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Speed-of-Sound Mapping for Pulse-Echo Ultrasound Raw Data Using Linked-Autoencoders

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Machine Learning for Multimodal Healthcare Data (ML4MHD 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14315))

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Abstract

Recent studies showed the possibility of extracting SoS information from pulse-echo ultrasound raw data (a.k.a. RF data) using deep neural networks that are fully trained on simulated data. These methods take sensor domain data, i.e., RF data, as input and train a network in an end-to-end fashion to learn the implicit mapping between the RF data domain and the SoS domain. However, such networks are prone to overfitting to simulated data which results in poor performance and instability when tested on measured data. We propose a novel method for SoS mapping employing learned representations from two linked autoencoders. We test our approach on simulated and measured data acquired from human breast mimicking phantoms. We show that SoS mapping is possible using the learned representations by linked autoencoders. The proposed method has a Mean Absolute Percentage Error (MAPE) of \(2.39\%\) on the simulated data. On the measured data, the predictions of the proposed method are close to the expected values (MAPE of \(1.1\) \(\%\)). Compared to an end-to-end trained network, the proposed method shows higher stability and reproducibility.

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Acknowledgments

We thank Prof. Michael Golatta and the university hospital of Heidelberg for providing the Tomosynthesis dataset.

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Authors and Affiliations

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

    Farnaz Khun Jush & Andreas Maier

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

    Peter M. Dueppenbecker

Authors
  1. Farnaz Khun Jush
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  2. Peter M. 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. Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Bayern, Germany

    Andreas K. Maier

  2. Technical University of Munich, Garching bei München, Bayern, Germany

    Julia A. Schnabel

  3. College of Engineering, University of Wisconsin–Madison, Madison, WI, USA

    Pallavi Tiwari

  4. German Cancer Research Center, Heidelberg, Germany

    Oliver Stegle

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Khun Jush, F., Dueppenbecker, P.M., Maier, A. (2024). Speed-of-Sound Mapping for Pulse-Echo Ultrasound Raw Data Using Linked-Autoencoders. In: Maier, A.K., Schnabel, J.A., Tiwari, P., Stegle, O. (eds) Machine Learning for Multimodal Healthcare Data. ML4MHD 2023. Lecture Notes in Computer Science, vol 14315. Springer, Cham. https://doi.org/10.1007/978-3-031-47679-2_8

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  • DOI: https://doi.org/10.1007/978-3-031-47679-2_8

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-031-47679-2

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