Abstract
In this paper we present a method for reconstructing D-MRI data on regular grids from sparse data without assuming specific diffusion models. This is particularly important when studying the fetal brain in utero, since registration methods applied for movement and distortion correction produce scattered data in spatial and angular (gradient) domains. We propose the use of a groupwise registration method, and a dual spatio-angular interpolation by using radial basis functions (RBF). Experiments performed on adult data showed a high accuracy of the method when estimating diffusion images in unavailable directions. The application to fetal data showed an improvement in the quality of the sequences according to criteria based on fractional anisotropy (FA) maps, and differences in the tractography results.
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
Rohde, G.K., Barnett, A.S., Basser, P.J., Marenco, S., Pierpaoli, C.: Comprehensive approach for correction of motion and distortion in diffusion-weighted MRI. Magn. Reson. Med. 51(1), 103–114 (2004)
Rousseau, F., Glenn, O.A., Iordanova, B., Rodriguez-Carranza, C., Vigneron, D.B., Barkovich, J.A., Studholme, C.: Registration-based approach for reconstruction of high-resolution in utero fetal MR brain images. Acad. Radiol. 13(9), 1072–1081 (2006)
Jiang, S., Xue, H., Counsell, S., Anjari, M., Allsop, J., Rutherford, M., Rueckert, D., Hajnal, J.V.: Diffusion tensor imaging (DTI) of the brain in moving subjects: application to in-utero fetal and ex-utero studies. Magn. Reson. Med. 62(3), 645–655 (2009)
Tuch, D.S., Reese, T.G., Wiegell, M.R., Makris, N., Belliveau, J.W., Wedeen, V.J.: High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity. Magn. Reson. Med. 48(4), 577–582 (2002)
Guimond, A., Meunier, J., Thirion, J.-P.: Average brain models: a convergence study. Comput. Vis. Image Understand 77, 192–210 (2000)
Carfora, M.F.: Interpolation on spherical geodesic grids: a comparative study. J. Comput. Appl. Math. 210(1-2), 99–105 (2007)
Nielsen, J.F., Ghugre, N.R., Panigrahy, A.: Affine and polynomial mutual information coregistration for artifact elimination in diffusion tensor imaging of newborns. Magn. Reson Imaging 22(9), 1319–1323 (2004)
Netsch, T., van Muiswinkel, A.: Quantitative evaluation of image-based distortion correction in diffusion tensor imaging. IEEE Trans. Med. Imaging 23(7), 789–798 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Oubel, E., Koob, M., Studholme, C., Dietemann, JL., Rousseau, F. (2010). Reconstruction of Scattered Data in Fetal Diffusion MRI. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010. MICCAI 2010. Lecture Notes in Computer Science, vol 6361. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15705-9_70
Download citation
DOI: https://doi.org/10.1007/978-3-642-15705-9_70
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15704-2
Online ISBN: 978-3-642-15705-9
eBook Packages: Computer ScienceComputer Science (R0)