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arXiv · 1904.02843

Deep Learning-based Universal Beamformer for Ultrasound Imaging

Shujaat Khan·Jaeyoung Huh·Jong Chul Ye
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Abstract

In ultrasound (US) imaging, individual channel RF measurements are back-propagated and accumulated to form an image after applying specific delays. While this time reversal is usually implemented using a hardware- or software-based delay-and-sum (DAS) beamformer, the performance of DAS decreases rapidly in situations where data acquisition is not ideal. Herein, for the first time, we demonstrate that a single data-driven adaptive beamformer designed as a deep neural network can generate high quality images robustly for various detector channel configurations and subsampling rates. The proposed deep beamformer is evaluated for two distinct acquisition schemes: focused ultrasound imaging and planewave imaging. Experimental results showed that the proposed deep beamformer exhibit significant performance gain for both focused and planar imaging schemes, in terms of contrast-to-noise ratio and structural similarity.

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Shujaat Khan, Jaeyoung Huh, Jong Chul Ye. 2019-04-05. Deep Learning-based Universal Beamformer for Ultrasound Imaging. https://arxiv.org/abs/1904.02843

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