Abstract: PET attenuation correction technology is of significant clinical importance,effectively reducing cancer misdiagnosis rates and enabling more precise treatment planning.However,traditional PET attenuation correction methods face challenges such as long scan times and errors introduced during post-processing,limiting their applicability.Recently,attenuation correction methods based on directly generating CT from PET have gained popularity in clinical settings due to shorter scan times and the advantage of avoiding post-processing errors.However,the substantial semantic differences and misalignment between PET and CT pose significant challenges for direct PET-to-CT attenuation methods in modal generation.To address this challenge,this paper proposes a PET attenuation correction method based on Cycle-S2SCT-Net.Cycle-S2SCT-Net utilizes a cyclic generative adversarial structure to learn the mapping between PET and CT distributions,facilitating semantic translation between the two modalities.Within a single generative adversarial network,Cycle-S2SCT-Net integrates an imaging slice continuity module to enhance the network's semantic alignment capability,thereby improving the continuity and accuracy of generated images.Addi-tionally,this paper introduces a network feature layer loss function(Layer Loss) to enhance the feature extraction capability of the generation network.The experimental results demonstrate that CT generated by Cycle-S2SCT-Net and its attenuation-corrected PET exhibit excellent performance in both quantitative evaluation metrics,such as peak signal-to-noise ratio(PSNR),structural similarity index(SSIM),root mean square error(RMSE),and visual quality.

Key words: PET,Attenuation correction methods,CT,Slice continuity,Cycle generative adversarial