Abstract #4318

Renal segmentation from non-contrast T1-weighted MR images

Nicole Wake¹, Jeremy C Lim², Artem Mikheev¹, Jas-mine Seah³, Elissa Botterill², Shawna Farquharson⁴, Henry Rusinek¹, and Ruth P Lim^2,5

¹Bernard and Irene Schwartz Center for Biomedical Imaging, Center for Advanced Imaging Innovation and Research, Department of Radiology, New York University School of Medicine, New York, NY, United States, ²Department of Radiology, Austin Health, Melbourne, Australia, ³Department of Endocrinology, Austin Health, Melbourne, Australia, ⁴Florey Neuroscience Institute, Melbourne, Australia, ⁵The University of Melbourne, Melbourne, Australia

A semi-automatic renal segmentation technique for non-contrast T1-weighted MR images was developed. Renal segmentation and volumetric analysis was tested in ten healthy volunteers and ten Type I diabetic patients. We found that this segmentation tool is fast, reliable, and requires minimal user interaction. Upon further validation, this method has clinical potential for monitoring renal status in appropriate patient populations.

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