Adaptive Medical Ultrasound Imaging with Data Dependent Weighting Spatial Smoothing Technique

DOI

10.2991/caai-18.2018.8How to use a DOI?

Keywords

ultrasound imaging; adaptive beamforming; spatial smoothing; minimum variance

Abstract

In adaptive medical ultrasound imaging, the performance of the adaptive beamformer directly depends on the estimation of the spatial characteristic of the noise and interferences. Accurate estimation of the array covariance matrix leads to significant improvement in image quality compared with non-adaptive delay-and-sum (DAS) beamformers. Most of the techniques have been employed to get a good estimation of array covariance matrix are based on spatial smoothing and diagonal loading. In this paper, spatial smoothing with data dependent weighting has been applied for array covariance matrix estimation, which is then employed in minimum variance (MV) weights calculation. The adaptive weighting spatial smoothing (AWSS) MV beamformer utilizes forward-backward averaging and data dependent weights to make the estimated array covariance matrix as close to Toeplitz matrix as possible. Using AWSS beamformer instead of the normal forward-only beamformers leads to more accurate estimation of the array covariance matrix, significantly improve the imaging resolution and contrast without the need for temporal smoothing and diagonal loading. The performance of the proposed approach is demonstrated by several simulated examples.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Lutao Wang
AU  - Jin Gang
AU  - Wei Wang
PY  - 2018/08
DA  - 2018/08
TI  - Adaptive Medical Ultrasound Imaging with Data Dependent Weighting Spatial Smoothing Technique
BT  - Proceedings of the 2018 3rd International Conference on Control, Automation and Artificial Intelligence (CAAI 2018)
PB  - Atlantis Press
SP  - 31
EP  - 35
SN  - 2589-4919
UR  - https://doi.org/10.2991/caai-18.2018.8
DO  - 10.2991/caai-18.2018.8
ID  - Wang2018/08
ER  -