PET attenuation correction using non-AC PET-based synthetic CT

The accuracy of attenuation correction on whole-body PET images is subject to inter-scan motion, image artifacts such as truncation and distortion, and erroneous transformation of structural voxel-intensities to PET mu-map values. We proposed a deep-learning-based method to derive synthetic CT (sCT) images from non-attenuation corrected PET (NAC PET) images for AC on whole-body PET imaging. We utilized a 3D cycle-consistent generative adversarial networks (CycleGAN) to synthesize CT images from NAC PET. The model learns a transformation that minimizes the difference between sCT, generated from NAC PET, and true CT. It also learns an inverse transformation such that cycle NAC PET image generated from the sCT is close to true NAC PET image. Both generators are implemented by a fully convolutional attention network (FCAN), and followed by a discriminator which is structured as a fully convolutional network. A retrospective study was performed with a total of 60 sets of whole-body PET/CT, 40 sets for training and 20 sets for testing. The sCT images generated with proposed method show great contrast on lung, soft tissue and bony structures. The mean absolute error of sCT over true CT is less than 110 HU. Using sCT for whole-body PET AC, the mean error of PET quantification is less than 1% and normalized mean square error is less than 1.4%. We proposed a deep learning-based approach to generate synthetic CT from whole-body NAC PET for PET AC, which demonstrates excellent synthetic CT estimation accuracy and PET quantification accuracy.