Abstract #2869

Comparison of whole-body and abdominal MRI for the estimation of renal sinus fat in healthy volunteers and people with type 1 and type 2 diabetes

Filippo Carlo Michelotti^1,2, Rio Koshiba¹, Katharina Arens¹, Clara Möser^1,2,3, Yuliya Kupriyanova^1,2, Michael Roden^1,2,3, Robert Wagner^1,2, and Vera Schrauwen-Hinderling^1,2

¹Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research, Düsseldorf, Germany, ²German Center for Diabetes Research (DZD), München-Neuherberg, Germany, ³Department of Endocrinology and Diabetology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Düsseldorf, Germany

Synopsis

Keywords: Segmentation, Metabolism

Motivation: Whole-body MRI performed in large cohorts with interslice-gaps is not ideal to determine small organ-substructures such as the renal sinus fat (RSF). Thus, the accuracy of such estimates remains unclear.

Goal(s): To compare the estimates of RSF obtained by automatic segmentation of whole-body TSE images with interslice-gaps and continuous abdominal dual-echo GRE images.

Approach: Test the performance of U-Net models for RSF segmentation in both the external and internal sets and compare outcomes.

Results: U-Net models showed high accuracy in the detection of RSF volume. The estimates of RSF from whole-body TSE images were ~30% lower as compared to abdominal dual-echo GRE images.

Impact: Whole-body MRI with interslice-gaps can be used to accurately estimate renal sinus fat in people with type 2 diabetes, however, corrections of values based on more detailed, high-resolution MRI measurements are necessary.

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