Abstract
Fractional anisotropy (FA), a very widely used measure of fiber integrity based on diffusion tensor imaging (DTI), is a problematic concept as it is influenced by several quantities including the number of dominant fiber directions within each voxel, each fiber’s anisotropy, and partial volume effects from neighboring gray matter. With High-angular resolution diffusion imaging (HARDI) and the tensor distribution function (TDF), one can reconstruct multiple underlying fibers per voxel and their individual anisotropy measures by representing the diffusion profile as a probabilistic mixture of tensors. We found that FA, when compared with TDF-derived anisotropy measures, correlates poorly with individual fiber anisotropy, and may sub-optimally detect disease processes that affect myelination. By contrast, mean diffusivity (MD) as defined in standard DTI appears to be more accurate. Overall, we argue that novel measures derived from the TDF approach may yield more sensitive and accurate information than DTI-derived measures.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Basser, P.J., Pierpaoli, C.: Microstructural and physiological features of tissues elucidated by quantitative diffusion tensor MRI. J. Magn. Reson. B 111(3), 209–219 (1996)
Le Bihan, D.: IVIM method measures diffusion and perfusion. Diagn Imaging (San Franc) 12(6), 133–136 (1990)
Tuch, D.S.: Q-Ball Imaging. Magnetic Resonance in Medicine 52, 1358–1372 (2004)
Tuch, D., Diffusion, M.R.I.: of complex tissue structure. PhD thesis, Harvard University-Massachusetts Institute of Technology, Cambridge, Massachusetts (2002)
Alexander, D.: Maximum entropy spherical deconvolution for diffusion MRI. In: Proceedings of the 19th International Conference on Information Processing in Medical Imaging (IPMI), Glenwood Springs, CO, USA (2005)
Hess, C.P., Mukherjee, P., Han, E.T., Xu, D., Vigneron, D.B.: Q-ball reconstruction of multimodal fiber orientations using the spherical harmonic basis. Magn. Reson. Med. 56, 104–117 (2006)
Jansons, K.M., Alexander, D.: Persistent angular structure: new insights from diffusion magnetic resonance imaging data. Inverse Probl. 19, 1031–1046 (2003)
Anderson, A.: Measurement of fiber orientation distributions using high angular resolution diffusion imaging. Magn. Reson. Med. 54, 1194–1206 (2005)
Özarslan, E., Shepherd, T., Vemuri, B.C., Blackband, S., Mareci, T.: Resolution of complex tissue microarchitecture using the diffusion orientation transform (DOT). NeuroImage 31(3), 1083–1106 (2006)
Tournier, J.D., Calamante, F., Gadian, D., Connelly, A.: Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution. NeuroImage 23, 1176–1185 (2004)
Leow, A.D., Zhu, S., Zhan, L., de Zubicaray, G.I., Meredith, M., Wright, M., Toga, A.W., Thompson, P.M.: The Tensor Distribution Function. Magn Reson Med. 18; 61(1), 205–214 (2009)
Zhang, Y., Schuff, N., Jahng, G.H., Bayne, W., Mori, S., Schad, L., Mueller, S., Du, A.T., Kramer, J.H., Yaffe, K., Chui, H., Jagust, W.J., Miller, B.L., Weiner, M.: Diffusion tensor imaging of cingulum fibers in mild cognitive impairment and Alzheimer disease. Neurology 68(1), 13–19 (2007)
Jones, D.K., Horsfield, M.A., Simmons, A.: Optimal strategies for measuring diffusion in anisotropic systems by magnetic resonance Imaging. Magn. Res. Med. 42(3), 515–525 (1999)
Roberts, T.P.L., Liu, F., Kassner, A., Mori, S., Guha, A.: Fiber Density Index Correlates with Reduced Fractional Anisotropy in White Matter of Patients with Glioblastoma. AJNR Am J. Neuroradiol. 26, 2183–2186 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhan, L. et al. (2009). A Novel Measure of Fractional Anisotropy Based on the Tensor Distribution Function. In: Yang, GZ., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009. MICCAI 2009. Lecture Notes in Computer Science, vol 5761. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04268-3_104
Download citation
DOI: https://doi.org/10.1007/978-3-642-04268-3_104
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04267-6
Online ISBN: 978-3-642-04268-3
eBook Packages: Computer ScienceComputer Science (R0)