Attenuation correction for brain PET imaging using deep neural network based on dixon and ZTE MR images
This work addresses a critical challenge in quantitative PET/MR imaging for neuroscience, offering an incremental improvement over existing methods.
The authors tackled the problem of attenuation correction for brain PET imaging in PET/MR systems by developing deep neural network methods to derive attenuation coefficients from MR images, achieving superior performance over standard methods with a modified U-net structure reducing PET quantification error further.
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 than 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.