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

Chunxia Wang; Jingwan Fang; Huafeng Liu

doi:10.1117/12.2653876

7 April 2023 Direct reconstruction and separation for triple-tracer PET imaging based on three-dimensional encoder-decoder network

Chunxia Wang, Jingwan Fang, Huafeng Liu

Author Affiliations +

Proceedings Volume 12463, Medical Imaging 2023: Physics of Medical Imaging; 124632P (2023) https://doi.org/10.1117/12.2653876
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

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.

Citation Download Citation

Chunxia Wang, Jingwan Fang, and Huafeng Liu "Direct reconstruction and separation for triple-tracer PET imaging based on three-dimensional encoder-decoder network", Proc. SPIE 12463, Medical Imaging 2023: Physics of Medical Imaging, 124632P (7 April 2023); https://doi.org/10.1117/12.2653876

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