DNN Beamforming for High Contrast Targets in the Presence of Reverberation Clutter

Abstract

We evaluated training deep neural network (DNN) beamformers for the task of high contrast imaging in the presence of reverberation clutter. Training data was generated using simulated hypoechoic cysts and a pseudo nonlinear method for generating reverberation clutter. Performance was compared to standard delay-and-sum (DAS) beamforming on simulated hypoechoic cysts having a different size. For a hypoechoic cyst in the presence of reverberation clutter, when the intrinsic contrast ratio (CR) was -10 dB and -20 dB, the measured CR for DAS beamforming was -9.2±0.8 dB and -14.3±0.5 dB, respectively, and the measured CR for DNNs was -10.7±1.4 dB and -20.0±1.0 dB, respectively. For a hypoechoic cyst with -20 dB intrinsic CR, the contrast-to-noise ratio (CNR) was 3.4±0.3 dB and 4.3±0.3 dB for DAS and DNN beamforming, respectively. These results show that DNN beamforming was able to extend contrast ratio dynamic range (CRDR) by about 10 dB while also improving CNR.