Publication

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

Gong K, Yang J, Kim K, El Fakhri G, Seo Y, Li Q.
Phys Med Biol. 2018 Jun 13;63(12):125011.

Three views of the PET reconstruction error images (PETpseudoCT – PETCT, unit: SUV) using the Dixon-Seg method (left column), the Dixon-atlas method (middle column) and the proposed Dixon-Unet method (right column).

Positron emission tomography (PET) is a functional imaging modality widely used in neuroscience studies. To obtain meaningful quantitative results from PET 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 this issue, we present deep neural network methods to derive the continuous attenuation coefficients for brain PET imaging from MR images. With only Dixon MR images as the network input, the existing U-net structure was adopted and analysis using forty patient data sets shows it is superior to other Dixon-based methods. When both Dixon and zero echo time (ZTE) images are available, we have proposed a modified U-net structure, named GroupU-net, to efficiently make use of both Dixon and ZTE information through group convolution modules when the network goes deeper. Quantitative analysis based on fourteen real patient data sets demonstrates that both network approaches can perform better than the standard methods, and the proposed network structure can further reduce the PET quantification error compared to the U-net structure.