In PET/CT imaging, CT is used for PET attenuation correction (AC). Mismatch between CT and PET due to patient body motion results in AC artifacts. In addition, artifact caused by metal, beam-hardening and count-starving in CT itself also introduces inaccurate AC for PET. Maximum likelihood reconstruction of activity and attenuation (MLAA) was proposed to solve those issues by simultaneously reconstructing tracer activity (-MLAA) and attenuation map (-MLAA) based on the PET raw data only. However, -MLAA suffers from high noise and -MLAA suffers from large bias as compared to the reconstruction using the CT-based attenuation map (-CT). Recently, a convolutional neural network (CNN) was applied to predict the CT attenuation map (-CNN) from -MLAA and -MLAA, in which an image-domain loss (IM-loss) function between the -CNN and the ground truth -CT was used. However, IM-loss does not directly measure the AC errors according to the PET attenuation physics, where the line-integral projection of the attenuation map () along the path of the two annihilation events, instead of the itself, is used for AC. Therefore, a network trained with the IM-loss may yield suboptimal performance in the generation. Here, we propose a novel line-integral projection loss (LIP-loss) function that incorporates the PET attenuation physics for generation. Eighty training and twenty testing datasets of whole-body 18F-FDG PET and paired ground truth -CT were used. Quantitative evaluations showed that the model trained with the additional LIP-loss was able to significantly outperform the model trained solely based on the IM-loss function.
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