Direct reconstruction and separation for triple-tracer PET imaging based on three-dimensional encoder-decoder network

So far, dual-tracer positron emission tomography (PET) imaging has been a rising topic in the field of PET imaging. This study focused on the reconstruction and signal separation for simultaneous triple-tracer PET imaging, where three tracers were injected at the same time. To reconstruct the image of each tracer, we proposed a three-dimensional encoder-decoder network based on multi-task learning. The mixed dynamic sinogram of three tracers was input into the encoder module. Then, the three different decoding modules output the dynamic image of each tracer according to their unique characteristics. The proposed model could simultaneously learn the spatial information and temporal information from the mixed PET signals. The reconstructions were evaluated by multi-scale structural similarity (MS-SSIM) and peak signal-to-noise ratio (PSNR). The robustness of this method was verified by simulated datasets with different phantoms, tracer combinations and sampling protocols.