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Detail-Preserving PET Reconstruction with Sparse Image Representation and Anatomical Priors

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Information Processing in Medical Imaging (IPMI 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9123))

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Abstract

Positron emission tomography (PET) reconstruction is an ill-posed inverse problem which typically involves fitting a high-dimensional forward model of the imaging process to noisy, and sometimes undersampled photon emission data. To improve the image quality, prior information derived from anatomical images of the same subject has been previously used in the penalised maximum likelihood (PML) method to regularise the model complexity and selectively smooth the image on a voxel basis in PET reconstruction. In this work, we propose a novel perspective of incorporating the prior information by exploring the sparse property of natural images. Instead of a regular voxel grid, the sparse image representation jointly determined by the prior image and the PET data is used in reconstruction to leverage between the image details and smoothness, and this prior is integrated into the PET forward model and has a closed-form expectation maximisation (EM) solution. Simulations show that the proposed approach achieves improved bias versus variance trade-off and higher contrast recovery than the current state-of-the-art methods, and preserves the image details better. Application to clinical PET data shows promising results.

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Acknowledgement

EPSRC (EP/H046410/1, EP/J020990/1, EP/K005278), the MRC (MR/J01107X/1), the NIHR Biomedical Research Unit (Dementia) at UCL and the National Institute for Health Research University College London Hospitals Biomedical Research Centre (NIHR BRC UCLH/UCL High Impact Initiative). Mattias Heinrich, Benjamin Irving, Carole Sudre, Michael Hütel, M. Jorge Cardoso, Pankaj Daga and Matthias Ehrhardt for discussions.

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Authors and Affiliations

  1. Translational Imaging Group, CMIC, University College London, London, UK

    Jieqing Jiao, Pawel Markiewicz, Ninon Burgos & Sebastien Ourselin

  2. Centre for Medical Imaging, University College London, London, UK

    David Atkinson

  3. Institute of Nuclear Medicine, University College London, London, UK

    Brian Hutton

  4. Centre for Medical Image Computing, University College London, London, UK

    Simon Arridge

  5. Dementia Research Centre, Institute of Neurology, University College London, London, UK

    Sebastien Ourselin

Authors
  1. Jieqing Jiao
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  2. Pawel Markiewicz
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  3. Ninon Burgos
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  4. David Atkinson
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  5. Brian Hutton
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  6. Simon Arridge
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  7. Sebastien Ourselin
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Corresponding author

Correspondence to Jieqing Jiao .

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Editors and Affiliations

  1. Centre for Medical Image Computing, University College London, London, United Kingdom

    Sebastien Ourselin

  2. Centre for Medical Image Computing, University College London, London, United Kingdom

    Daniel C. Alexander

  3. Dept. of Radiology, Harvard Medical School Brigham and Women's Hospital, Boston, Massachusetts, USA

    Carl-Fredrik Westin

  4. Centre for Medical Image Computing, University College London, London, United Kingdom

    M. Jorge Cardoso

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Jiao, J. et al. (2015). Detail-Preserving PET Reconstruction with Sparse Image Representation and Anatomical Priors. In: Ourselin, S., Alexander, D., Westin, CF., Cardoso, M. (eds) Information Processing in Medical Imaging. IPMI 2015. Lecture Notes in Computer Science(), vol 9123. Springer, Cham. https://doi.org/10.1007/978-3-319-19992-4_42

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  • DOI: https://doi.org/10.1007/978-3-319-19992-4_42

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19991-7

  • Online ISBN: 978-3-319-19992-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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