How does angular resolution affect diffusion imaging measures?

Liang Zhan, Alex D Leow, Neda Jahanshad, Ming-Chang Chiang, Marina Barysheva, Agatha D. Lee, Arthur W. Toga, McMahon, KL, de Zubicaray, GI, Wright, MJ and Paul M. Thompson (2010) How does angular resolution affect diffusion imaging measures?. NeuroImage, 49 2: 1357-1371. doi:10.1016/j.neuroimage.2009.09.057

Author Liang Zhan
Alex D Leow
Neda Jahanshad
Ming-Chang Chiang
Marina Barysheva
Agatha D. Lee
Arthur W. Toga
McMahon, KL
de Zubicaray, GI
Wright, MJ
Paul M. Thompson
Title How does angular resolution affect diffusion imaging measures?
Journal name NeuroImage   Check publisher's open access policy
ISSN 1053-8119
Publication date 2010-01-15
Year available 2009
Sub-type Article (original research)
DOI 10.1016/j.neuroimage.2009.09.057
Volume 49
Issue 2
Start page 1357
End page 1371
Total pages 15
Editor Dr J. C. Mazziotta
Dr R. S. J. Frackowiak
K. J. Friston
Place of publication United States of America
Publisher Academic Press
Collection year 2010
Language eng
Subject C1
920199 Clinical Health (Organs, Diseases and Abnormal Conditions) not elsewhere classified
970111 Expanding Knowledge in the Medical and Health Sciences
080106 Image Processing
110999 Neurosciences not elsewhere classified
170205 Neurocognitive Patterns and Neural Networks
Abstract A key question in diffusion imaging is how many diffusion-weighted images suffice to provide adequate signal-to-noise ratio (SNR) for studies of fiber integrity. Motion, physiological effects, and scan duration all affect the achievable SNR in real brain images, making theoretical studies and simulations only partially useful. We therefore scanned 50 healthy adults with 105-gradient high-angular resolution diffusion imaging (HARDI) at 4T. From gradient image subsets of varying size (6 ≤ N ≤ 94) that optimized a spherical angular distribution energy, we created SNR plots (versus gradient numbers) for seven common diffusion anisotropy indices: fractional and relative anisotropy (FA, RA), mean diffusivity (MD), volume ratio (VR), geodesic anisotropy (GA), its hyperbolic tangent (tGA), and generalized fractional anisotropy (GFA). SNR, defined in a region of interest in the corpus callosum, was near-maximal with 58, 66, and 62 gradients for MD, FA, and RA, respectively, and with about 55 gradients for GA and tGA. For VR and GFA, SNR increased rapidly with more gradients. SNR was optimized when the ratio of diffusion-sensitized to non-sensitized images was 9.13 for GA and tGA, 10.57 for FA, 9.17 for RA, and 26 for MD and VR. In orientation density functions modeling the HARDI signal as a continuous mixture of tensors, the diffusion profile reconstruction accuracy rose rapidly with additional gradients. These plots may help in making trade-off decisions when designing diffusion imaging protocols.
Keyword High-angular resolution diffusion imaging
Generalized fractional anisotropy
Signal-to-noise ratio
Kullback-Leibler divergence
Q-Index Code C1
Q-Index Status Confirmed Code

Document type: Journal Article
Sub-type: Article (original research)
Collections: 2010 Higher Education Research Data Collection
Centre for Advanced Imaging Publications
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Citation counts: TR Web of Science Citation Count  Cited 25 times in Thomson Reuters Web of Science Article | Citations
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Created: Sun, 03 Jan 2010, 00:00:52 EST