Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
608-03-001 ISMRM Abstract

A Travelling Kidney and Repeatability Study using the harmonised UKRIN-MAPS multiparametric renal MRI protocol

Primary: Body - Kidney
Secondary: Acquisition & Reconstruction - Quantitative Imaging: relaxometry, multi-parametric, MR Fingerprinting, and synthetic MRI
608-03-001 · Quantitative Imaging: Multiparametric and Synthetic MRI · Thursday, 14 May, 4:00 PM–5:50 PM · Meeting Room 1.60
Keywords: Data Acquisition Precision & Accuracy Multivendor Kidney function Analysis of consistency
Accepted
Charlotte Buchanan1, Hao Li2, Alexander J Daniel1, Martin Craig1,3, Kevin Teh4, Iosif A Mendichovszky5,6, David L Thomas7,8,9, Steven Sourbron4, Andrew N Priest6,10, Susan Francis 1,3
1Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom
2Fudan University, Shanghai, China
3Nottingham Biomedical Research Centre, National Institute for Health Research, Nottingham, United Kingdom
4Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom
5Department of Radiology, Addenbrooke's Hospital, Cambridge, United Kingdom
6NIHR Cambridge Biomedical Research Centre, Cambridge, United Kingdom
7Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
8Department of Translational Neuroscience and Stroke, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
9Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
10Department of Radiology, University of Cambridge, Cambridge, United Kingdom
Presenting Author: Susan Francis

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References

1. UKRIN-MAPS (MRI Acquisition and Processing Standardisation) website: https://www.nottingham.ac.uk/research/groups/spmic/research/uk-renal-imaging-network/ukrin-maps.aspx
2. Mendichovszky et al., “Technical recommendations for clinical translation of renal MRI: a consensus project of the Cooperation in Science and Technology Action PARENCHIMA,” Magnetic Resonance Materials in Physics, Biology and Medicine, vol. 33, no. 1, 2020, doi: 10.1007/s10334-019-00784-w. [doi]
3. A. Dekkers et al., “Consensus-based technical recommendations for clinical translation of renal T1 and T2 mapping MRI,” Magnetic Resonance Materials in Physics, Biology and Medicine, vol. 33, no. 1, 2020, doi: 10.1007/s10334-019-00797-5. [doi]
4. O. Bane et al., “Consensus-based technical recommendations for clinical translation of renal BOLD MRI,” Magnetic Resonance Materials in Physics, Biology and Medicine, vol. 33, no. 1, 2020, doi: 10.1007/s10334-019-00802-x. [doi]
5. F. Nery et al., “Consensus-based technical recommendations for clinical translation of renal ASL MRI,” Magnetic Resonance Materials in Physics, Biology and Medicine, vol. 33, no. 1, 2020, doi: 10.1007/s10334-019-00800-z. [doi]
6. A. de Boer et al., “Consensus-Based Technical Recommendations for Clinical Translation of Renal Phase Contrast MRI,” Journal of Magnetic Resonance Imaging, vol. 55, no. 2, 2022, doi: 10.1002/jmri.27419. [doi]
7. A. Ljimani et al., “Consensus-based technical recommendations for clinical translation of renal diffusion-weighted MRI,” Magnetic Resonance Materials in Physics, Biology and Medicine, vol. 33, no. 1, pp. 177–195, Feb. 2020, doi: 10.1007/s10334-019-00790-y. [doi]
8. A. J. Daniel et al., “Automated renal segmentation in healthy and chronic kidney disease subjects using a convolutional neural network,” Magn Reson Med, vol. 86, no. 2, pp. 1125–1136, Aug. 2021, doi: 10.1002/mrm.28768. [doi]
9. Isensee, F., Jaeger, P. F., Kohl, S. A. A., Petersen, J. & Maier-Hein, K. H. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18, 203–211 (2021).
10. Cox EC, Gong Z, Craig M, Mohammadi-Nejad A-R, Auer D, Sotiropoulos S, Chen X, Francis S. Automated Analysis of Kidney MRI data in the UK Biobank. 5th Renal MRI meeting Ghent, 2023.
11. J. J. M. Van Griethuysen et al., “Computational radiomics system to decode the radiographic phenotype,” Cancer Res, vol. 77, no. 21, 2017, doi: 10.1158/0008-5472.CAN-17-0339. [doi]
12. H. Li et al., “Improvements in Between-Vendor MRI Harmonization of Renal T2 Mapping using Stimulated Echo Compensation,” Journal of Magnetic Resonance Imaging, 2024, doi: 10.1002/jmri.29282. [doi]
13. Application of Functional Renal MRI to improve assessment of chronic kidney disease (AFiRM) study website: https://www.uhdb.nhs.uk/afirm-study/

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/608-03-001