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Proceedings Paper

Effects of important parameters variations on computing eigenspace-based minimum variance weights for ultrasound tissue harmonic imaging
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Paper Abstract

In recent years, the minimum variance (MV) beamforming has been widely studied due to its high resolution and contrast in B-mode Ultrasound imaging (USI). However, the performance of the MV beamformer is degraded at the presence of noise, as a result of the inaccurate covariance matrix estimation which leads to a low quality image. Second harmonic imaging (SHI) provides many advantages over the conventional pulse-echo USI, such as enhanced axial and lateral resolutions. However, the low signal-to-noise ratio (SNR) is a major problem in SHI. In this paper, Eigenspace-based minimum variance (EIBMV) beamformer has been employed for second harmonic USI. The Tissue Harmonic Imaging (THI) is achieved by Pulse Inversion (PI) technique. Using the EIBMV weights, instead of the MV ones, would lead to reduced sidelobes and improved contrast, without compromising the high resolution of the MV beamformer (even at the presence of a strong noise). In addition, we have investigated the effects of variations of the important parameters in computing EIBMV weights, i.e., K, L, and δ, on the resolution and contrast obtained in SHI. The results are evaluated using numerical data (using point target and cyst phantoms), and the proper parameters of EIBMV are indicated for THI.

Paper Details

Date Published: 22 February 2018
PDF: 9 pages
Proc. SPIE 10494, Photons Plus Ultrasound: Imaging and Sensing 2018, 104946R (22 February 2018); doi: 10.1117/12.2291549
Show Author Affiliations
Mehdi Haji Heidari, Tarbiat Modares Univ. (Iran, Islamic Republic of)
Moein Mozaffarzadeh, Tarbiat Modares Univ. (Iran, Islamic Republic of)
Institute for Advanced Medical Technologies
Rayyan Manwar, Wayne State Univ. (United States)
Mohammadreza Nasiriavanaki, Wayne State Univ. (United States)


Published in SPIE Proceedings Vol. 10494:
Photons Plus Ultrasound: Imaging and Sensing 2018
Alexander A. Oraevsky; Lihong V. Wang, Editor(s)

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