marw-enedigaeth [cyd-brif ymchwilydd]
- Rhaglen Wellcome Leap In Utero, $ 3,500,000
- Datblygu Dull MRI Microstrwythurol Aml-ddimensiwn ar gyfer Asesu Math o Ffibr Cyhyrau Ysgerbydol Anfewnwthiol [prif ymchwilydd]
- Cyfnewidfeydd Rhyngwladol y Gymdeithas Frenhinol, £10,728
- Asesu Strwythur a Swyddogaeth Placental trwy Fodelu Mecanyddol Hylif Unedig ac MRI in-vivo [ymchwilydd, cyd-ymchwilydd, prif awdur grant]
- Grant Safonol EPSRC, £1,124,022
Goruchwylio a Mentora
Ymchwilwyr ôl-ddoethurol:
- Victor Navarro, Prifysgol Caerdydd 2025 –
- ZhuangJian Yang, UCL 2024 -
- Diana Marta Cruz De Oliveira, UCL 2022 - 2026
Myfyrwyr PhD:
- Finnlay Gough, Prifysgol Caerdydd 2025 –
- Yahia Khubrani, Prifysgol Caerdydd 2023 –
- Snigdha Sen, UCL 2022 - 2025
Staff Academaidd:
- Stefano Zappala, Prifysgol Caerdydd 2024 –
Bywgraffiad
Cyflogaeth
2023-presennol: Darlithydd, Ysgol Cyfrifiadureg a Gwybodeg, Prifysgol Caerdydd.
2020-2023: Uwch Gymrawd Ymchwil, Canolfan Cyfrifiadura Delweddau Meddygol, Coleg Prifysgol Llundain.
2016-2020: Cyswllt Ymchwil, Canolfan Cyfrifiadura Delweddau Meddygol, Coleg Prifysgol Llundain.
2016: Cynorthwy-ydd Ymchwil, Canolfan Bioleg Systemau, Prifysgol Warwick.
Addysg
2011-2015: MSc + PhD Systemau Bioleg, Canolfan Bioleg Systemau, Prifysgol Warwick.
2007-2011: BSc Mathemateg, Prifysgol Caeredin.
Anrhydeddau a dyfarniadau
2022: Cymrodoriaeth yr Academi Addysg Uwch
2019, 2022: Adolygydd nodedig Magnetic Resonance in Medicine
2019, 2021: Gwobr Magna cum laude yng nghyfarfod blynyddol Cymdeithas Ryngwladol Cyseiniant Magnetig mewn Meddygaeth (ISMRM)
2017: Gwobr Ymchwilydd Newydd Harold Fox yng Nghyfarfod Ffederasiwn Rhyngwladol Cymdeithasau Placenta (IFPA)
Aelodaethau proffesiynol
2023-presennol: Aelod o'r coleg adolygu cymheiriaid EPSRC
Pwyllgorau ac adolygu
Adolygydd cymheiriaid ar gyfer cyfnodolion lluosog:
- Cyseiniant Magnetig mewn Meddygaeth
- Dadansoddi Delweddau Meddygol
- NMR mewn Biofeddygaeth
- Brych
- Trafodion IEEE ar Ddelweddu Meddygol
- Cyfnodolyn Meddygaeth Mamol-Ffetws a Newyddenedigol
- Bioleg Ffisegol
- NiwroDdelwedd
- Cyfnodolyn Cyseiniant Magnetig
- Ffiniau mewn Ffiseg
- The Journal of Machine Learning for Biomedical Imaging (MELBA)
- Cynhadledd Ryngwladol ar Gyfrifiadura Delweddau Meddygol ac Ymyrraeth â Chymorth Cyfrifiadur (MICCAI)
- Cyfathrebu'r Ymennydd
- Radioleg Ewropeaidd
Contact Details
[email protected]
Canolfan Ymchwil Delweddu'r Ymennydd Prifysgol Caerdydd, Ystafell 1.014, Heol Maendy, Caerdydd, CF24 4HQ
Canolfan Ymchwil Delweddu'r Ymennydd Prifysgol Caerdydd, Ystafell 1.014, Heol Maendy, Caerdydd, CF24 4HQ
Themâu ymchwil
Arbenigeddau
- Delweddu biofeddygol
- Delweddu cyfrifiadurol
- Modelu ac efelychu