1. lop-DWI: A Novel Scheme for Pre-Processing of Diffusion-Weighted Images in the Gradient Direction Domain
- Author
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Farshid eSepehrband, Jeiran eChoupan, Emmanuel eCaruyer, Nyoman Dana Kurniawan, Yaniv eGal, Quang M Tieng, Katie eMcMahon, Viktor eVegh, David C Reutens, Zhengyi eYang, Centre for Advanced Imaging, University of Queensland [Brisbane], Queensland Brain Institute, Section for Biomedical Image Analysis (SBIA), Perelman School of Medicine, University of Pennsylvania [Philadelphia]-University of Pennsylvania [Philadelphia], and School of Information Technology and Electrical Engineering [Brisbane]
- Subjects
Computer science ,Noise reduction ,diffusion-weighted imaging ,computer.software_genre ,Signal ,Synthetic data ,lcsh:RC346-429 ,signal processing on the sphere ,diffusion MRI ,Sampling (signal processing) ,Voxel ,acquisition design ,HARDI ,Methods Article ,[INFO.INFO-IM]Computer Science [cs]/Medical Imaging ,Computer vision ,spiral sampling ,gradient direction domain ,local reconstruction ,Spiral ,lcsh:Neurology. Diseases of the nervous system ,pre-processing ,business.industry ,Orientation (computer vision) ,Diffusion Weighted Imaging ,filtering ,Neurology ,Neurology (clinical) ,Artificial intelligence ,Data mining ,business ,computer ,Diffusion MRI ,Neuroscience - Abstract
International audience; We describe and evaluate a pre-processing method based on a periodic spiral sampling of diffusion-gradient directions for high angular resolution diffusion magnetic resonance imaging. Our pre-processing method incorporates prior knowledge about the acquired diffusion-weighted signal, facilitating noise reduction. Periodic spiral sampling of gradient direction encodings results in an acquired signal in each voxel that is pseudo-periodic with characteristics that allow separation of low-frequency signal from high frequency noise. Consequently, it enhances local reconstruction of the orientation distribution function used to define fiber tracks in the brain. Denoising with periodic spiral sampling was tested using synthetic data and in vivo human brain images. The level of improvement in signal-to-noise ratio and in the accuracy of local reconstruction of fiber tracks was significantly improved using our method.
- Published
- 2015
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