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

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