频域分割的复合超声热应变成像
Improved ultrasound thermal strain imaging using frequency domain segmentation
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摘要: 在超声热应变成像(TSI)中, 二维TSI图像中加热区域即感兴趣区域(ROI)的后方(远离声源方向)由于热声棱镜效应、互相关函数峰值误判等可能出现较强的噪声, 影响对热区的识别。为对TSI图像降噪并优化ROI的选取, 提出一种频域分割的复合超声热应变成像方法。首先, 对加热过程中超声成像得到的射频数据进行频段划分, 将划分后每个频段的数据视作一次独立成像的结果。其次, 利用不同频段数据分别进行TSI计算, 所得ROI的形态及其中的热应变分布有很好的一致性, 而噪声分布差异较大。基于此对ROI与噪声区域进行区分, 通过多频段TSI的复合实现降噪。最后, 对降噪后的TSI图像, 借助径向梯度指标实现ROI的识别。基于数值仿真和离体猪脂肪加热实验, 对所建立的方法进行了验证, 仿真中ROI热应变成像的对比度提升了1.7 dB, 实验中提升了13.6 dB。Abstract: In the two-dimensional results in ultrasound thermal strain imaging (TSI), intense noise may appear near (in the direction away from the probe) the heating region due to the thermo-acoustic lens effect and peak mislocalization in the cross-correlation function, making identification of the region of interest (ROI) a challenging task. An integrated TSI method based on frequency domain segmentation is proposed to achieve noise reduction in TSI images and optimize the selection of the ROIs. Firstly, ultrasonic RF data during the heating process is divided into multiple frequency bands, while data in each band is treated as the result of an independent imaging process. Then, TSI calculations are carried out in each band. The morphologies of ROIs and thermal spots are similar for different bands, while the noise distribution varies. Based on this observation, the ROI can be easily distinguished from the noise region, and the integration of multi-band TSI realizes noise reduction. Finally, the radial gradient index is used to identify the ROI. The proposed method is verified via numerical simulations and in vitro experiments. The contrast in the ROI of the accumulated thermal strain image is raised by 1.7 dB in the simulation and by 13.6 dB in the experiment.