Abstract
Diffusion Tensor MRI has become the preferred imaging modality to explore white matter structure and brain connectivity in vivo. Conventional region of interest analysis and voxel-based comparison does not make use of the geometric properties of fiber tracts. This paper explores shape modelling of major fiber bundles. We describe tracts, represented as clustered sets of curves of similar shape, by a shape prototype swept along a space trajectory. This approach can naturally describe white matter structures observed either as bundles dispersing towards the cortex or tracts defined as dense patterns of parallel fibers. Sets of streamline curves obtained from tractography are clustered, parametrized and aligned with a similarity transform. An average curve and eigenmodes of shape variation describe a compact statistical shape model. Reconstruction by sweeping the template along the trajectory results in a simplified model of a tract. Feasibility is demonstrated by modelling callosal and cortico-spinal fasciculi of two different subjects.
Chapter PDF
Similar content being viewed by others
References
Basser, P.J., Pajevic, S., Pierpaoli, C., Aldroubi, A.: Fiber tract following in the human brain using DT-MRI data. IEICE Trans. on Information and Systems E85-D(1), 15–21 (2002)
Björnemo, M., Brun, A., Kikinis, R., Westin, C.-F.: Regularized stochastic white matter tractography using diffusion tensor MRI. In: Dohi, T., Kikinis, R. (eds.) MICCAI 2002. LNCS, vol. 2488, pp. 435–442. Springer, Heidelberg (2002)
Coulon, O., Alexander, D.C., Arridge, S.R.: A regularization scheme for diffusion tensor magnetic resonance images. In: Insana, M.F., Leahy, R.M. (eds.) IPMI 2001. LNCS, vol. 2082, pp. 92–105. Springer, Heidelberg (2001)
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)
Westin, C.-F., Maier, S.E., Mamata, H., Nabavi, A., Jolesz, F.A., Kikinis, R.: Processing and visualization for diffusion tensor MRI. Medical Image Analysis 6, 93–108 (2002)
Zhukov, L., Barr, A.H.: Oriented tensor reconstruction: tracing neural pathways from diffusion tensor MRI. In: Proc. IEEE Visualization (2002)
Alexander, D.C., Gee, J.C.: Elastic matching of diffusion tensor images. Computer Vision and Image Understanding 77, 233–250 (2000)
Xu, D., Mori, S., Solaiyappan, M., van Zijl, P.C.M., Davatzikos, C.: A framework for callosal fiber distribution analysis. NeuroImage 17, 1131–1143 (2002)
Fillard, P., Gilmore, J., Lin, W., Gerig, G.: Quantitative analysis of white matter fiber properties along geodesic paths. In: Ellis, R.E., Peters, T.M. (eds.) MICCAI 2003. LNCS, vol. 2879, pp. 16–23. Springer, Heidelberg (2003)
Ding, Z., Gore, J.C., Anderson, A.W.: Classification and quantification of neuronal fiber pathways using diffusion tensor MRI. Magn. Res. Med. 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. of IEEE ISBI, pp. 344–347 (2004)
Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models - their training and application. Computer Vision and Image Understanding 61(1), 38–59 (1995)
Goodall, C.: Procrustes methods in the statistical analysis of shape. J.R. Statist. Soc. B 53(2), 239–285 (1991)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Corouge, I., Gouttard, S., Gerig, G. (2004). A Statistical Shape Model of Individual Fiber Tracts Extracted from Diffusion Tensor MRI. In: Barillot, C., Haynor, D.R., Hellier, P. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2004. MICCAI 2004. Lecture Notes in Computer Science, vol 3217. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30136-3_82
Download citation
DOI: https://doi.org/10.1007/978-3-540-30136-3_82
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22977-3
Online ISBN: 978-3-540-30136-3
eBook Packages: Springer Book Archive