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Ultrasound B-Mode images are created from data obtained from each element in the transducer array in a process called beamforming. The beamforming goal is to enhance signals from specified spatial locations, while reducing signal from all other locations. On clinical systems, beamforming is accomplished with the delay-and-sum (DAS) algorithm. DAS is efficient but fails in patients with high noise levels, so various adaptive beamformers have been proposed. Recently, deep learning methods have been developed for this task. With deep learning methods, beamforming is typically framed as a regression problem, where clean, ground-truth data is known, and usually simulated. For in vivo data, however, it is extremely difficult to collect ground truth information, and deep networks trained on simulated data underperform when applied to in vivo data, due to domain shift between simulated and in vivo data. In this work, we show how to correct for domain shift by learning deep network beamformers that leverage both simulated data, and unlabeled in vivo data, via a novel domain adaption scheme. A challenge in our scenario is that domain shift exists both for noisy input, and clean output. We address this challenge by extending cycle-consistent generative adversarial networks, where we leverage maps between synthetic simulation and real in vivo domains to ensure that the learned beamformers capture the distribution of both noisy and clean in vivo data. We obtain consistent in vivo image quality improvements compared to existing beamforming techniques, when applying our approach to simulated anechoic cysts and in vivo liver data. 

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. 

We trained convolutional neural networks (CNNs) to suppress off-axis scattering in the short-time Fourier Transform (STFT) domain. Our training data were point target responses from simulated anechoic cysts. We used random neural architecture search to build CNN models with variable input formulations, layer sizes, and training hyperparameters. Our results showed that CNNs were easier to train, as they required fewer network weights to match the performance of fully-connected networks (FCNs). The best CNN models achieved comparable phantom CNRs with with two to three orders of magnitude fewer weights. 

Deep neural networks have been shown to be effective adaptive beamformers for ultrasound imaging. However, when training with traditional L p norm loss functions, model selection is difficult because lower loss values are not always associated with higher image quality. This ultimately limits the maximum achievable image quality with this approach and raises concerns about the optimization objective. In an effort to align the optimization objective with the image quality metrics of interest, we implemented a novel ultrasound-specific loss function based on the spatial lag-one coherence and signal-to-noise ratio of the delayed channel data in the short-time Fourier domain. We employed the R-Adam optimizer with look ahead and cyclical learning rate to make the training more robust to initialization and local minima, leading to better model performance and more reliable convergence. With our custom loss function and optimization scheme, we achieved higher contrast-to-noise-ratio, higher speckle signal-to-noise-ratio, and more accurate contrast ratio reconstruction than with previous deep learning and delay-and-sum beamforming approaches.