Name
Abstract | ||
|---|---|---|
Conventional delay-and-sum (DAS) beamforming is highly efficient but also suffers from various sources of image degradation. Several adaptive beamformers have been proposed to address this problem, including more recently proposed deep learning methods. With deep learning, adaptive beamforming is typically framed as a regression problem, where clean ground-truth physical information is used for tr... |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TMI.2021.3107198 | IEEE Transactions on Medical Imaging |
Keywords | DocType | Volume |
In vivo,Array signal processing,Training,Data models,Training data,Ultrasonic imaging,Image quality | Journal | 41 |
Issue | ISSN | Citations |
1 | 0278-0062 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jaime Tierney | 1 | 0 | 1.01 |
| Adam Luchies | 2 | 0 | 0.34 |
| Christopher Khan | 3 | 0 | 0.34 |
| Jennifer Baker | 4 | 0 | 0.68 |
| Daniel Brown | 5 | 29 | 6.43 |
| Brett Byram | 6 | 0 | 0.34 |
| Matthew S. Berger | 7 | 20 | 3.67 |