title: A wavelet multiscale denoising algorithm for magnetic resonance (MR) images.
abstract: 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.
authors.affiliation: 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.
authors.forename: Xiaofeng
authors.initials: X
authors.lastname: Yang
authors.forename: Baowei
authors.initials: B
authors.lastname: Fei
keywords: Radon transform; Rician distribution; magnetic resonance imaging (MRI); multiscale denoising; translation invariant; wavelet transform