We investigated using deep neural networks (DNNs) to beamform ultrasound images with high dynamic range targets. The DNNs operated on frequency domain data, the inputs consisted of the separated in-phase and quadrature components observed across the aperture of the array, and the outputs of the DNNs had the same structure as the inputs. We compared several methods for generating training data, including training with hypoechoic and anechoic cysts. All training data was generated using a linear ultrasound simulation tool. The results demonstrate the potential for using DNN beamformers to extend the dynamic range of ultrasound beamforming.

Date Published: 15 March 2019
PDF: 7 pages
Proc. SPIE 10955, Medical Imaging 2019: Ultrasonic Imaging and Tomography, 109550P (15 March 2019); doi: 10.1117/12.2514185

Published in SPIE Proceedings Vol. 10955:
Medical Imaging 2019: Ultrasonic Imaging and Tomography
Brett C. Byram; Nicole V. Ruiter, Editor(s)