Denoise magnitude diffusion magnetic resonance images via variance-stabilizing transformation and optimal singular-value manipulation

• Published in: NeuroImage (Orlando, Fla.) Vol. 215; p. 116852
• Main Authors: Ma, Xiaodong, Uğurbil, Kâmil, Wu, Xiaoping
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.07.2020; Elsevier Limited; Elsevier
• Subjects: Algorithms; Brain - diagnostic imaging; Brain - physiology; Computer Simulation - standards; Denoising; Diffusion Magnetic Resonance Imaging - methods; Diffusion Magnetic Resonance Imaging - standards; Diffusion MRI; Gene expression; High field MRI; Human connectome project; Humans; Image Enhancement - methods; Image Enhancement - standards; Image Processing, Computer-Assisted - methods; Image Processing, Computer-Assisted - standards; Imaging, Three-Dimensional - methods; Imaging, Three-Dimensional - standards; Magnetic resonance imaging; Methods; Neuroimaging; Noise; Parameter estimation; Principal components analysis; Signal-To-Noise Ratio; Simultaneous multi-slice; Singular value decomposition; Spatial discrimination; Therapeutic applications; Ultrastructure; Variance stabilizing transformation; Wavelet transforms; Variance stabilizing transformation; Human connectome project; Diffusion MRI; Simultaneous multi-slice; Denoising; Singular value decomposition; High field MRI
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2020.116852

Although shown to have a great utility for a wide range of neuroscientific and clinical applications, diffusion-weighted magnetic resonance imaging (dMRI) faces a major challenge of low signal-to-noise ratio (SNR), especially when pushing the spatial resolution for improved delineation of brain’s fine structure or increasing the diffusion weighting for increased angular contrast or both. Here, we introduce a comprehensive denoising framework for denoising magnitude dMRI. The framework synergistically combines the variance stabilizing transform (VST) with optimal singular value manipulation. The purpose of VST is to transform the Rician data to Gaussian-like data so that an asymptotically optimal singular value manipulation strategy tailored for Gaussian data can be used. The output of the framework is the estimated underlying diffusion signal for each voxel in the image domain. The usefulness of the proposed framework for denoising magnitude dMRI is demonstrated using both simulation and real-data experiments. Our results show that the proposed denoising framework can significantly improve SNR across the entire brain, leading to substantially enhanced performances for estimating diffusion tensor related indices and for resolving crossing fibers when compared to another competing method. More encouragingly, the proposed method when used to denoise a single average of 7 ​Tesla Human Connectome Project-style diffusion acquisition provided comparable performances relative to those achievable with ten averages for resolving multiple fiber populations across the brain. As such, the proposed denoising method is expected to have a great utility for high-quality, high-resolution whole-brain dMRI, desirable for many neuroscientific and clinical applications. •We propose a framework for denoising magnitude dMRI that combines VST with optimal singular value manipulation.•The effectiveness of the proposed denoising method is demonstrated by using both simulation and real-data experiments.•The proposed denoising framework can effectively minimize Rician bias, improving DTI and crossing fiber estimation.•The proposed method can outperform the MPPCA approach, a state-of-the-art method in denoising magnitude diffusion images.