title: Generalized total variation-based MRI Rician denoising model with spatially adaptive regularization parameters.
abstract: Magnetic resonance imaging (MRI) is an outstanding medical imaging modality but the quality often suffers from noise pollution during image acquisition and transmission. The purpose of this study is to enhance image quality using feature-preserving denoising method. In current literature, most existing MRI denoising methods did not simultaneously take the global image prior and local image features into account. The denoising method proposed in this paper is implemented based on an assumption of spatially varying Rician noise map. A two-step wavelet-domain estimation method is developed to extract the noise map. Following a Bayesian modeling approach, a generalized total variation-based MRI denoising model is proposed based on global hyper-Laplacian prior and Rician noise assumption. The proposed model has the properties of backward diffusion in local normal directions and forward diffusion in local tangent directions. To further improve the denoising performance, a local variance estimator-based method is introduced to calculate the spatially adaptive regularization parameters related to local image features and spatially varying noise map. The main benefit of the proposed method is that it takes full advantage of the global MR image prior and local image features. Numerous experiments have been conducted on both synthetic and real MR data sets to compare our proposed model with some state-of-the-art denoising methods. The experimental results have demonstrated the superior performance of our proposed model in terms of quantitative and qualitative image quality evaluations.
authors.affiliation: Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, P.R. China; Research Center for Medical Image Computing, The Chinese University of Hong Kong Shatin, New Territories, Hong Kong SAR, P.R. China.
authors.forename: Ryan Wen
authors.initials: RW
authors.lastname: Liu
authors.affiliation: Research Center for Medical Image Computing, The Chinese University of Hong Kong Shatin, New Territories, Hong Kong SAR, P.R. China; Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, P.R. China. Electronic address: shilin@cuhk.edu.hk.
authors.forename: Lin
authors.initials: L
authors.lastname: Shi
authors.affiliation: Department of Biomedical Engineering and Shun Hing Institute of Advanced Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, P.R. China. Electronic address: hwh@fimmu.com.
authors.forename: Wenhua
authors.initials: W
authors.lastname: Huang
authors.affiliation: Department of Biomedical Engineering and Shun Hing Institute of Advanced Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, P.R. China.
authors.forename: Jing
authors.initials: J
authors.lastname: Xu
authors.affiliation: Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, P.R. China.
authors.forename: Simon Chun Ho
authors.initials: SC
authors.lastname: Yu
authors.affiliation: Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, P.R. China; Research Center for Medical Image Computing, The Chinese University of Hong Kong Shatin, New Territories, Hong Kong SAR, P.R. China; CUHK Shenzhen Research Institute, Shenzhen, Guangdong, P.R. China; Institute of Clinical Anatomy, Southern Medical University, Guangzhou, Guangdong, P.R. China.
authors.forename: Defeng
authors.initials: D
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D016880:Anisotropy; D016477:Artifacts; D001499:Bayes Theorem; D001921:Brain; D056324:Diffusion Tensor Imaging; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging
keywords: Diffusion tensor MRI (DT-MRI); Hyper-Laplacian prior; Image denoising; Magnetic resonance imaging (MRI); Rician distribution; Total variation