Deep learning-based attenuation map generation with simultaneously reconstructed PET activity and attenuation and low-dose application


 In PET/CT imaging, CT is used for PET attenuation correction (AC). Artifacts in CT or misalignment between PET and CT can cause AC artifacts and quantification errors in PET. Simultaneous reconstruction (MLAA) of PET activity (λ-MLAA) and attenuation (μ-MLAA) maps was proposed to solve those issues using the time-of-flight (TOF) PET raw data only. However, λ-MLAA still suffers from quantification error as compared to the reconstruction using the gold-standard CT-based attenuation map (μ-CT). Recently, a deep learning (DL)-based framework was proposed to improve MLAA by predicting the CT attenuation map (μ-DL) from λ-MLAA and μ-MLAA using an image domain loss function (IM-loss). However, IM-loss does not directly measure the AC errors according to the PET attenuation physics. Our preliminary studies showed that an additional physics-based loss function that measures the line-integral difference between μ-DL and μ-CT in the projection domain can lead to more accurate PET AC. In this study, we propose to optimize the attenuation map generation framework for clinical full-dose 18F-FDG studies. We also investigate the effectiveness of the optimized network on predicting the attenuation maps for synthetic low-dose oncological PET studies. We optimized the proposed DL framework by applying different preprocessing steps and hyperparameter optimization, including the patch size, the weights of the loss terms and the number of angles in the projection-domain loss term. The optimization was performed based on 100 skull-to-toe 18F‐FDG PET/CT scans with minimal misalignment. The optimized framework was further evaluated on 85 clinical full-dose neck-to-thigh 18F-FDG cancer datasets as well as synthetic low-dose studies with only 10% of the full-dose raw data. The clinical evaluation of tumor quantification as well as the physics-based figure-of-merit metric evaluation validated the promising performance of our proposed method. For both full-dose and low-dose studies, the proposed framework achieved <1% error in tumor SUV measures.