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.

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.

Objective To investigate the changes of insulin resistance and islet β cell function in gout patients with different status of glucose metabolism, and to analyse metabolic features in gout patients with hyperglycemia. Methods Ninety-six patients with gout were consecutively enrolled into the study and were divided into normal glucose tolerance group ( NGT, n = 35 ) , impaired glucose regulation group ( IGR, n = 27 ) , and diabetic group (DM, n=34). Height, weight, blood pressure, fasting plasma glucose, fasting insulin, HbA1C,serum uric acid, C-reactive protein (CRP), total cholesterol (TC), and triglycerides were determined in all subjects. Body mass index (BMI), homeostasis model assessment for insulin resistance index (HOMA-IR),homeostasis model assessment for β cell function index (HOMA-B), and insulin sensitivity index (ISI) were calculated. Results Compared with the NGT group, the levels of BMI, 2hPG, fasting insulin, HbA1C,TC,triglycerides, CRP, HOMA-IR in the DM and IGR groups were higher while ISI was lower (0.023±0.018 and 0.024±0.017 vs 0. 052±0. 026, P<0.05 ). HOMA-B was significantly different among the three groups ( 87.6±25. 1,126.46±34. 2, and 173.75±32.1, P<0.05). Family history of diabetes was more commonly seen in DM group than NGT group ( 41.17% vs 11.4%, P< 0.05 ). Logistic analysis showed that age, BMI, systolic blood pres(s)ure, triglyceride, CRP, and ISI were independently associated with diabetes, but not with uric acid.Conclusions Severe insulin resistance, β cell dysfunction, increased BMI and CRP, lipid disorders, and hereditary susceptibility may be the main metabolic features of gout patients with hyperglycemia.