TY - JOUR
T1 - Denoising high angular resolution diffusion imaging data by combining singular value decomposition and non-local means filter
AU - Wu, Zhanxiong
AU - Potter, Thomas
AU - Wu, Dongnan
AU - Zhang, Yingchun
N1 - Funding Information:
The research is supported in part by Natural Science Foundation of Zhejiang Province ( LY17E070007 ), National Natural Science Foundation of China ( 51207038 ), and the University of Houston . Synthetic datasets were acquired from the project of HARDI reconstruction challenge. Real HARDI datasets were provided by the Human Connectome Project and WU-Minn Consortium.
Publisher Copyright:
© 2018 Elsevier B.V.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2019/1/15
Y1 - 2019/1/15
N2 - Background: High angular resolution diffusion imaging (HARDI) data is typically corrupted with Rician noise. Although larger b-values help to retrieve more accurate angular diffusivity information, they also lead to an increase in noise generation. New method: In order to sufficiently reduce noise in HARDI images and improve the construction of orientation distribution function (ODF) fields, a novel denoising method was developed in this study by combining the singular value decomposition (SVD) and non-local means (NLM) filter. Similar 3D patches were first recruited into a matrix from a search volume. HARDI signals in the matrix were then re-estimated using the SVD low rank approximation, and a NLM filter was employed to filter out any residual noise. Results: The performance of the proposed method was evaluated against the state-of-the-art denoising methods based on both synthetic and real HARDI datasets. Results demonstrated the superior performance of the developed SVD-NLM method in denoising HARDI data through preserving fine angular structural details and estimating diffusion orientations from improved ODF fields. Conclusion: The proposed SVD-NLM method can improve HARDI quantitative computations, such as MRI brain tissue segmentation and diffusion profile estimation, that rely on the quality of imaging data.
AB - Background: High angular resolution diffusion imaging (HARDI) data is typically corrupted with Rician noise. Although larger b-values help to retrieve more accurate angular diffusivity information, they also lead to an increase in noise generation. New method: In order to sufficiently reduce noise in HARDI images and improve the construction of orientation distribution function (ODF) fields, a novel denoising method was developed in this study by combining the singular value decomposition (SVD) and non-local means (NLM) filter. Similar 3D patches were first recruited into a matrix from a search volume. HARDI signals in the matrix were then re-estimated using the SVD low rank approximation, and a NLM filter was employed to filter out any residual noise. Results: The performance of the proposed method was evaluated against the state-of-the-art denoising methods based on both synthetic and real HARDI datasets. Results demonstrated the superior performance of the developed SVD-NLM method in denoising HARDI data through preserving fine angular structural details and estimating diffusion orientations from improved ODF fields. Conclusion: The proposed SVD-NLM method can improve HARDI quantitative computations, such as MRI brain tissue segmentation and diffusion profile estimation, that rely on the quality of imaging data.
KW - High angular resolution diffusion imaging
KW - Non-local means filter
KW - Orientation distribution function
KW - Singular value decomposition
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U2 - 10.1016/j.jneumeth.2018.11.020
DO - 10.1016/j.jneumeth.2018.11.020
M3 - Article
C2 - 30472071
AN - SCOPUS:85057326318
VL - 312
SP - 105
EP - 113
JO - Journal of Neuroscience Methods
JF - Journal of Neuroscience Methods
SN - 0165-0270
ER -