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15 March 2019 High dynamic range ultrasound beamforming using deep neural networks
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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
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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);

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