Generalized total variation-based MRI Rician denoising model with spatially adaptive regularization parameters
Publication in refereed journal


摘要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. © 2014 Elsevier Inc.
著者Liu R.W., Shi L., Huang W., Xu J., Yu S.C.H., Wang D.
期刊名稱Magnetic Resonance Imaging
出版社Elsevier BV
頁次702 - 720
關鍵詞Diffusion tensor MRI (DT-MRI), Hyper-Laplacian prior, Image denoising, Magnetic resonance imaging (MRI), Rician distribution, Total variation

上次更新時間 2020-15-10 於 02:22