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Abstract #0694

Deep Learning based pseudo-CT estimation using ZTE and Dixon MR images for PET attenuation correction

Sandeep Kaushik1, Dattesh Shanbhag1, Andrew Leynes2, Hariharan Ravishankar1, Jaewon Yang2, Peder Larson2, Thomas Hope2, and Florian Wiesinger3

1GE Global Research, Bangalore, India, 2Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States, 3GE Global Research, Garching b. Munchen, Germany

Simultaneous PET/MR is now being adapted for clinical studies. Earlier methods of PET/MR-AC have considered all bones as a single entity irrespective of their density. In this work, we demonstrate using ZTE and Dixon LAVA-Flex MRI data, and deep learning framework, a continuous density pseudo-CT (pCT) image which combines soft tissue pCT (from Dixon LAVA-Flex) with a continuous density bone pCT from ZTE.

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