DNN-based Speed-of-Sound Reconstruction for Automated Breast Ultrasound

Khun Jush F, Biele M, Dueppenbecker PM, Schmidt O, Maier A (2020)


Publication Language: English

Publication Type: Conference contribution, Original article

Publication year: 2020

Publisher: IEEE

Pages Range: 1-7

Conference Proceedings Title: 2020 IEEE International Ultrasonics Symposium (IUS)

Event location: Las Vegas, NV US

ISBN: 978-1-7281-5449-7

URI: https://ieeexplore.ieee.org/document/9251579

DOI: 10.1109/IUS46767.2020.9251579

Abstract

The gold-standard for breast cancer screening is xray mammography. Alongside, ultrasound scans are being used as additional source of information for patients with dense breast tissue. However, conventional ultrasound imaging is a qualitative approach and is prone to errors. Quantitative approaches can provide valuable information about tissue properties, e.g. the speed-of-sound in the tissue can be used as a biomarker for breast tissue malignancy. Recent studies showed the possibility of speed-of-sound reconstruction from ultrasound raw data using Deep Neural Networks (DNNs). In this study, we investigate the feasibility of DNN-based speed-of-sound reconstruction for automated breast ultrasound with simulated and real data. We set up a DNN for speed-of-sound reconstruction. The network is fully trained on simulated data. Simulations are based on the LightABVS transducer, a linear transducer with 192 active channels. The input of the network is raw channel data from a single plane-wave acquisition. The output of the network is a speed-of-sound map with a resolution of 0.1 mm. We achieved Mean Absolute Percentage Error of 0.39 ± 0.03% and Root-Mean-Square Error of 14.85 ± 0.52 m/s on simulated dataset and promising results on real dataset which demonstrates great potential of this method for integration in conventional ultrasound systems.

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How to cite

APA:

Khun Jush, F., Biele, M., Dueppenbecker, P.M., Schmidt, O., & Maier, A. (2020). DNN-based Speed-of-Sound Reconstruction for Automated Breast Ultrasound. In 2020 IEEE International Ultrasonics Symposium (IUS) (pp. 1-7). Las Vegas, NV, US: IEEE.

MLA:

Khun Jush, Farnaz, et al. "DNN-based Speed-of-Sound Reconstruction for Automated Breast Ultrasound." Proceedings of the 2020 IEEE International Ultrasonics Symposium (IUS), Las Vegas, NV IEEE, 2020. 1-7.

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