Attenuation correction without structural images for PET imaging

Abstract

Deriving accurate attenuation correction factors for whole-body positron emission tomography (PET) images is challenging due to issues such as truncation, inter-scan motion, and erroneous transformation of structural voxelintensities to PET μ-map values. In this work, we proposed a deep-learning-based attenuation correction (DL-AC) method to derive the nonlinear mapping between attenuation corrected PET (AC PET) and non-attenuation corrected PET (NAC PET) images for whole-body PET imaging. A 3D cycle-consistent generative adversarial networks (cycle GAN) framework was employed to synthesize AC PET from NAC PET. The method learns a transformation that minimizes the difference between DL-AC PET, generated from NAC PET, and AC PET images. It also learns an inverse transformation such that cycle NAC PET image generated from the DL-AC PET is close to real NAC PET image. Both transformation network architectures are implemented by a residual network and outputs are judged by a fully convolutional network. A retrospective study was performed on 23 sets of whole-body PET/CT with leave-one-out cross validation. The proposed DL-AC method obtained the average mean error and normalized mean square error of the whole-body of -0.01%±2.91% and 1.21%±1.73%. We proposed a deep-learning-based approach to perform wholebody PET attenuation correction from NAC PET. The method demonstrates excellent quantification accuracy and reliability.