ABSTRACT
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.
ABSTRACT
This paper proposes a technique to denoise the worm artifacts of elastogram using 2-D wavelet shrinkage denoising method. Firstly, strain estimate matrix including worm artifacts was decomposed to 3 levels by 2-D discrete wavelet transform with Sym8 wavelet function, and the thresholds were obtained using Birg6-Massart algorithm. Secondly, all the high frequency coefficients on different levels were quantized by using hard threshold and soft threshold function. Finally, the strain estimate matrix was reconstructed by using the 3rd layer low frequency coefficients and other layer quantized high frequency coefficients. The simulation results illustrated that the present technique could efficiently denoise the worm artifacts, enhance the elastogram performance indices, such as elastographic signal-to-noise ratio (SNRe) and elastographic contrast-to-noise ratio (CNRe), and could increase the correlation coefficient between the denoised elastogram and the ideal elastogram. In comparison with 2-D low-pass filtering, it could also obtain the higher elastographic SNRe and CNRe, and have clearer hard lesion edge. In addition, the results demonstrated that the proposed technique could suppress worm artifacts of elastograms for various applied strains. This work showed that the 2-D wavelet shrinkage denoising could efficiently denoise the worm artifacts of elastogram and enhance the performance of elastogram.
Subject(s)
Humans , Algorithms , Artifacts , Elasticity Imaging Techniques , Methods , Image Processing, Computer-Assisted , Wavelet AnalysisABSTRACT
Objective No reports has been found to date on whether frequency compounding can improve elastographic image signal to noise ratio (SNRe) and how it affects elastogram performance.In this paper simulations investigation was carried out on transmit-side frequency compounding (TSFC)for elastography.Methods 50 mm×50 mm tissue model was simulated with two round hard inclusions of 10mm diameter uniformly distributed along the tissue central axial line,and their elasticity modulus were 10 times of the background.Then simulation of 3.5 MHz、5 MHz and 7.5 MHz probes were introduced to form compression elastography of the double-lesion model by quasi-static compression method (applied strain 1%).Then,sub-elastograms obtained by the combination of 3.5 MHz and 5 MHz,3.5 MHz and 5 MHz,3.5 MHz and 7.5 MHz were compounded,respectively.Results Before compounding,signal to noise ratio (SNRe) of the various sub-elastograms were 8.42,9.62,10.73,respectively,contrast to noise ratio (CNRe) were 11.35,14.82,18.37,respectively and axial resolutions were 9.83,9.82,9.81.After compounding elastograms,the SNRe were 11.82,13.05,19.45,CNRe were 22.31,27.63,56.12,while axial resolutions were 9.83,9.83,9.83.Conclusion Frequency compounding elastograms have higher SNRe and CNRe than any sub-elastogram before compounding and have no axial resolution loss.The TSFC can improve elastogram performance efficiently and frequency compounding for elastography enhancement is feasible.