Dr Stephen Paisey

2025

• Warren, F. et al. 2025. A feature-driven acquisition strategy using scale-invariant descriptors for deep active learning in preclinical CT segmentation. Presented at: 29th Medical Image Understanding and Analysis Conference Leeds, UK 15-17 July 2025. Published in: Ali, S. , Hogg, D. C. and Peckham, M. eds. Proceedings of MIUA. Vol. 15918.Annual Conference on Medical Image Understanding and Analysis Springer. , pp.129-145. (10.1007/978-3-031-98694-9_10)
• Paisey, S. J. et al. 2025. Imaging of human stem cell-derived dopamine grafts correlates with behavioural recovery and reveals microstructural brain changes. Neurobiology of Disease 209 106910. (10.1016/j.nbd.2025.106910)

2023

• Cattaneo, M. et al., 2023. The longevity-associated BPIFB4 gene supports cardiac function and vascularization in ageing cardiomyopathy. Cardiovascular Research 119 (7), pp.1583-1595. (10.1093/cvr/cvad008)

2022

• Knight, R. et al. 2022. Oral progenitor cell line-derived small extracellular vesicles as a treatment for preferential wound healing outcome. Stem Cells Translational Medicine 11 (8), pp.861-875. (10.1093/stcltm/szac037)

2021

• Exner, R. M. et al., 2021. Explorations into peptide nucleic acid contrast agents as emerging scaffolds for breakthrough solutions in medical imaging and diagnosis. ACS Omega 6 (43), pp.28455-28462. (10.1021/acsomega.1c03994)
• Alsyed, E. et al. 2021. A self organizing map for exploratory analysis of PET radiomic features. Presented at: 2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) Boston, MA, USA 31 October -7 November 2020. 2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). IEEE. (10.1109/NSS/MIC42677.2020.9507846)
• Chang, H. et al., 2021. Deep learning image transformation under radon transform. Presented at: 2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) Boston, MA, USA 31 October -7 November 2020. 2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). IEEE. , pp.1-3. (10.1109/NSS/MIC42677.2020.9507793)

2020

• Fenwick, A. J. et al. 2020. Absolute standardisation and determination of the half-life and gamma emission intensities of 89Zr. Applied Radiation and Isotopes 166 109294. (10.1016/j.apradiso.2020.109294)
• Cavaliere, A. et al. 2020. Radiosynthesis of [18F]-labelled pro-nucleotides (ProTides). Molecules 25 (3) 704. (10.3390/molecules25030704)

2019

• Alsyed, E. et al. 2019. The statistical influence of imaging time and segmentation volume on PET radiomic features: A preclinical study. Presented at: 2019 IEEE NSS-MIC Manchester, UK 26 October - 2 November 2019. Proceedings of the IEEE Nuclear Science Symposium and Medical Imaging Conference. IEEE. , pp.1-4. (10.1109/NSS/MIC42101.2019.9059863)
• Watson, H. A. et al. 2019. L-selectin enhanced T cells improve the efficacy of cancer immunotherapy. Frontiers in Immunology 10 1321. (10.3389/fimmu.2019.01321)
• Smith, R. L. et al. 2019. Reinforcement learning for object detection in PET imaging. Presented at: 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) Manchester, England 26 October - 02 November 2019. 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). IEEE. , pp.1-4. (10.1109/NSS/MIC42101.2019.9060031)

2017

• Trueman, R. C. et al., 2017. Systematic and detailed analysis of behavioural tests in the rat middle cerebral artery occlusion model of stroke: tests for long-term assessment. Journal of Cerebral Blood Flow and Metabolism 37 (4), pp.1349-1361. (10.1177/0271678X16654921)

2016

• Hughes, E. et al., 2016. Treating metastatic disease through manipulation of regulatory T cells. European Journal of Immunology 46 , pp.578-578. 827. (10.1002/eji.201670200)
• Knight, J. C. et al., 2016. Scaling-down antibody radiolabeling reactions with zirconium-89. Dalton Transactions 45 (15), pp.6343-6347. (10.1039/C5DT04774A)

2015

• Dabkowski, A. M. et al. 2015. Optimization of cyclotron production for radiometal of Zirconium 89. Acta Physica Polonica Series A 127 (5), pp.1479-1482. (10.12693/APhysPolA.127.1479)
• Koehoorn, J. et al., 2015. Automated digital hair removal by threshold decomposition and morphological analysis. Presented at: International Symposium on Mathematical Morphology and Its Applications to Signal and Image Processing (ISMM 2015) Reykjavik, Iceland 27-29 May 2015. Published in: Benediktsson, J. A. et al., Mathematical Morphology and Its Applications to Signal and Image Processing. Vol. 9082.Lecture Notes in Computer Science (LNCS) Springer. , pp.15-26. (10.1007/978-3-319-18720-4_2)

2014

• Evans, B. A. J. et al. 2014. Preclinical assessment of a new magnetic resonance-based technique for determining bone quality by characterization of trabecular microarchitecture. Calcified Tissue International 95 (6), pp.506-520. (10.1007/s00223-014-9922-z)

2012

• Kalogerou, M. et al. 2012. T2 weighted MRI for assessing renal lesions in transgenic mouse models of tuberous sclerosis. European Journal of Radiology 81 (9), pp.2069-2074. (10.1016/j.ejrad.2011.06.054)

2011

• Knight, J. C. , Edwards, P. G. and Paisey, S. J. 2011. Fluorinated contrast agents for magnetic resonance imaging; a review of recent developments. RSC Advances 1 (8), pp.1415-1425. (10.1039/c1ra00627d)
• Knight, J. C. et al. 2011. Evaluation of a fluorescent derivative of AMD3100 and its interaction with the CXCR4 chemokine receptor. ChemBioChem 12 (17), pp.2692-2698. (10.1002/cbic.201100441)