Introduction: Computed tomography (CT) measurements of bone mineral attenuation may be a useful means to identify older women who should be prioritized for bone mineral density screening. Methods: We compared bone min...Introduction: Computed tomography (CT) measurements of bone mineral attenuation may be a useful means to identify older women who should be prioritized for bone mineral density screening. Methods: We compared bone mineral attenuation as measured in the L1 vertebra of CT studies to the results of dual-energy x-ray absorptiometry (DEXA) studies to determine what CT attenuation thresholds might yield a reasonable combination of sensitivity and specificity for the detection of osteoporosis. The study was limited to women between the ages of 65 and 75 years who had a DEXA study and a CT that included the L1 or adjacent vertebra performed within 3 years of the DEXA study. Results: There were 1226 women in this study, of whom 452 (38%) had osteoporosis based on a T-score ≤ −2.5 by DEXA. There were 830 CT studies performed with contrast and 396 studies which were performed without contrast. There was a statistically significant difference in the mean HU of those studies performed without contrast compared to those with contrast (unenhanced mean 103 HU versus 125 HU, p < 0.001). Different CT attenuation thresholds provided the most appropriate combination of sensitivity and specificity for the detection of osteoporosis when comparing CT studies performed without or with IV contrast and when all the CT data were used in aggregate. Conclusion: Different thresholds appear necessary when using the mean CT vertebral attenuation to identify older women for preferential referral for DEXA studies.展开更多
In this paper,we explore the use of iterative curvelet thresholding for seismic random noise attenuation.A new method for combining the curvelet transform with iterative thresholding to suppress random noise is demons...In this paper,we explore the use of iterative curvelet thresholding for seismic random noise attenuation.A new method for combining the curvelet transform with iterative thresholding to suppress random noise is demonstrated and the issue is described as a linear inverse optimal problem using the L1 norm.Random noise suppression in seismic data is transformed into an L1 norm optimization problem based on the curvelet sparsity transform. Compared to the conventional methods such as median filter algorithm,FX deconvolution, and wavelet thresholding,the results of synthetic and field data processing show that the iterative curvelet thresholding proposed in this paper can sufficiently improve signal to noise radio（SNR） and give higher signal fidelity at the same time.Furthermore,to make better use of the curvelet transform such as multiple scales and multiple directions,we control the curvelet direction of the result after iterative curvelet thresholding to further improve the SNR.展开更多
Signal extraction is critical in GRP data processing and noise attenuation. When the target depth is shallow, its refl ection echo signal will overlap with the background noise, affecting the detection of arrival time...Signal extraction is critical in GRP data processing and noise attenuation. When the target depth is shallow, its refl ection echo signal will overlap with the background noise, affecting the detection of arrival time and localization of the target. Thus, we propose a noise attenuation method based on the curvelet transform. First, the original signal is transformed into the curvelet domain, and then the curvelet coefficients of the background noise are extracted according to the distribution features that differ from the effective signal. In the curvelet domain, the coarse-scale curvelet atom is isotropic. Hence, a two-dimensional directional filter is designed to estimate the high-energy background noise in the coarsescale domain, and then, attenuate the background noise and highlight the effective signal. In this process, we also use a subscale threshold value of the curvelet domain to fi lter out random noise. Finally, we compare the proposed method with the average elimination and 2D continuous wavelet transform methods. The results show that the proposed method not only removes the background noise but also eliminates the coherent interference and random noise. The numerical simulation and the real data application suggest and verify the feasibility and effectiveness of the proposed method.展开更多
