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Abstract #2722

Clinically applicable automatic quantitative renal perfusion measurement using ASL-MRI and machine learning

Isabell Katrin Bones1, Clemens Bos1, Chrit Moonen1, Jeroen Hendrikse2, and Marijn van Stralen1
1Center for Image Sciences, University Medical Center Utrecht, Utrecht, Netherlands, 2Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands

ASL-MRI quantification involves kidney segmentation and cortex-medulla differentiation to obtain cortical renal blood flow, requiring time consuming manual interaction hampering clinical adoption. We applied machine learning to automat renal ASL-MRI quantification. A cascade of three U-nets was constructed to replace manual segmentation steps. Automatic segmentation yielded a dice score of 0.78, which was similar to the inter-observer variability of 0.77. Moreover, good agreement for cortical RBF was found between automatic and manual segmentations on group and individual level; 211±31 and 208±31mL/min/100g, respectively. Our proposed method automates quantification without compromising performance. This makes renal ASL-MRI more attractive for clinical application.

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