Neural Maximum-A-Posteriori Beamforming For Ultrasound Imaging

Ultrasound imaging is an attractive imaging modality due to its low-cost and real-time feedback, but often lacks in image quality as compared to MRI and CT imaging. Conventional ultrasound image reconstruction, such as Delay-and-Sum (DAS) beamforming, is derived from maximum-likelihood (ML) estimation. As such, no prior information is exploited in the image formation process, which limits potential image quality. Maximum-a-posteriori (MAP) beamforming aims to overcome this issue, but often relies on rough approximations of the underlying signal statistics. Deep learning based reconstruction methods have demonstrated impressive results over the past years, but often lack interpretability and require vast amounts of data. In this work we present a neural MAP beamforming technique, which efficiently combines deep learning in the MAP beamforming framework. We show that this model-based deep learning approach can achieve high-quality imaging, improving over the state-of-the-art, without compromising the real-time abilities of ultrasound imaging.