Background Simultaneous dual-tracer positron emission tomography (PET) imaging can observe two molecular targets in a single scan, which is conducive to disease diagnosis and tracking. Since the signals emitted by different tracers are the same, it is crucial to separate each single tracer from the mixed signals. The current study proposed a novel deep learning-based method to reconstruct single-tracer activity distributions from the dual-tracer sinogram. Methods We proposed the Multi-task CNN, a three-dimensional convolutional neural network (CNN) based on a framework of multi-task learning. One common encoder extracted features from the dual-tracer dynamic sinogram, followed by two distinct and parallel decoders which reconstructed the single-tracer dynamic images of two tracers separately. The model was evaluated by mean squared error (MSE), multiscale structural similarity (MS-SSIM) index and peak signal-to-noise ratio (PSNR) on simulated data and real animal data, and compared to the filtered back-projection method based on deep learning (FBP-CNN). Results In the simulation experiments, the Multi-task CNN reconstructed single-tracer images with lower MSE, higher MS-SSIM and PSNR than FBP-CNN, and was more robust to the changes in individual difference, tracer combination and scanning protocol. In the experiment of rats with an orthotopic xenograft glioma model, the Multi-task CNN reconstructions also showed higher qualities than FBP-CNN reconstructions. Conclusions The proposed Multi-task CNN could effectively reconstruct the dynamic activity images of two single tracers from the dual-tracer dynamic sinogram, which was potential in the direct reconstruction for real simultaneous dual-tracer PET imaging data in future.

No abstract

Since the multi-tracer positron emission tomography (PET) technology will increase the total dose of tracers, it is necessary to study the low-dose multi-tracer PET technology. In this paper, we proposed a model based on attention mechanism to estimate the standard-dose single-tracer sinogram from the low-dose dual-tracer sinogram, that is, to achieve both standard-dose estimation and dual-tracer signal separation in sinogram domain. Both the spatial attention and channel attention were implemented. We cascaded the FBP-Net for reconstruction after the proposed model, and verified the proposed method in simulation experiment and rat experiment. At the same time, we compared the results of dual-tracer PET signal separation in sinogram domain and activity map domain. The results prove the effectiveness of the proposed method in the problem of low-dose dual-tracer PET image reconstruction, and also show that signal separation in sinogram domain is more effective than separation in activity map domain.