How does angular resolution affect diffusion imaging measures?

Zhan, L., Leow, A. D., Jahanshad, N., Chiang, M. C., Barysheva, M., Lee, A. D., Toga, A. W., McMahon, K. L., de Zubicaray, G. I., Wright, M. J., & Thompson, P. M. (2010) How does angular resolution affect diffusion imaging measures? NeuroImage, 49(2), pp. 1357-1371.

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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.

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29 citations in Scopus
28 citations in Web of Science®
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ID Code: 85860
Item Type: Journal Article
Refereed: Yes
Keywords: Anisotropy, Generalized fractional anisotropy, High-angular resolution diffusion imaging, Kullback-Leibler divergence, Signal-to-noise ratio, Tensor
DOI: 10.1016/j.neuroimage.2009.09.057
ISSN: 1095-9572
Divisions: Current > QUT Faculties and Divisions > Faculty of Health
Current > Institutes > Institute of Health and Biomedical Innovation
Copyright Owner: Copyright 2009 Elsevier Inc
Deposited On: 06 Oct 2015 03:53
Last Modified: 16 Oct 2015 03:21

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