Dr Paddy Slator

2024

• Hall, M., Suff, N., Slator, P., Rutherford, M., Shennan, A., Hutter, J. and Story, L. 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., Thomas, D., Slator, P., White, R. D. and Farnell, D. J. 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, article number: 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), article number: e1012470. (10.1371/journal.pcbi.1012470)
• Cromb, D., Slator, P. J., Hall, M., Price, A., Alexander, D. C., Counsell, S. J. and Hutter, J. 2024. Advanced magnetic resonance imaging detects altered placental development in pregnancies affected by congenital heart disease. Scientific Reports 14(1), article number: 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.

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, article number: 103483. (10.1016/j.nicl.2023.103483)
• Slator, P. J., Aviles, J. A., Tomi-Tricot, R., Hajnal, J. V., Alexander, D. C. and Hutter, J. 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., Slator, P. J., Avena Zampieri, C., Hall, M., Rutherford, M. and Story, L. 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., Blumberg, S. B., Narayan, N., Epstein, S. C., Alexander, D. C., Palombo, M. and Slator, P. J. 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), article number: 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., Battocchio, M., Parker, C. S. and Slator, P. J. 2021. Generalised hierarchical bayesian microstructure modelling for diffusion MRI. Presented at: International Workshop on Computational Diffusion MRI, 01 October 2021 Presented at 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)
• Lin, H., Zhou, Y., Slator, P. J. and Alexander, D. C. 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 Presented at de Bruijne, M. et al. eds.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, article number: 102045. (10.1016/j.media.2021.102045)
• Hutter, J., Ho, A., Jackson, L. H., Slator, P. J., Chappell, L. C., Hajnal, J. V. and Rutherford, M. A. 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. eds. 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 Presented at Bonet-Carne, E. et al. eds.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 Presented at Martel, A. et al. eds.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., Hutter, J., Ianus, A., Panagiotaki, E., Rutherford, M. A., Hajnal, J. V. and Alexander, D. C. 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 Presented at Bonet-Carne, E. et al. eds.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. 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 Presented at Chung, A. C. S. et al. eds.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., Hutter, J., Pangiotaki, E., Rutherford, M. A., Hajnal, J. V. and Alexander, D. C. 2018. IVIM MRI of the Placenta. In: Le Bihan, D. et al. eds. 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, article number: 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 2017Medical 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)