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

Khun Jush F, Dueppenbecker PM, Maier A (2024)


Publication Type: Conference contribution

Publication year: 2024

Journal

Publisher: Springer Science and Business Media Deutschland GmbH

Book Volume: 14315 LNCS

Pages Range: 103-114

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Honolulu, HI, USA

ISBN: 9783031476785

DOI: 10.1007/978-3-031-47679-2_8

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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How to cite

APA:

Khun Jush, F., Dueppenbecker, P.M., & Maier, A. (2024). Speed-of-Sound Mapping for Pulse-Echo Ultrasound Raw Data Using Linked-Autoencoders. In Andreas K. Maier, Julia A. Schnabel, Pallavi Tiwari, Oliver Stegle (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 103-114). Honolulu, HI, USA: Springer Science and Business Media Deutschland GmbH.

MLA:

Khun Jush, Farnaz, Peter M. Dueppenbecker, and Andreas Maier. "Speed-of-Sound Mapping for Pulse-Echo Ultrasound Raw Data Using Linked-Autoencoders." Proceedings of the 1st International Workshop on Machine Learning for Multimodal Healthcare Data, ML4MHD 2023, Honolulu, HI, USA Ed. Andreas K. Maier, Julia A. Schnabel, Pallavi Tiwari, Oliver Stegle, Springer Science and Business Media Deutschland GmbH, 2024. 103-114.

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