Abstract
Diffusion tensor magnetic resonance imaging is widely used to study the structure of the fiber pathways of brain white matter. However, the diffusion tensor cannot capture complex intra-voxel fiber architecture such as fiber crossings. Consequently, a number of methods have been proposed to recover intra-voxel fiber bundle orientations from high angular-resolution diffusion imaging scans, which are optimized to resolve fiber crossings. In this work we study how multi-tensor, spherical deconvolution, analytical QBall and diffusion basis function methods perform under clinical scanning conditions. Our experiments indicate that it is feasible to apply some of these methods in clinical data sets.
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Basser, P.J., Pierpaoli, C.: Microstructural and physiological features of tissues elucidated by quantitative DT-MRI. J. Magn. Reson. B 111, 209–219 (1996)
Behrens, T.E.J., Berga, H.J., Jbabdi, S., Rushworth, M.F.S., Woolrich, M.W.: Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? NeuroImage 34(1), 144–155 (2007)
Alexander, D.C.: Multiple-fibre reconstruction algorithms for diffusion MRI. Annals of the New York Academy of Sciences 1046, 113–133 (2005)
Alexander, D.C., Barker, G.J.: Optimal imaging parameters for fiber-orientation estimation in diffusion MRI. NeuroImage 27, 357–367 (2005)
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)
Descoteaux, M., Angelino, E., Fitzgibbons, S., Deriche, R.: Regularized, fast and robust analytical Q-ball imaging. Magn. Reson. Med. 58(3), 497–510 (2007)
Alexander, D.C.: Maximum entropy spherical deconvolution for diffusion MRI. In: Proc. Inf. Processing Med. Imaging, Glenwood Springs, CO, USA, pp. 76–87 (2005)
Tournier, J.D., Calamante, F., Connelly, A.: Robust determination of the fibre orientation distribution in diffusion MRI: Non-negativity constrained super-resolved spherical deconvolution. NeuroImage 35(4), 1459–1472 (2007)
Ramirez-Manzanares, A., Rivera, M., Vemuri, B.C., Carney, P., Mareci, T.: Diffusion basis functions decomposition for estimating white matter intravoxel fiber geometry. IEEE Trans. Med. Imaging 26(8), 1091–1102 (2007)
Jian, B., Vemuri, B.C.: A unified computational framework for deconvolution to reconstruct multiple fibers from DW-MRI. IEEE Trans. Med. Imaging 26(11), 1464–1471 (2007)
Cook, P.A., Bai, Y., Nedjati-Gilani, S., Seunarine, K.K., Hall, M.G., Parker, G.J., Alexander, D.C.: Camino: Open-source diffusion-MRI reconstruction and processing. In: Proc. 14th Scientific Meeting of the ISMRM, Seattle, WA, USA, p. 2759 (2006)
Westin, C.F., Peled, S., Gudbjartsson, H., Kikinis, R., Jolesz, F.A.: Geometrical diffusion measures for MRI from tensor basis analysis. In: Proc. 5th Scientific Meeting of the ISMRM, Vancouver, Canada, p. 1742 (1997)
Hall, M.G., Alexander, D.C.: Finite pulse width improve fibre orientation estimates in diffusion tensor MRI. In: Proc. 14th Scientific Meeting of the ISMRM, Seattle, WA, USA, p. 1076 (2006)
Cook, P.A., Symms, M., Boulby, P.A., Alexander, D.C.: Optimal acquisition orders of diffusion-weighted MRI measurements. J. Magn. Reson. Imaging 25(5), 1051–1058 (2007)
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Ramirez-Manzanares, A., Cook, P.A., Gee, J.C. (2008). A Comparison of Methods for Recovering Intra-voxel White Matter Fiber Architecture from Clinical Diffusion Imaging Scans. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008. MICCAI 2008. Lecture Notes in Computer Science, vol 5241. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85988-8_37
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DOI: https://doi.org/10.1007/978-3-540-85988-8_37
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