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Data-Driven Speed-of-Sound Reconstruction for Medical Ultrasound: Impacts of Training Data Format and Imperfections on Convergence

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Medical Image Understanding and Analysis (MIUA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12722))

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Abstract

B-mode imaging is a qualitative method and its interpretation depends on users’ experience. Quantitative tissue information can increase precision and decrease user ambiguity. For example, Speed-of-Sound (SoS) in tissue is known to carry diagnostic information. Studies showed the possibility of SoS reconstruction from ultrasound raw data (a.k.a., RF data) using deep neural networks (DNNs). However, many ultrasound systems are designed to process demodulated data (i.e., IQ data) and often decimate data in early stages of acquisition. In this study we investigated the impacts of input data format and decimation on convergence of the DNNs for SoS reconstruction. Our results show that fully data-driven SoS reconstruction is possible using demodulated ultrasound data presented in Cartesian or Polar format using an encoder-decoder network. We performed a study using only amplitude and only phase information of ultrasound data for SoS reconstruction. Our results showed that distortion of the phase information results in inconsistent SoS predictions, indicating sensitivity of the investigated approach to phase information. We demonstrated that without losing significant accuracy, decimated IQ data can be used for SoS reconstruction.

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Authors and Affiliations

  1. Technology Excellence, Siemens Healthcare GmbH, Erlangen, Germany

    Farnaz Khun Jush & Peter Michael Dueppenbecker

  2. Pattern Recognition Lab, Friedrich-Alexander-University, Erlangen, Germany

    Farnaz Khun Jush & Andreas Maier

Authors
  1. Farnaz Khun Jush
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  2. Peter Michael Dueppenbecker
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  3. Andreas Maier
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Corresponding author

Correspondence to Farnaz Khun Jush .

Editor information

Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Bartłomiej W. Papież

  2. Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates

    Mohammad Yaqub

  3. University of Oxford, Oxford, UK

    Jianbo Jiao

  4. University of Oxford, Oxford, UK

    Ana I. L. Namburete

  5. University of Oxford, Oxford, UK

    J. Alison Noble

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Khun Jush, F., Dueppenbecker, P.M., Maier, A. (2021). Data-Driven Speed-of-Sound Reconstruction for Medical Ultrasound: Impacts of Training Data Format and Imperfections on Convergence. In: Papież, B.W., Yaqub, M., Jiao, J., Namburete, A.I.L., Noble, J.A. (eds) Medical Image Understanding and Analysis. MIUA 2021. Lecture Notes in Computer Science(), vol 12722. Springer, Cham. https://doi.org/10.1007/978-3-030-80432-9_11

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  • DOI: https://doi.org/10.1007/978-3-030-80432-9_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-80431-2

  • Online ISBN: 978-3-030-80432-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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