A Joint Framework for Denoising and Estimating Diffusion Kurtosis Tensors Using Multiple Prior Information

• Published in: IEEE transactions on medical imaging 2022-02, Vol.41 (2), p.308-319
• Main Authors: Guo, Li, Lyu, Jian, Zhang, Zhe, Shi, Jinping, Feng, Qianjin, Feng, Yanqiu, Gao, Mingyong, Zhang, Xinyuan
• Format: Article
• Language: English
• Subjects: Brain - diagnostic imaging; Diffusion; Diffusion kurtosis imaging; Diffusion Magnetic Resonance Imaging - methods; Diffusion tensor imaging; Diffusion Tensor Imaging - methods; Estimation; Fitting; Information dissemination; Kurtosis; Magnetic resonance imaging; Mathematical analysis; Medical diagnosis; Medical diagnostic imaging; Noise; Noise reduction; noncentral χ-distribution noise; parameter estimation; prior information; Self-similarity; Signal to noise ratio; Smoothness; Spatial smoothing; Tensors
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2021.3112515

Diffusion kurtosis imaging (DKI) has been shown to be valuable in a wide range of neuroscientific and clinical applications. However, reliable estimation of DKI tensors is often compromised by noise, especially for the kurtosis tensor (KT). Here, we propose a joint denoising and estimating framework that integrates multiple sources of prior information, including nonlocal structural self-similarity (NSS), local spatial smoothness (LSS), physical relevance (PR) of the DKI model, and noise characteristics of magnitude diffusion MRI (dMRI) images for improved estimation of DKI tensors. The local and nonlocal spatial smoothing constraints are complementary to each other, making the proposed framework highly effective in reducing the noise fluctuations on DKI tensors, especially KT. As an additional refinement, we propose to impose a physically relevant constraint within our joint denoising and estimation framework. We further adopt the first-moment noise-corrected fitting model (M 1 NCM) to remove the noncentral {\chi } -distribution noise bias. The effectiveness of integrating multiple sources of priors into the joint framework is verified by comparing the proposed M 1 NCM-NSS-LSS-PR method with various versions of M 1 NCM-based estimators and two state-of-the-art methods. Results show that the proposed method outperformed the compared methods in simulations and in-vivo dMRI datasets of both spatially stationary and nonstationary noise distributions. The in-vivo experiments also show that the proposed M 1 NCM-NSS-LSS-PR method was robust to the number of diffusion directions.