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Sepehrband F, Choupan J, Caruyer E, Kurniawan ND, Gal Y, Tieng QM, McMahon KL, Vegh V, Reutens DC, Yang Z. lop-DWI: A Novel Scheme for Pre-Processing of Diffusion-Weighted Images in the Gradient Direction Domain. Front Neurol 2015; 5:290. [PMID: 25628600 PMCID: PMC4290594 DOI: 10.3389/fneur.2014.00290] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2014] [Accepted: 12/21/2014] [Indexed: 12/13/2022] Open
Abstract
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.
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Affiliation(s)
- Farshid Sepehrband
- Centre for Advanced Imaging, The University of Queensland , Brisbane, QLD , Australia ; Queensland Brain Institute, The University of Queensland , Brisbane, QLD , Australia
| | - Jeiran Choupan
- Centre for Advanced Imaging, The University of Queensland , Brisbane, QLD , Australia ; Queensland Brain Institute, The University of Queensland , Brisbane, QLD , Australia
| | - Emmanuel Caruyer
- Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania , Philadelphia, PA , USA
| | - Nyoman D Kurniawan
- Centre for Advanced Imaging, The University of Queensland , Brisbane, QLD , Australia
| | - Yaniv Gal
- School of Information Technology and Electrical Engineering, The University of Queensland , Brisbane, QLD , Australia
| | - Quang M Tieng
- Centre for Advanced Imaging, The University of Queensland , Brisbane, QLD , Australia
| | - Katie L McMahon
- Centre for Advanced Imaging, The University of Queensland , Brisbane, QLD , Australia
| | - Viktor Vegh
- Centre for Advanced Imaging, The University of Queensland , Brisbane, QLD , Australia
| | - David C Reutens
- Centre for Advanced Imaging, The University of Queensland , Brisbane, QLD , Australia
| | - Zhengyi Yang
- Centre for Advanced Imaging, The University of Queensland , Brisbane, QLD , Australia ; School of Information Technology and Electrical Engineering, The University of Queensland , Brisbane, QLD , Australia
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Nir TM, Villalon-Reina JE, Prasad G, Jahanshad N, Joshi SH, Toga AW, Bernstein MA, Jack CR, Weiner MW, Thompson PM. Diffusion weighted imaging-based maximum density path analysis and classification of Alzheimer's disease. Neurobiol Aging 2015; 36 Suppl 1:S132-40. [PMID: 25444597 PMCID: PMC4283487 DOI: 10.1016/j.neurobiolaging.2014.05.037] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2013] [Revised: 05/13/2014] [Accepted: 05/13/2014] [Indexed: 10/24/2022]
Abstract
Characterizing brain changes in Alzheimer's disease (AD) is important for patient prognosis and for assessing brain deterioration in clinical trials. In this diffusion weighted imaging study, we used a new fiber-tract modeling method to investigate white matter integrity in 50 elderly controls (CTL), 113 people with mild cognitive impairment, and 37 AD patients. After clustering tractography using a region-of-interest atlas, we used a shortest path graph search through each bundle's fiber density map to derive maximum density paths (MDPs), which we registered across subjects. We calculated the fractional anisotropy (FA) and mean diffusivity (MD) along all MDPs and found significant MD and FA differences between AD patients and CTL subjects, as well as MD differences between CTL and late mild cognitive impairment subjects. MD and FA were also associated with widely used clinical scores. As an MDP is a compact low-dimensional representation of white matter organization, we tested the utility of diffusion tensor imaging measures along these MDPs as features for support vector machine based classification of AD.
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Affiliation(s)
- Talia M Nir
- Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Los Angeles, CA, USA
| | - Julio E Villalon-Reina
- Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Los Angeles, CA, USA
| | - Gautam Prasad
- Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Los Angeles, CA, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Los Angeles, CA, USA
| | - Shantanu H Joshi
- Department of Neurology, UCLA School of Medicine, Los Angeles, CA, USA
| | - Arthur W Toga
- Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Los Angeles, CA, USA
| | - Matt A Bernstein
- Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Michael W Weiner
- Department of Radiology and Biomedical Imaging, UCSF School of Medicine, San Francisco, CA, USA
| | - Paul M Thompson
- Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Los Angeles, CA, USA; Department of Neurology, University of Southern California, Los Angeles, CA, USA; Department of Psychiatry, University of Southern California, Los Angeles, CA, USA; Department of Radiology, University of Southern California, Los Angeles, CA, USA; Department of Engineering, University of Southern California, Los Angeles, CA, USA; Department of Pediatrics, University of Southern California, Los Angeles, CA, USA; Department of Ophthalmology, University of Southern California, Los Angeles, CA, USA.
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