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Multi-view Image Reconstruction: Application to Fetal Ultrasound Compounding

  • Veronika A. Zimmer
  • Alberto Gomez
  • Yohan Noh
  • Nicolas Toussaint
  • Bishesh Khanal
  • Robert Wright
  • Laura Peralta
  • Milou van Poppel
  • Emily Skelton
  • Jacqueline Matthew
  • Julia A. Schnabel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11076)

Abstract

Ultrasound (US), a standard diagnostic tool to detect fetal abnormalities, is a direction dependent imaging modality, i.e. the position of the probe highly influences the appearance of the image. View-dependent artifacts such as shadows can obstruct parts of the anatomy of interest and degrade the quality and usefulness of the image. If multiple images of the same structure are acquired from different views, view-dependent artifacts can be minimized.

In this work, we propose a new US image reconstruction technique using multiple B-spline grids to enable multi-view US image compounding. The B-spline coefficients of different control point grids adapted to the geometry of the data are simultaneously optimized at every resolution level. Data points are weighted depending on their view, position and intensity. We demonstrate our method on the compounding of co-planar 2D fetal US images acquired from multiple views. Using quantitative and qualitative evaluation scores, we show that the proposed method outperforms other multi-view compounding methods.

Notes

Acknowledgements

This work was supported by the Wellcome Trust IEH Award [102431]. This work was also supported by the Wellcome/EPSRC Centre for Medical Engineering [WT203148/Z/16/Z]. The research was also supported by the National Institute for Health Research (NIHR) Biomedical Research Centre at Guy’s and St Thomas’ NHS Foundation Trust and King’s College London. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Veronika A. Zimmer
    • 1
  • Alberto Gomez
    • 1
  • Yohan Noh
    • 1
  • Nicolas Toussaint
    • 1
  • Bishesh Khanal
    • 1
  • Robert Wright
    • 1
  • Laura Peralta
    • 1
  • Milou van Poppel
    • 1
  • Emily Skelton
    • 1
  • Jacqueline Matthew
    • 1
  • Julia A. Schnabel
    • 1
  1. 1.School of Biomedical Engineering and Imaging SciencesKing’s College LondonLondonUK

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