Transfer learning-based attenuation correction for static and dynamic cardiac PET using a generative adversarial network

Abstract

Purpose Current attenuation correction (AC) of myocardial perfusion (MP) positron emission tomography (PET) remains challenging in routine clinical practice due to the propagation of CT-based artifacts and potential mismatch between PET and CT. The goal of this work is to demonstrate the feasibility of directly generating attenuation-corrected PET (AC PET) images from non-attenuation-corrected PET (NAC PET) images in the reconstruction domain for [13N]ammonia MP PET based on a generative adversarial network (GAN). Methods We recruited 60 patients who underwent rest [13N]ammonia cardiac PET/CT examinations. One static frame and twenty-one dynamic frames were acquired for each patient with both NAC PET and CT-based AC (CTAC) PET images. Paired 3D static or dynamic NAC and CTAC PET images were used as network inputs and labels for static (S-DLAC) and dynamic (D-DLAC) MP PET, respectively. In addition, the pre-trained S-DLAC network was fine-tuned by 3D paired dynamic NAC and CTAC PET frames for then AC in the dynamic PET images (D-DLAC-FT). Qualitative and quantitative assessments were implemented using CTAC PET as reference. Results The proposed S-DLAC, D-DLAC and D-DLAC-FT methods were qualitatively and quantitatively consistent with clinical CTAC. The S-DLAC showed a higher correlation with the reference static CTAC (S-CTAC) as compared to static NAC. The estimated kinetic parameters and blood volume fraction images from D-DLAC and D-DLAC-FT methods showed comparable performances with the reference dynamic CTAC (D-CTAC). D-DLAC-FT was slightly better than D-DLAC in terms of various physical and clinical indices. Conclusion The proposed S-DLAC, D-DLAC and D-DLAC-FT methods reduced attenuation artifacts significantly and achieved comparable performance with clinical CTAC for static and dynamic cardiac PET. The use of transfer learning is effective for the dynamic MP PET AC purpose.