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.

Authors with CRIS profile

Farnaz Khun Jush Lehrstuhl für Informatik 5 (Mustererkennung) Andreas Maier Lehrstuhl für Informatik 5 (Mustererkennung)

Involved external institutions

Siemens AG, Healthcare Sector DE Germany (DE)

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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