Synthetic CT Generation via Variant Invertible Network for All-digital Brain PET Attenuation Correction

TL;DR

This paper introduces a novel deep learning method using an invertible network to generate synthetic CT images from PET scans for attenuation correction, eliminating the need for anatomical imaging.

Contribution

It develops a bidirectional invertible network with variable augmentation for synthetic CT generation from PET images, improving brain PET attenuation correction accuracy.

Findings

Outperforms existing AC models like Cycle-GAN and Pix2pix

Demonstrates high accuracy in synthetic CT generation

Validates effectiveness on 1440 clinical datasets

Abstract

Attenuation correction (AC) is essential for the generation of artifact-free and quantitatively accurate positron emission tomography (PET) images. However, AC of PET faces challenges including inter-scan motion and erroneous transformation of structural voxel-intensities to PET attenuation-correction factors. Nowadays, the problem of AC for quantitative PET have been solved to a large extent after the commercial availability of devices combining PET with computed tomography (CT). Meanwhile, considering the feasibility of a deep learning approach for PET AC without anatomical imaging, this paper develops a PET AC method, which uses deep learning to generate continuously valued CT images from non-attenuation corrected PET images for AC on brain PET imaging. Specifically, an invertible network combined with the variable augmentation strategy that can achieve the bidirectional inference processes is proposed for synthetic CT generation (IVNAC). To evaluate the performance of the proposed algorithm, we conducted a comprehensive study on a total of 1440 data from 37 clinical patients using comparative algorithms (such as Cycle-GAN and Pix2pix). Perceptual analysis and quantitative evaluations illustrate that the invertible network for PET AC outperforms other existing AC models, which demonstrates the potential of the proposed method and the feasibility of achieving brain PET AC without CT.