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Deep Learning for Fast Adaptive Beamforming

机译：深度学习以实现快速自适应波束成形

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The real-time nature that makes diagnostic ultrasonography so appealing to clinicians imposes strong constraints on the computational complexity of image reconstruction algorithms. As such, these typically rely on traditional delay-and-sum beamforming, a low-complexity approach that unfortunately comes at the cost of reduced image quality as compared to more advanced and content-adaptive beamformers. Here, we propose a model-aware deep learning strategy to ultrasound image reconstruction, which leverages knowledge of minimum variance beamforming while exploiting the efficiency of deep neural networks. Our approach yields high quality images with strong contrast at real-time reconstruction rates. The neural network is trained using in vivo and simulated radio frequency channel data of a single plane wave transmit, and corresponding high-quality minimum-variance beamformed reconstructions. Performance is benchmarked using simulated acquisitions from the PICMUS [1] dataset, demonstrating the convincing generalizability and image quality of the proposed beamformer.
机译：使得诊断超声检查如此吸引临床医生的实时性质对图像重建算法的计算复杂性施加了严格的约束。因此，这些通常依赖于传统的延迟和求和波束形成，这是一种低复杂度的方法，与更先进且适应内容的波束形成器相比，不幸的是以降低图像质量为代价。在这里，我们提出了一种用于超声图像重建的模型感知型深度学习策略，该策略利用最小方差波束形成的知识，同时利用深度神经网络的效率。我们的方法可在实时重建速率下产生具有强烈对比度的高质量图像。使用单个平面波发射的体内和模拟射频信道数据以及相应的高质量最小方差波束成形重建来训练神经网络。使用来自PICMUS [1]数据集的模拟采集来对性能进行基准测试，这证明了所提出的波束形成器的令人信服的通用性和图像质量。

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