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Abstract #1841

Automatic Extraction of Liver Parenchyma and R2* Estimation using Deep Learning for UTE Imaging for Assessment of Hepatic Iron Overload

Utsav Shrestha1, Cara Morin2,3, Ralf Loeffler4, Zachary R. Abramson2, Jane Hankins2, Claudia Hillenbrand4, and Aaryani Tipirneni-Sajja1,2
1The University of Memphis, Memphis, TN, United States, 2St. Jude Children's Research Hospital, Memphis, TN, United States, 3Cincinnati Children’s Hospital Medical Center, CINCINNATI, OH, United States, 4University of New South Wales, Sydney, Australia

Synopsis

Keywords: Liver, Liver, Deep Learning, Vessel Segmentation, R2*, HICUltra-short echo time (UTE) imaging increases the accuracy of R2*-based hepatic iron content (HIC) quantification in cases of high iron overload when conventional GRE sequences can fail due to rapid signal decay. Segmenting whole liver to estimate liver R2* requires human expert and is time consuming. In this study, we trained a convolutional neural network (CNN) to automatically segment the liver parenchyma on radial UTE acquisitions using magnitude images and R2* maps. Our results show an excellent agreement between manual and CNN-based liver segmentation and mean R2* values, hence demonstrating the potential of our proposed method for automated HIC assessment.

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Keywords