高重叠率下弹性成像应变估计值的小波去噪研究

Wavelet de-noising of strain estimates in elastography at high overlap

目的高的数据窗重叠率是提高弹性成像轴向分辨率的必要条件，但重叠率的增加会使位移估计的相关误差急剧增长，产生所谓的“蠕虫”噪声。本研究使用小波收缩法去除高重叠率下弹性图像蠕虫噪声。方法对每一条轴向应变A-line先进行3级离散小波分解，然后根据4种自适应阈值之一使用软阈值函数对每一层小波高频系数进行量化，最后进行小波重构产生去噪后的应变A—Line。结果仿真结果表明提出的技术能有效去除蠕虫噪声，增强弹性图像的信噪比（SNRe）和对比度噪声比（CNRe）；与低通滤波相比，使用小波去噪产生的弹性图像更接近于理想弹性图（有更高的相关系数）；另外，仿真结果也显示小波去噪应用于应变估计值比应用于位移估计值能获得更好的图像质量参数；弹性体模实验结果也表明该技术能有效改进应变图像性能。结论小波收缩去噪技术能有效地去除弹性图像的蠕虫噪声，在保持高的轴向分辨率的情况下提高弹性图像的性能。

Object High overlap of data window is essential to improve axial resolution in elastogaphy. However, correlated errors in displacement estimates increase dramatically with the increase of the overlap, and generate the so-called ＂worm＂ artifacts. This paper presents a wavelet shrinkage de-noising in strain estimates to reduce the worm artifacts at high overlap. Methods Each of axial strain A-lines was decomposed using discrete wavelet transformation up to 3 levels. The high frequency components of every levels of wavelet coefficients were quantified by using soft threshold function according to different adaptive thresholds. Then the discrete wavelet reconstruction were performed to produce a wavelet shrinkage denoised strain line. Results The simulation results illustrated that the presented technique could efficiently denoise worm artifacts and enhance the elastogram performance indices such as elastographic SNRe and CNRe. Elastogram obtained by wavelet de- noising had the closest correspondence with ideal strain image. In addition, the results also demonstrated that wavelet shrinkage de-noising applied in strain estimates could obtain better image quality parameters than that applied in displacement estimates. The elastic phantom experiments also showed the similar elastogram performance improvement. Conclusion Wavelet shrinkage de-noising can efficiently denoise the worm artifacts noise of elastogram and improve the performance indices of elastogram while maintaining the high axial resolution.