Abstract
Monitoring the accumulation of renal sinus fat (RSF) by non-invasive magnetic resonance imaging (MRI) holds promise for assessing the risk of nephropathy in individuals with diabetes. Automatic image segmentation using dedicated U-Net models was deployed for accurate quantification of RSF content and renal parenchyma (RP) from different MRI protocols. Therefore, the accuracy of volumetric vs single-slice analysis for quantifying RP and RSF was assessed. Further, the resulting kidney structures obtained from a whole-body MR images acquired with partial kidney coverage were compared to high-resolution MRI protocol with full-kidney coverage, in people with and without diabetes. Quantification of kidney structures showed accurate estimates of both RP and RSF volume across people with different glycaemic status and imaging protocols. A systematic overestimation of the RSF-to-RP ratio was observed when using the conventional single-slice assessment, supporting the need for volumetric kidney analysis, particularly for small structures such as the RSF. Moreover, MR images with interslice-gaps were found to substantially underestimate RSF content, highlighting the need for careful evaluation and correction of estimates from small kidney structures when data are pooled from different MR imaging protocols. In summary, automatic image segmentation enabled us to determine differences in the precision of RSF content obtained using different methodological approaches and MRI sequences with different kidney coverage.
Data availability
Data of this study can be made available from the corresponding author upon request and subject to institutional approval.
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Acknowledgements
We would like to thank the staff of the GDS Study for their excellent support.
Funding
Open Access funding enabled and organized by Projekt DEAL. The research of F.C.M. and V.B.S–H. is supported by the EFSD and Boehringer Ingelheim European Research Programme on “Multi-System Challenges in Diabetes, Obesity and Cardiovascular Disease” 2024. The GDS was initiated and financed by the German Diabetes Center (DDZ), which is funded by the German Federal Ministry of Health (Berlin, Germany) and the Ministry of Culture and Science of the State of Northrhine-Westphalia (Düsseldorf, Germany) and from the German Federal Ministry of Education and Research (BMBF) to the German Center for Diabetes Research (DZD e. V.). The GDS is supported in part by funds of the German Federal Ministry of Education and Research (BMBF) to the German Center for Diabetes Research (DZD e. V.). The research of M.R. is supported by grants from the European Community (HORIZON-HLTH-2022-STAYHLTH-02–01: Panel A) to the INTERCEPT-T2D consortium, EUREKA Eurostars-2 (E!-113230-DIA-PEP), the Deutsche Forschungsgemeinschaft (DFG; SFB/CRC1116, RTG/GRK 2576), the Schmutzler-Stiftung, and by the programme “Profilbildung 2020”, an initiative of the Ministry of Culture and Science of the State of Northrhine-Westphalia and the Schmutzler Stiftung. The sole responsibility for the content of this publication lies with the authors.
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FCM, CM, RW, and VSH designed and supervised the study. FCM and RK analyzed the results. FCM, VSH, and RW drafted the original manuscript. MR, VSH, and RW provided the resources to conduct the study. KM and YK contributed to data acquisition. FCM and TM supervised and conducted the statistical analysis. All authors reviewed, edited, and approved the final version of the manuscript.
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Michelotti, F.C., Koshiba, R., Möser, C. et al. A comparison study to assess U-Net driven volumetric versus single-slice analysis and MRI sequences with different volume coverage to detect renal sinus fat in people with and without diabetes. Sci Rep (2026). https://doi.org/10.1038/s41598-025-33098-0
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DOI: https://doi.org/10.1038/s41598-025-33098-0
