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EMISTA-Based Quantitative PET Reconstruction

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Image and Graphics (ICIG 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12889))

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Abstract

As an important tool for cancer diagnosis and brain function imaging, PET is widely used in clinical fields where quantitative accuracy takes an important role. Several methods have been suggested to improve the quantification performance of PET by introducing quantitative coefficients, taking advantages of the time-of-flight (TOF) information, or modeling the point spread function (PSF). However, some physical effects such as photon attenuation limit the quantification potential, which should be focused on. In this paper, we proposed a novel method based on the combination of the expectation maximization (EM) and the learning ability of neural networks, achieving the quantification of PET reconstruction from raw sinogram data without attenuation correction (AC). The EM module was utilized to recover less-accurate PET images without the consideration of attenuation. And these rough images were finely adjusted by the network module which has strong nonlinearity to improve the quantitative performance. The proposed method was evaluated on simulated phantom data and compared to several existing reconstruction methods. It turns out that our method has great potential of high-quality PET image reconstruction.

Supported in part by the National Key Technology Research and Development Program of China (No: 2016YFC1300302, 2017YFE0104000), the National Natural Science Foundation of China (No: U1809204, 61701436), and by the Key Research and Development Program of Zhejiang Province (No: 2021C03029).

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Authors and Affiliations

  1. State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, 310027, China

    Linlin Zhao & Huafeng Liu

Authors
  1. Linlin Zhao
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  2. Huafeng Liu
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Corresponding author

Correspondence to Huafeng Liu .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Yuxin Peng

  2. Tsinghua University, Beijing, China

    Shi-Min Hu

  3. Tampere University, Tampere, Finland

    Moncef Gabbouj

  4. Zhejiang University, Hangzhou, China

    Kun Zhou

  5. Technion – Israel Institute of Technology, Haifa, Israel

    Michael Elad

  6. Tsinghua University, Beijing, China

    Kun Xu

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Zhao, L., Liu, H. (2021). EMISTA-Based Quantitative PET Reconstruction. In: Peng, Y., Hu, SM., Gabbouj, M., Zhou, K., Elad, M., Xu, K. (eds) Image and Graphics. ICIG 2021. Lecture Notes in Computer Science(), vol 12889. Springer, Cham. https://doi.org/10.1007/978-3-030-87358-5_56

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  • DOI: https://doi.org/10.1007/978-3-030-87358-5_56

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-87357-8

  • Online ISBN: 978-3-030-87358-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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