Hu Kai, Cheng Qiaocui, Gao Xieping
The MOE Key Laboratory of Intelligent Computing and Information Processing, Xiangtan University, Xiangtan, 411105, China; College of Information Engineering, Xiangtan University, Xiangtan, 411105, China.
The MOE Key Laboratory of Intelligent Computing and Information Processing, Xiangtan University, Xiangtan, 411105, China; College of Information Engineering, Xiangtan University, Xiangtan, 411105, China.
Magn Reson Imaging. 2016 Oct;34(8):1128-40. doi: 10.1016/j.mri.2016.05.011. Epub 2016 May 26.
Magnetic resonance (MR) images are affected by random noises, which degrade many image processing and analysis tasks. It has been shown that the noise in magnitude MR images follows a Rician distribution. Unlike additive Gaussian noise, the noise is signal-dependent, and consequently difficult to reduce, especially in low signal-to-noise ratio (SNR) images. Wirestam et al. in [20] proposed a Wiener-like filtering technique in wavelet-domain to reduce noise before construction of the magnitude MR image. Based on Wirestam's study, we propose a wavelet-domain translation-invariant (TI) Wiener-like filtering algorithm for noise reduction in complex MR data. The proposed denoising algorithm shows the following improvements compared with Wirestam's method: (1) we introduce TI property into the Wiener-like filtering in wavelet-domain to suppress artifacts caused by translations of the signal; (2) we integrate one Stein's Unbiased Risk Estimator (SURE) thresholding with two Wiener-like filters to make the hard-thresholding scale adaptive; and (3) the first Wiener-like filtering is used to filter the original noisy image in which the noise obeys Gaussian distribution and it provides more reasonable results. The proposed algorithm is applied to denoise the real and imaginary parts of complex MR images. To evaluate our proposed algorithm, we conduct extensive denoising experiments using T1-weighted simulated MR images, diffusion-weighted (DW) phantom and in vivo data. We compare our algorithm with other popular denoising methods. The results demonstrate that our algorithm outperforms others in term of both efficiency and robustness.
磁共振(MR)图像会受到随机噪声的影响,这会降低许多图像处理和分析任务的效果。研究表明,幅度MR图像中的噪声服从莱斯分布。与加性高斯噪声不同,这种噪声与信号相关,因此难以降低,尤其是在低信噪比(SNR)图像中。Wirestam等人在[20]中提出了一种小波域中的类维纳滤波技术,用于在构建幅度MR图像之前降低噪声。基于Wirestam的研究,我们提出了一种用于复MR数据降噪的小波域平移不变(TI)类维纳滤波算法。与Wirestam的方法相比,所提出的去噪算法有以下改进:(1)我们将TI特性引入小波域中的类维纳滤波,以抑制信号平移引起的伪影;(2)我们将一个斯坦无偏风险估计器(SURE)阈值处理与两个类维纳滤波器相结合,使硬阈值处理具有尺度适应性;(3)第一个类维纳滤波用于对噪声服从高斯分布的原始噪声图像进行滤波,它提供了更合理的结果。所提出的算法应用于对复MR图像的实部和虚部进行去噪。为了评估我们提出的算法,我们使用T1加权模拟MR图像、扩散加权(DW)体模和体内数据进行了广泛的去噪实验。我们将我们的算法与其他流行的去噪方法进行了比较。结果表明,我们的算法在效率和鲁棒性方面均优于其他算法。