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Positron Emission Tomography (PET) is a functional imaging modality widelyused in neuroscience studies. To obtain meaningful quantitative results fromPET images, attenuation correction is necessary during image reconstruction.For PET/MR hybrid systems, PET attenuation is challenging as Magnetic Resonance(MR) images do not reflect attenuation coefficients directly. To address thisissue, we present deep neural network methods to derive the continuousattenuation coefficients for brain PET imaging from MR images. With only DixonMR images as the network input, the existing U-net structure was adopted andanalysis using forty patient data sets shows it is superior than other Dixonbased methods. When both Dixon and zero echo time (ZTE) images are available,we have proposed a modified U-net structure, named GroupU-net, to efficientlymake use of both Dixon and ZTE information through group convolution moduleswhen the network goes deeper. Quantitative analysis based on fourteen realpatient data sets demonstrates that both network approaches can perform betterthan the standard methods, and the proposed network structure can furtherreduce the PET quantification error compared to the U-net structure.

Attenuation correction for brain PET imaging using deep neural network based on dixon and ZTE MR images