Neural Maximum-a-Posteriori Beamforming for Ultrasound Imaging
Ben Luijten (Eindhoven University of Technology)
Boudewine W. Ossenkoppele (TU Delft - ImPhys/Verweij group, TU Delft - ImPhys/Medical Imaging)
N. Jong (Erasmus MC, TU Delft - ImPhys/De Jong group)
MD Verweij (TU Delft - ImPhys/Medical Imaging, TU Delft - ImPhys/Verweij group, Erasmus MC)
Yonina C. Eldar (Weizmann Institute of Science)
Massimo Mischi (Eindhoven University of Technology)
Ruud J.G. van Sloun (Eindhoven University of Technology, Philips Research)
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Abstract
Ultrasound imaging is an attractive imaging modality due to its low-cost and real-time feedback, although it often falls short in image quality compared to MRI and CT imaging. Conventional ultrasound image reconstruction, such as Delay-and-Sum beamforming, is derived from maximum-likelihood 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.