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

TL;DR

This paper introduces deep neural network methods, including a modified U-net called GroupU-net, to improve attenuation correction in brain PET imaging using MR images, achieving higher accuracy than existing methods.

Contribution

The study develops a novel GroupU-net architecture that effectively combines Dixon and ZTE MR images for improved attenuation correction in PET, outperforming standard U-net and Dixon-based methods.

Findings

GroupU-net reduces PET quantification error more than standard U-net.

Both proposed networks outperform traditional Dixon-based attenuation correction methods.

Analysis on patient data confirms improved accuracy in PET imaging.

Abstract

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.