Deep Learning for Fast Adaptive Beamforming

Abstract

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.