Multiscale deep-learning network based reconstruction of PET images from sinogram domain

Zhiyuan Liu; Huafeng Liu

doi:10.1117/12.2601192

9 October 2021 Multiscale deep-learning network based reconstruction of PET images from sinogram domain

Zhiyuan Liu, Huafeng Liu

Proceedings Volume 11900, Optics in Health Care and Biomedical Optics XI; 1190010 (2021) https://doi.org/10.1117/12.2601192
Event: SPIE/COS Photonics Asia, 2021, Nantong, JS, China

Abstract

Positron emission tomography (PET) is an indispensable medical imaging technique which can reveal the physiology activities by injecting special PET tracers. In recent years, deep learning has been widely used in PET reconstruction tasks. In this work, a multiscale direct reconstruction framework based on cGANs has been presented. First, the generator is built as two scales which will combine both global and detail information in training phase. Second, to match the generator, we tried multiscale discriminators. Each discriminator shares the same structure, but operates on different scales. Both coarse field and fine field were scored by the discriminators, so this score considered not only the global structure but also fine details. In order to verify the feasibility of the proposed framework, both simulation datasets and real SD rat dataset were utilized in our experiments. We also compared our method with U-Net and Deep- PET network, the results showed that we got more accurate and real radioactivity images on all datasets.

Conference Presentation

Citation Download Citation

Zhiyuan Liu and Huafeng Liu "Multiscale deep-learning network based reconstruction of PET images from sinogram domain", Proc. SPIE 11900, Optics in Health Care and Biomedical Optics XI, 1190010 (9 October 2021); https://doi.org/10.1117/12.2601192

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