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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 Lim2,5

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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