A Deep ADMM-net for iterative low-count PET reconstruction

Yingying Li; Jun Li; HuaFeng Liu

doi:10.1117/12.2573412

10 October 2020 A Deep ADMM-net for iterative low-count PET reconstruction

Yingying Li, Jun Li, HuaFeng Liu

Proceedings Volume 11553, Optics in Health Care and Biomedical Optics X; 115531S (2020) https://doi.org/10.1117/12.2573412
Event: SPIE/COS Photonics Asia, 2020, Online Only

Conference Poster

Abstract

In clinical, researchers have shown an increasing effort in low-dose PET (LdPET) which reduces the risk of radiotracer while maintaining an acceptable image quality and is challenging in practice. To address this issue, regularized model-based image reconstruction (MBIR) is widely applied and the convolutional neural network (CNN) has been demonstrated the efficiency of noise reduction. In this study, we proposed a deep Alternating Direction Method of Multipliers (ADMM) network with residual CNNs. Human brain data pairs of Poisson sampled sinogram and full-dose MLEM reconstructed image was used as the input and ground truth in training phase respectively.Results showed that ADMM-TV-Net outperformed the traditional EM reconstruction and existing algorithms for LdPET, such as nonlocal mean (NLM) and TV in terms of normalized mean square error (NMSE) and reconstruction speed.

Conference Presentation

Citation Download Citation

Yingying Li, Jun Li, and HuaFeng Liu "A Deep ADMM-net for iterative low-count PET reconstruction", Proc. SPIE 11553, Optics in Health Care and Biomedical Optics X, 115531S (10 October 2020); https://doi.org/10.1117/12.2573412

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