Abstract
Multi-compartment diffusion models (MCM) are increasingly used to characterize the brain white matter microstructure from diffusion MRI. We address the problem of interpolation and averaging of MCM images as a simplification problem based on spectral clustering. As a core part of the framework, we propose novel solutions for the averaging of MCM compartments. Evaluation is performed both on synthetic and clinical data, demonstrating better performance for the “covariance analytic” averaging method. We then present an MCM template of normal controls constructed using the proposed interpolation.
Chapter PDF
Similar content being viewed by others
Keywords
- Spectral Cluster
- White Matter Microstructure
- Spherical Harmonic Basis
- Human Brain Data
- Orientation Distribution Func
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
Arsigny, V., Fillard, P., Pennec, X., Ayache, N.: Log-Euclidean metrics for fast and simple calculus on diffusion tensors. MRM 56(2), 411–421 (2006)
Barmpoutis, A., Vemuri, B.C., Forder, J.R.: Registration of high angular resolution diffusion MRI images using 4th order tensors. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part I. LNCS, vol. 4791, pp. 908–915. Springer, Heidelberg (2007)
Basser, P.J., Pierpaoli, C.: Microstructural and physiological features of tissues elucidated by quantitative diffusion-tensor MRI. Journal of Magnetic Resonance, Series B 111(3), 209–219 (1996)
Ferizi, U., Schneider, T., et al.: A ranking of diffusion MRI compartment models with in vivo human brain data. MRM 72(6), 1785–1792 (2014)
Geng, X., et al.: Diffusion MRI registration using orientation distribution functions. In: Prince, J.L., Pham, D.L., Myers, K.J. (eds.) IPMI 2009. LNCS, vol. 5636, pp. 626–637. Springer, Heidelberg (2009)
Goh, A., Lenglet, C., Thompson, P.M., Vidal, R.: A nonparametric Riemannian framework for processing high angular resolution diffusion images and its applications to ODF-based morphometry. Neuroimage 56, 1181–1201 (2011)
Guimond, A., Meunier, J., Thirion, J.P.: Average brain models: A convergence study. Computer Vision and Image Understanding 77(2), 192–210 (2000)
McGraw, T., Vemuri, B.: Von mises-fisher mixture model of the diffusion ODF. In: IEEE ISBI, pp. 65–68 (2006)
Ng, A.Y., Jordan, M.I., Weiss, Y., et al.: On spectral clustering: Analysis and an algorithm. Advances in Neural Information Processing Systems 2, 849–856 (2002)
Ruiz-Alzola, J., Westin, C.F., et al.: Nonrigid registration of 3D tensor medical data. Medical Image Analysis 6(2), 143–161 (2002)
Stamm, A., Pérez, P., Barillot, C.: A new multi-fiber model for low angular resolution diffusion mri. In: ISBI, pp. 936–939. IEEE (2012)
Suarez, R.O., Commowick, O., et al.: Automated delineation of white matter fiber tracts with a multiple region-of-interest approach. Neuroimage 59(4), 3690–3700 (2012)
Taquet, M., Scherrer, B., Commowick, O., Peters, J., Sahin, M., Macq, B., Warfield, S.K.: Registration and analysis of white matter group differences with a multi-fiber model. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part III. LNCS, vol. 7512, pp. 313–320. Springer, Heidelberg (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Hédouin, R., Commowick, O., Stamm, A., Barillot, C. (2015). Interpolation and Averaging of Multi-Compartment Model Images. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds) Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science(), vol 9350. Springer, Cham. https://doi.org/10.1007/978-3-319-24571-3_43
Download citation
DOI: https://doi.org/10.1007/978-3-319-24571-3_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-24570-6
Online ISBN: 978-3-319-24571-3
eBook Packages: Computer ScienceComputer Science (R0)