Xiying Xue, Dongjiang Ji, Chunyu Xu, Yuqing Zhao, Yimin Li, Chunhong Hu
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引用次数: 0
摘要
背景:低剂量计算机断层扫描(CT)成功地减少了患者的辐射暴露。然而,在低剂量 CT 中使用稀疏角度采样重建往往会导致重建图像中出现严重的条纹伪影:为了解决这一问题并保留图像边缘细节,本研究提出了一种带有核回归的自适应正交方向总变异方法:方法:首先通过核回归处理 CT 重建图像,得到 N 项泰勒级数,作为回归函数的局部表示。通过将数列扩展到二阶,我们得到了所需的回归函数估计值以及一阶导数和二阶导数的局部信息。为了减轻噪声对这些导数的影响,我们再次执行核回归来更新一阶和二阶导数。随后,使用加权方案将原始重建图像、其局部近似值和更新导数相加,得出用于计算方向信息的图像。为了进一步去除条纹伪影,研究引入了自适应正交方向总变化(AODTV)方法,该方法以先前获得的方向为指导,沿边缘方向和法线方向进行去噪:结果:模拟和实际实验都取得了良好的效果。两次实际实验结果表明,所提方法获得的 PSNR 值分别为 34.5408 dB 和 29.4634 dB,比对比度去噪算法分别高出 1.2392-5.9333 dB 和 2.828-6.7995 dB,表明所提方法具有良好的去噪性能:该研究证明了该方法在消除条纹伪影和保留图像细节方面的有效性。
Adaptive orthogonal directional total variation with kernel regression for CT image denoising.
Background: Low-dose computed tomography (CT) has been successful in reducing radiation exposure for patients. However, the use of reconstructions from sparse angle sampling in low-dose CT often leads to severe streak artifacts in the reconstructed images.
Objective: In order to address this issue and preserve image edge details, this study proposes an adaptive orthogonal directional total variation method with kernel regression.
Methods: The CT reconstructed images are initially processed through kernel regression to obtain the N-term Taylor series, which serves as a local representation of the regression function. By expanding the series to the second order, we obtain the desired estimate of the regression function and localized information on the first and second derivatives. To mitigate the noise impact on these derivatives, kernel regression is performed again to update the first and second derivatives. Subsequently, the original reconstructed image, its local approximation, and the updated derivatives are summed using a weighting scheme to derive the image used for calculating orientation information. For further removal of stripe artifacts, the study introduces the adaptive orthogonal directional total variation (AODTV) method, which denoises along both the edge direction and the normal direction, guided by the previously obtained orientation.
Results: Both simulation and real experiments have obtained good results. The results of two real experiments show that the proposed method has obtained PSNR values of 34.5408 dB and 29.4634 dB, which are 1.2392-5.9333 dB and 2.828-6.7995 dB higher than the contrast denoising algorithm, respectively, indicating that the proposed method has good denoising performance.
Conclusions: The study demonstrates the effectiveness of the method in eliminating strip artifacts and preserving the fine details of the images.
期刊介绍:
Research areas within the scope of the journal include:
Interaction of x-rays with matter: x-ray phenomena, biological effects of radiation, radiation safety and optical constants
X-ray sources: x-rays from synchrotrons, x-ray lasers, plasmas, and other sources, conventional or unconventional
Optical elements: grazing incidence optics, multilayer mirrors, zone plates, gratings, other diffraction optics
Optical instruments: interferometers, spectrometers, microscopes, telescopes, microprobes