Effects of ultrasound imaging parameter to estimate nonlinearity

• Main Authors: Chih-Hao Lai, 賴志豪
• Other Authors: 曹建和
• Format: Others
• Language: zh-TW
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/54726842060056278826

碩士 === 國立臺灣大學 === 生醫電子與資訊學研究所 === 101 === The conventional fundamental ultrasound imaging is by detecting the magnitude of reflected sound, and the harmonic imaging, which has higher resolution and deeper penetration depth, is by detecting the magnitude of reflected sound distortion resulted from tissue nonlinearity and obtained from filtering. However, some organ diseases change specific ultrasound parameters, such as liver disease, and in addition these ultrasound parameters changes little except for nonlinearity. Different textures in ultrasound imaging are cause by different ultrasound parameter in both fundamental and harmonic ultrasound imaging. Though area with different ultrasound parameter do not ensure that there is an interface, it is difficult to detect disease by the fundamental imaging and the harmonic imaging. Nevertheless, it is might be useful to image nonlinearity to detect disease area. In this study, we simulate ultrasound propagating in the tissue and estimate ultrasound nonlinearity by simulation signals. The simulation method is solving acoustics equation by the pseudo-spectrum, the perfectly matched layer boundary condition, and the second relaxation model. To estimate nonlinearity and image, we simulate first, filter the resulted signal, estimate the attenuation, and finally compute by the derived equation. This estimation affect by ultrasound parameter such as frequency, bandwidth, depth, disease area, and the magnitude of changes. The estimating nonlinearity has higher contrast when emit higher frequency, but only for the more shallow depths. The estimating nonlinearity has greater resolution when emit greater bandwidth signals, which increase noise and decrease SNR when considering the random effects. Finally the resolution of estimating nonlinearity is relate to the size and magnitude of disease area. Such estimating method requires differential operation, which is increase the noise effect. Therefore the biggest drawback is the requirement of very high input SNR. It might increase SNR by emit low bandwidth signals and decrease a little resolution. However, due to the low bandwidth of the emit signal obtained with lower resolution, it is possible to increase resolution and SNR by synthetic spectrum.