Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
366-05-007 ISMRM Abstract

Repeatability of single-kidney parenchymal shape and size in healthy volunteers

Primary: Body - Kidney
Secondary: Analysis Methods - Radiomics
366-05-007 · Quantitative Applications in Body MRI: Make the Numbers Count · Monday, 11 May, 4:10 PM–5:05 PM · Digital Posters Row G
Keywords: Radiomics Repeatability Deep Learning Segmentation Kidney MRI Dixon-MRI
Accepted
Joao Periquito 1,2, Virva Saunavaara3, Kanishka Sharma4, Kywe Soe1, Ajo Thomas1, Bashair Alhummiany5, Jonathan Fulford6, David Shelley5, Angela Shore6,7, Nicolas Grenier8, Loreto Gesualdo9, Paola Pontrelli9, Francesca Conserva9, Niina Koivuviita3, Maria Gomez10, Kim Gooding6,7, Steven Sourbron1
1POLARIS, Division of Clinical Medicine, School of Medicine & Population Health, The University of Sheffield, Sheffield, United Kingdom
2Institute for Systems and Robotics – Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
3Department of Medicine, Turku University Hospital and University of Turku, Turku, Finland
4Antaros Medical, Gothenburg, Sweden
5University of Leeds, Leeds, United Kingdom
6University of Exeter Medical School, Exeter, United Kingdom
7NIHR Exeter Clinical Research Facility, Royal Devon University Healthcare NHS Foundation Trust, Exeter, United Kingdom
8Université de Bordeaux, Bordeaux, France
9Università degli Studi di Bari Aldo Moro, Italy
10Department of Clinical Sciences, Lund University Diabetes Centre, Malmö, Sweden, Sweden
Presenting Author: Joao Periquito

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References

1. Gooding, Kim M et al. “Prognostic imaging biomarkers for diabetic kidney disease (iBEAt): study protocol.” BMC nephrology vol. 21,1 242. 29 Jun. 2020, doi:10.1186/s12882-020-01901-x [doi]
2. Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2), 203-211.
3. van Griethuysen, J. J. M., Fedorov, A., Parmar, C., Hosny, A., Aucoin, N., Narayan, V., Beets-Tan, R. G. H., Fillion-Robin, J. C., Pieper, S., Aerts, H. J. W. L. (2017). Computational Radiomics System to Decode the Radiographic Phenotype. Cancer Research, 77(21), e104–e107. https://doi.org/10.1158/0008-5472.CAN-17-0339 [doi]
4. Stéfan van der Walt, Johannes L. Schönberger, Juan Nunez-Iglesias, François Boulogne, Joshua D. Warner, Neil Yager, Emmanuelle Gouillart, Tony Yu, and the scikit-image contributors. scikit-image: Image processing in Python. PeerJ 2:e453 (2014) https://doi.org/10.7717/peerj.453 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/366-05-007