Name
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-32254-0_69 | Lecture Notes in Computer Science |
Keywords | DocType | Volume |
Ultrasound,Adaptive beamforming,Deep neural network | Conference | 11768 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shujaat Khan | 1 | 38 | 9.56 |
| Jaeyoung Huh | 2 | 5 | 1.40 |
| Jong Chul Ye | 3 | 715 | 79.99 |