CAJ | 학술논문

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.
목적 고적수거창중첩솔시제고탄성성상축향분변솔적필요조건,단중첩솔적증가회사위이고계적상관오차급극증장,산생소위적"연충"조성.본연구사용소파수축법거제고중첩솔하탄성도상연충조성.방법 대매일조축향응변A-line선진행3급리산소파분해,연후근거4충자괄응역치지일사용연역치함수대매일층소파고빈계수진행양화,최후진행소파중구산생거조후적응변A-line.결과 방진결과표명제출적기술능유효거제연충조성,증강탄성도상적신조비(SNRe)화대비도조성비(CNRe);여저통려파상비,사용소파거조산생적탄성도상경접근우이상탄성도(유경고적상관계수);령외,방진결과야현시소파거조응용우응변고계치비응용우위이고계치능획득경호적도상질량삼수;탄성체모실험결과야표명해기술능유효개진응변도상성능.결론 소파수축거조기술능유효지거제탄성도상적연충조성,재보지고적축향분변솔적정황하제고탄성도상적성능.
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 denoising 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 apphed 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.