一种用于磁共振（MR）图像的小波多尺度去噪算法。
A wavelet multiscale denoising algorithm for magnetic resonance (MR) images.
作者信息
Yang Xiaofeng, Fei Baowei
机构信息
Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, People's Republic of China ; Department of Radiology, Emory University, Atlanta, GA 30329, USA.
出版信息
Meas Sci Technol. 2011 Feb 1;22(2):25803. doi: 10.1088/0957-0233/22/2/025803.
Based on the Radon transform, a wavelet multiscale denoising method is proposed for MR images. The approach explicitly accounts for the Rician nature of MR data. Based on noise statistics we apply the Radon transform to the original MR images and use the Gaussian noise model to process the MR sinogram image. A translation invariant wavelet transform is employed to decompose the MR 'sinogram' into multiscales in order to effectively denoise the images. Based on the nature of Rician noise we estimate noise variance in different scales. For the final denoised sinogram we apply the inverse Radon transform in order to reconstruct the original MR images. Phantom, simulation brain MR images, and human brain MR images were used to validate our method. The experiment results show the superiority of the proposed scheme over the traditional methods. Our method can reduce Rician noise while preserving the key image details and features. The wavelet denoising method can have wide applications in MRI as well as other imaging modalities.
基于拉东变换，提出了一种用于磁共振图像的小波多尺度去噪方法。该方法明确考虑了磁共振数据的莱斯分布特性。基于噪声统计，我们将拉东变换应用于原始磁共振图像，并使用高斯噪声模型处理磁共振正弦图图像。采用平移不变小波变换将磁共振“正弦图”分解为多尺度，以便有效地对图像进行去噪。基于莱斯噪声的特性，我们估计不同尺度下的噪声方差。对于最终去噪后的正弦图，我们应用逆拉东变换来重建原始磁共振图像。使用体模、模拟脑磁共振图像和人脑磁共振图像来验证我们的方法。实验结果表明，所提出的方案优于传统方法。我们的方法可以在保留关键图像细节和特征的同时降低莱斯噪声。小波去噪方法在磁共振成像以及其他成像模态中具有广泛的应用。
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