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lop-DWI: A Novel Scheme for Pre-Processing of Diffusion-Weighted Images in the Gradient Direction Domain

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 no... Full description

Journal Title: Frontiers in neurology 2014, Vol.5, pp.290
Main Author: Sepehrband, Farshid
Other Authors: Choupan, Jeiran , Caruyer, Emmanuel , Kurniawan, Nyoman D , Gal, Yaniv , Tieng, Quang M , Mcmahon, Katie L , Vegh, Viktor , Reutens, David C , Yang, Zhengyi
Format: Electronic Article Electronic Article
Language: English
Subjects:
ID: ISSN: 1664-2295 ; PMID: 25628600 Version:1 ; DOI: 10.3389/fneur.2014.00290
Link: http://pubmed.gov/25628600
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recordid: medline25628600
title: lop-DWI: A Novel Scheme for Pre-Processing of Diffusion-Weighted Images in the Gradient Direction Domain
format: Article
creator:
  • Sepehrband, Farshid
  • Choupan, Jeiran
  • Caruyer, Emmanuel
  • Kurniawan, Nyoman D
  • Gal, Yaniv
  • Tieng, Quang M
  • Mcmahon, Katie L
  • Vegh, Viktor
  • Reutens, David C
  • Yang, Zhengyi
subjects:
  • Hardi
  • Diffusion-Weighted Imaging
  • Gradient Direction Domain
  • Local Reconstruction
  • Pre-Processing
  • Spiral Sampling
ispartof: Frontiers in neurology, 2014, Vol.5, pp.290
description: 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.
language: eng
source:
identifier: ISSN: 1664-2295 ; PMID: 25628600 Version:1 ; DOI: 10.3389/fneur.2014.00290
fulltext: fulltext
issn:
  • 16642295
  • 1664-2295
url: Link


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titlelop-DWI: A Novel Scheme for Pre-Processing of Diffusion-Weighted Images in the Gradient Direction Domain
creatorSepehrband, Farshid ; Choupan, Jeiran ; Caruyer, Emmanuel ; Kurniawan, Nyoman D ; Gal, Yaniv ; Tieng, Quang M ; Mcmahon, Katie L ; Vegh, Viktor ; Reutens, David C ; Yang, Zhengyi
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subjectHardi ; Diffusion-Weighted Imaging ; Gradient Direction Domain ; Local Reconstruction ; Pre-Processing ; Spiral Sampling
descriptionWe 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.
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abstractWe 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.
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pmid25628600
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