针对滚动轴承早期故障声发射信号受复杂传递路径和噪声的干扰,声发射信号信噪比较低,导致轴承故障特征难以提取的问题,提出了改进小波阈值函数-ACEWT的轴承故障特征提取方法。由于声发射信号呈冲击性与快速衰减的特点,构建一种衰减正弦...针对滚动轴承早期故障声发射信号受复杂传递路径和噪声的干扰,声发射信号信噪比较低,导致轴承故障特征难以提取的问题,提出了改进小波阈值函数-ACEWT的轴承故障特征提取方法。由于声发射信号呈冲击性与快速衰减的特点,构建一种衰减正弦型与指数型的小波阈值函数对低信噪比的声发射信号进行降噪。研究自相关运算与经验小波变换结合的方法(autocorrelation and empirical wavelet transform,ACEWT),用于滚动轴承故障声发射信号特征提取,解决了在低信噪比下经验小波变换对轴承故障特征提取的不足;引入经验小波能量比-熵指标,选取最优经验小波系数。通过与经验小波变换、改进小波阈值函数-EWT和MCKD-EWT方法进行对比研究,并试验验证。仿真和试验结果表明,所提方法明显优于经验小波变换、改进小波阈值函数-EWT和MCKD-EWT方法,可准确提取轴承故障声发射信号的频率特征。展开更多
文摘Introduction: Computed tomography (CT) measurements of bone mineral attenuation may be a useful means to identify older women who should be prioritized for bone mineral density screening. Methods: We compared bone mineral attenuation as measured in the L1 vertebra of CT studies to the results of dual-energy x-ray absorptiometry (DEXA) studies to determine what CT attenuation thresholds might yield a reasonable combination of sensitivity and specificity for the detection of osteoporosis. The study was limited to women between the ages of 65 and 75 years who had a DEXA study and a CT that included the L1 or adjacent vertebra performed within 3 years of the DEXA study. Results: There were 1226 women in this study, of whom 452 (38%) had osteoporosis based on a T-score ≤ −2.5 by DEXA. There were 830 CT studies performed with contrast and 396 studies which were performed without contrast. There was a statistically significant difference in the mean HU of those studies performed without contrast compared to those with contrast (unenhanced mean 103 HU versus 125 HU, p < 0.001). Different CT attenuation thresholds provided the most appropriate combination of sensitivity and specificity for the detection of osteoporosis when comparing CT studies performed without or with IV contrast and when all the CT data were used in aggregate. Conclusion: Different thresholds appear necessary when using the mean CT vertebral attenuation to identify older women for preferential referral for DEXA studies.
基金the National Science & Technology Major Projects(Grant No.2008ZX05023-005-013).
文摘In this paper,we explore the use of iterative curvelet thresholding for seismic random noise attenuation.A new method for combining the curvelet transform with iterative thresholding to suppress random noise is demonstrated and the issue is described as a linear inverse optimal problem using the L1 norm.Random noise suppression in seismic data is transformed into an L1 norm optimization problem based on the curvelet sparsity transform. Compared to the conventional methods such as median filter algorithm,FX deconvolution, and wavelet thresholding,the results of synthetic and field data processing show that the iterative curvelet thresholding proposed in this paper can sufficiently improve signal to noise radio（SNR） and give higher signal fidelity at the same time.Furthermore,to make better use of the curvelet transform such as multiple scales and multiple directions,we control the curvelet direction of the result after iterative curvelet thresholding to further improve the SNR.
基金supported by the National Natural Science Foundation of China(No.41074089)Special Financial Grant from the China Postdoctoral Science Foundation(No.201104654)
文摘Signal extraction is critical in GRP data processing and noise attenuation. When the target depth is shallow, its refl ection echo signal will overlap with the background noise, affecting the detection of arrival time and localization of the target. Thus, we propose a noise attenuation method based on the curvelet transform. First, the original signal is transformed into the curvelet domain, and then the curvelet coefficients of the background noise are extracted according to the distribution features that differ from the effective signal. In the curvelet domain, the coarse-scale curvelet atom is isotropic. Hence, a two-dimensional directional filter is designed to estimate the high-energy background noise in the coarsescale domain, and then, attenuate the background noise and highlight the effective signal. In this process, we also use a subscale threshold value of the curvelet domain to fi lter out random noise. Finally, we compare the proposed method with the average elimination and 2D continuous wavelet transform methods. The results show that the proposed method not only removes the background noise but also eliminates the coherent interference and random noise. The numerical simulation and the real data application suggest and verify the feasibility and effectiveness of the proposed method.
文摘针对滚动轴承早期故障声发射信号受复杂传递路径和噪声的干扰,声发射信号信噪比较低,导致轴承故障特征难以提取的问题,提出了改进小波阈值函数-ACEWT的轴承故障特征提取方法。由于声发射信号呈冲击性与快速衰减的特点,构建一种衰减正弦型与指数型的小波阈值函数对低信噪比的声发射信号进行降噪。研究自相关运算与经验小波变换结合的方法(autocorrelation and empirical wavelet transform,ACEWT),用于滚动轴承故障声发射信号特征提取,解决了在低信噪比下经验小波变换对轴承故障特征提取的不足;引入经验小波能量比-熵指标,选取最优经验小波系数。通过与经验小波变换、改进小波阈值函数-EWT和MCKD-EWT方法进行对比研究,并试验验证。仿真和试验结果表明,所提方法明显优于经验小波变换、改进小波阈值函数-EWT和MCKD-EWT方法,可准确提取轴承故障声发射信号的频率特征。
