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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