High dynamic range ultrasound beamforming using deep neural networks

Adam Luchies; Brett Byram

doi:10.1117/12.2514185

15 March 2019 High dynamic range ultrasound beamforming using deep neural networks

Adam Luchies, Brett Byram

Proceedings Volume 10955, Medical Imaging 2019: Ultrasonic Imaging and Tomography; 109550P (2019) https://doi.org/10.1117/12.2514185
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

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.

Conference Presentation

Citation Download Citation

Adam Luchies and Brett Byram "High dynamic range ultrasound beamforming using deep neural networks", Proc. SPIE 10955, Medical Imaging 2019: Ultrasonic Imaging and Tomography, 109550P (15 March 2019); https://doi.org/10.1117/12.2514185

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