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

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 fi...