Learning High-resolution Delay-and-sum Beamforming

Ultrasound (US) imaging is a versatile tool in modern healthcare diagnostics that often faces spatial resolution challenges. Although ultrasound localization microscopy (ULM) surpasses resolution constraints by fast perfusion scanning, it often relies on traditional delay-and-sum (DAS) beamformers. In response, we propose a differentiable DAS pipeline with a learnable apodization feature descriptor connected to a super-resolution network. Learning apodization weights and image super-resolution contributes to an improvement of B-mode image quality. Quantitative assessment on ULM data and validation with an in vivo dataset demonstrates the effectiveness of our approach. While this study employs ULM data, our findings provide insights that hold broader implications for computational beamforming.