Abstract
Diffusion tensor imaging (DTI) has become the major modality to study properties of white matter and the geometry of fiber tracts of the human brain. Clinical studies mostly focus on regional statistics of fractional anisotropy (FA) and mean diffusivity (MD) derived from tensors. Existing analysis techniques do not sufficiently take into account that the measurements are tensors, and thus require proper interpolation and statistics based on tensors, and that regions of interest are fiber tracts with complex spatial geometry. We propose a new framework for quantitative tract-oriented DTI analysis that includes tensor interpolation and averaging, using nonlinear Riemannian symmetric space. As a result, tracts of interest are represented by the geometry of the medial spine attributed with tensor statistics calculated within cross-sections. Examples from a clinical neuroimaging study of the early developing brain illustrate the potential of this new method to assess white matter fiber maturation and integrity.
This research is supported by the NIH NIBIB grant P01 EB002779, the NIMH Silvio Conte Center for Neuroscience of Mental Disorders MH064065, and the UNC Neurodevelopmental Disorders Research Center HD 03110. The work is also funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149-01, project NAMIC. We acknowledge the Insight Toolkit community for providing the software framework. Dr. Weili Lin, UNC Radiology, is acknowledged for active support of developing an improved neonatal DT MRI acquisition technique.
Chapter PDF
Similar content being viewed by others
References
Ding, Z., Gore, J., Anderson, A.: Classification and quantification of neuronal fiber pathways using diffusion tensor MRI. Magnetic Resonance in Medicine 49, 716–721 (2003)
Corouge, I., Gouttard, S., Gerig, G.: Towards a shape model of white matter fiber bundles using Diffusion Tensor MRI. In: Proc. IEEE ISBI, pp. 344–347 (2004)
Lim, K., Helpern, J.: Neuropsychiatric applications of DTI - a review. NMR in Biomedicine 15, 587–593 (2002)
Corouge, I., Gouttard, S., Gerig, G.: A statistical shape model of individual fiber tracts extracted from diffusion tensor MRI. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3217, pp. 671–679. Springer, Heidelberg (2004)
Fletcher, P.T., Joshi, S.: Principal geodesic analysis on symmetric spaces: Statistics of diffusion tensors. In: Sonka, M., Kakadiaris, I.A., Kybic, J. (eds.) CVAMIA/MMBIA 2004. LNCS, vol. 3117, pp. 87–98. Springer, Heidelberg (2004)
Fletcher, P.T.: Statistical variability in nonlinear spaces: Application to shape analysis and DT-MRI. PhD thesis, University of North Carolina (2004)
Batchelor, P.G., Moakher, M., Atkinson, D., Calamante, F., Connelly, A.: A rigorous framework for diffusion tensor calculus. Magnetic Resonance in Medicine 53, 221–225 (2005)
Helgason, S.: Differential Geometry, Lie Groups, and Symmetric Spaces. Academic Press, London (1978)
Fréchet, M.: Les éléments aléatoires de nature quelconque dans un espace distancié. Ann. Inst. H. Poincaré, 215–310 (1948)
Pennec, X.: Probabilities and statistics on Riemannian manifolds: basic tools for geometric measurements. In: IEEE Workshop on Nonlinear Signal and Image Processing (1999)
Cootes, T., Taylor, C., Cooper, D., Graham, J.: Active shape models - their training and application. Computer Vision and Image Understanding 61, 38–59 (1995)
Westin, C.F., Maier, S., Mamata, H., Nabavi, A., Jolesz, F., Kikinis, R.: Processing and visualization for diffusion tensor MRI. Medical Image Analysis 6, 93–108 (2002)
Fillard, P., Gerig, G.: Analysis tool for diffusion tensor MRI. In: Ellis, R.E., Peters, T.M. (eds.) MICCAI 2003. LNCS, vol. 2879, pp. 967–968. Springer, Heidelberg (2003)
Goodall, C.: Procrustes methods in the statistical analysis of shape. J.R. Statist. Soc. B 53, 285–339 (1991)
Gilmore, J., Zhai, G., Wilber, K., Smith, J., Lin, W., Gerig, G.: 3T magnetic resonance imaging of the brain in newborns. Psychiatry Research Neuroimaging 132, 81–85 (2004)
Rutherford, M.: MRI of the Neonatal Brain. WB Saunders Ltd. (2002) ISBN: 0702025348
Basser, P., Pierpaoli, C.: Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J. Magn. Reson. B 3, 209–219 (1996)
Gerig, G., Corouge, I., Vachet, C., Krishnan, K.R., MacFall, J.R.: Quantitative analysis of diffusion properties of white matter fiber tracts: A validation study. In: ISMRM (May 2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Corouge, I., Fletcher, P.T., Joshi, S., Gilmore, J.H., Gerig, G. (2005). Fiber Tract-Oriented Statistics for Quantitative Diffusion Tensor MRI Analysis. In: Duncan, J.S., Gerig, G. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2005. MICCAI 2005. Lecture Notes in Computer Science, vol 3749. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11566465_17
Download citation
DOI: https://doi.org/10.1007/11566465_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29327-9
Online ISBN: 978-3-540-32094-4
eBook Packages: Computer ScienceComputer Science (R0)Springer Nature Proceedings Computer Science
