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Plug-and-Play Priors for Reconstruction-Based Placental Image Registration

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Book cover Smart Ultrasound Imaging and Perinatal, Preterm and Paediatric Image Analysis (PIPPI 2019, SUSI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11798))

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Abstract

This paper presents a novel deformable registration framework, leveraging an image prior specified through a denoising function, for severely noise-corrupted placental images. Recent work on plug-and-play (PnP) priors has shown the state-of-the-art performance of reconstruction algorithms under such priors in a range of imaging applications. Integration of powerful image denoisers into advanced registration methods provides our model with a flexibility to accommodate datasets that have low signal-to-noise ratios (SNRs). We demonstrate the performance of our method under a wide variety of denoising models in the context of diffeomorphic image registration. Experimental results show that our model substantially improves the accuracy of spatial alignment in applications of 3D in-utero diffusion-weighted MR images (DW-MRI) that suffer from low SNR and large spatial transformations.

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Acknowledgement

This work was supported by NIH grant R01HD094381, NIH grant R01AG053548, and BrightFocus Foundation A2017330S.

Author information

Authors and Affiliations

  1. Electrical and Computer Engineering, University of Virginia, Charlottesville, USA

    Jiarui Xing & Miaomiao Zhang

  2. Computer Science and Engineering, Washington University in St. Louis, St. Louis, USA

    Ulugbek Kamilov

  3. Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, USA

    Ulugbek Kamilov

  4. Biomedical Engineering, Washington University in St. Louis, St. Louis, USA

    Wenjie Wu

  5. Obstetrics and Gynecology, Washington University in St. Louis, St. Louis, USA

    Wenjie Wu & Yong Wang

  6. Computer Science, University of Virginia, Charlottesville, USA

    Miaomiao Zhang

Authors
  1. Jiarui Xing
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  2. Ulugbek Kamilov
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  3. Wenjie Wu
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  4. Yong Wang
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  5. Miaomiao Zhang
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Corresponding author

Correspondence to Jiarui Xing .

Editor information

Editors and Affiliations

  1. Shanghai Jiao Tong University, Shanghai, China

    Qian Wang

  2. King's College London, London, UK

    Alberto Gomez

  3. King's College London, London, UK

    Jana Hutter

  4. GE Vingmed Ultrasound, GE Healthcare, Oslo, Norway

    Kristin McLeod

  5. School of Imaging Sciences & Biomedical Engineering, King's College London, London, UK

    Veronika Zimmer

  6. ImFusion GmbH, Munich, Germany

    Oliver Zettinig

  7. Computer Vision Lab, TU Vienna, Vienna, Austria

    Roxane Licandro

  8. School of Imaging Sciences & Biomedical Engineering, King's College London, London, UK

    Emma Robinson

  9. School of Imaging Sciences & Biomedical Engineering, King's College London, London, UK

    Daan Christiaens

  10. Boston Children's Hospital, Boston, UK

    Esra Abaci Turk

  11. School of Imaging Sciences & Biomedical Engineering, King's College London, London, UK

    Andrew Melbourne

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Xing, J., Kamilov, U., Wu, W., Wang, Y., Zhang, M. (2019). Plug-and-Play Priors for Reconstruction-Based Placental Image Registration. In: Wang, Q., et al. Smart Ultrasound Imaging and Perinatal, Preterm and Paediatric Image Analysis. PIPPI SUSI 2019 2019. Lecture Notes in Computer Science(), vol 11798. Springer, Cham. https://doi.org/10.1007/978-3-030-32875-7_15

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  • DOI: https://doi.org/10.1007/978-3-030-32875-7_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-32874-0

  • Online ISBN: 978-3-030-32875-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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