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International Conference on Medical Image Computing and Computer-Assisted Intervention

MICCAI 2020: Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 pp 783–792Cite as

Rethinking PET Image Reconstruction: Ultra-Low-Dose, Sinogram and Deep Learning

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12267))

Abstract

Although Positron emission tomography (PET) has a wide range of clinical applications, radiation exposure to patients in PET continues to draw concerns. To reduce the radiation risk, efforts have been made to obtain high resolution images from low-resolution images. However, previous studies mainly focused on denoising PET images in image space, which ignored the influence of sinogram quality and constraints in reconstruction process. This paper proposed a directly reconstruction framework from ultra-low-dose sinogram based on deep learning. Two coupled networks are introduced to sequentially denoise low-dose sinogram and reconstruct the activity map. Evaluation on in vivo PET dataset indicates that the proposed method can achieve better performance than other state-of-the-art methods and reconstruct satisfactory PET images with only 0.2% dose of standard one.

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Acknowledgements

This work is supported in part by the National Natural Science Foundation of China (No: 61525106, 61427807, U1809204), by the National Key Technology Research and Development Program of China (No: 2017YFE0104000,2016YFC1300302).

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Authors and Affiliations

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

    Qiupeng Feng & Huafeng Liu

Authors
  1. Qiupeng Feng
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  2. Huafeng Liu
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Corresponding author

Correspondence to Huafeng Liu .

Editor information

Editors and Affiliations

  1. University of Toronto, Toronto, ON, Canada

    Anne L. Martel

  2. The University of British Columbia, Vancouver, BC, Canada

    Purang Abolmaesumi

  3. University College London, London, UK

    Danail Stoyanov

  4. École Centrale de Nantes, Nantes, France

    Diana Mateus

  5. EURECOM, Biot, France

    Maria A. Zuluaga

  6. Chinese Academy of Sciences, Beijing, China

    S. Kevin Zhou

  7. Sorbonne University, Paris, France

    Daniel Racoceanu

  8. The Hebrew University of Jerusalem, Jerusalem, Israel

    Leo Joskowicz

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Feng, Q., Liu, H. (2020). Rethinking PET Image Reconstruction: Ultra-Low-Dose, Sinogram and Deep Learning. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12267. Springer, Cham. https://doi.org/10.1007/978-3-030-59728-3_76

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  • DOI: https://doi.org/10.1007/978-3-030-59728-3_76

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59727-6

  • Online ISBN: 978-3-030-59728-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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