Abstract
We propose a reconstructed diffusion gradient (RDG) method for spatial normalization of diffusion tensor imaging (DTI) data that warps the raw imaging data and then estimates the associated gradient direction for reconstruction of normalized DTI in the template space. The RDG method adopts the backward mapping strategy for DTI normalization, with a specially designed approach to reconstruct a specific gradient direction in combination with the local deformation force. The method provides a voxel-based strategy to make the gradient direction align with the raw diffusion weighted imaging (DWI) volumes, ensuring correct estimation of the tensors in the warped space and thereby retaining the orientation information of the underlying structure. Compared with the existing tensor reorientation methods, experiments using both simulated and human data demonstrated that the RDG method provided more accurate tensor information. Our method can properly estimate the gradient direction in the template space that has been changed due to image transformation, and subsequently use the warped imaging data to directly reconstruct the warped tensor field in the template space, achieving the same goal as directly warping the tensor image. Moreover, the RDG method also can be used to spatially normalize data using the Q-ball imaging (QBI) model.
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References
Basser, P.J., Mattiello, J., LeBihan, D.: MR diffusion tensor spectroscopy and imaging. Biophysical Journal 66, 259–267 (1994)
Alexander, D.C., Pierpaoli, C., Basser, P.J., Gee, J.C.: Spatial transformations of diffusion tensor magnetic resonance images. IEEE Transactions on Medical Imaging 20, 1131–1139 (2001)
Yeo, B.T., Vercauteren, T., Fillard, P., Peyrat, J.M., Pennec, X., Golland, P., Ayache, N., Clatz, O.: DT-REFinD: diffusion tensor registration with exact finite-strain differential. IEEE Transactions on Medical Imaging 28, 1914–1928 (2009)
Zhang, H., Yushkevich, P.A., Alexander, D.C., Gee, J.C.: Deformable registration of diffusion tensor MR images with explicit orientation optimization. Medical Image Analysis 10, 764–785 (2006)
Xu, D., Hao, X., Bansal, R., Plessen, K.J., Peterson, B.S.: Seamless warping of diffusion tensor fields. IEEE Transactions on Medical Imaging 27, 285–299 (2008)
Frank, L.R.: Anisotropy in high angular resolution diffusion-weighted MRI. Magn. Reson. Med. 45, 935–939 (2001)
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, 577–582 (2002)
Descoteaux, M., Angelino, E., Fitzgibbons, S., Deriche, R.: Regularized, fast, and robust analytical Q-ball imaging. Magn. Reson. Med. 58, 497–510 (2007)
Descoteaux, M., Angelino, E., Fitzgibbons, S., Deriche, R.: Apparent diffusion coefficients from high angular resolution diffusion imaging: estimation and applications. Magn. Reson. Med. 56, 395–410 (2006)
Ingalhalikar, M.A., Magnotta, V.A., Kim, J., Alexander, A.L.: A comparative study of diffusion tensor field transformations. In: Proc. SPIE Medical Imaging 2011, vol. 7259, pp. 72591Y–72591Y-8 (2009)
Ingalhalikar., M.A.: Spatial normalization of diffusion models and tensor analysis. vol. Ph.D. University of Iowa (2009)
Ashburner, J., Friston, K.J.: Nonlinear spatial normalization using basis functions. Hum. Brain. Mapp. 7, 254–266 (1999)
Ashburner, J., Neelin, P., Collins, D.L., Evans, A., Friston, K.: Incorporating prior knowledge into image registration. NeuroImage 6, 344–352 (1997)
Shen, D.G.: Image registration by local histogram matching. Pattern Recognition 40, 1161–1172 (2007)
Shen, D.G., Davatzikos, C.: HAMMER: Hierarchical attribute matching mechanism for elastic registration. IEEE Transactions on Medical Imaging 21, 1421–1439 (2002)
Fletcher, R.: A modified Marquardt subroutine for nonlinear least squares. Harwell Report 6799 (1971)
Basser, P.J., Mattiello, J., LeBihan, D.: Estimation of the effective self-diffusion tensor from the NMR spin echo. J. Magn. Reson. B 103, 247–254 (1994)
Wang, Z., Vemuri, B.C., Chen, Y., Mareci, T.H.: A constrained variational principle for direct estimation and smoothing of the diffusion tensor field from complex DWI. IEEE Transactions on Medical Imaging 23, 930–939 (2004)
Christiansen, O., Lee, T.M., Lie, J., Sinha, U., Chan, T.F.: Total variation regularization of matrix-valued images. Int. J. Biomed. Imaging, 27432 (2007)
Alexander, D.C., Pierpaoli, C., Basser, P.J., Gee, J.C.: Techniques for spatial normalization of diffusion tensor images. In: Proc. SPIE Medical Imaging, vol. 3919, pp. 470–481 (2000)
Basser, P.J., Pajevic, S.: Statistical artifacts in diffusion tensor MRI (DT-MRI) caused by background noise. Magn. Reson. Med. 44, 41–50 (2000)
Hult, J.: A fourth-order Runge-Kutta in the interaction picture method for, simulating supercontinuum generation in optical fibers. Journal of Lightwave Technology 25, 3770–3775 (2007)
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Liu, W. et al. (2012). Spatial Normalization of Diffusion Tensor Images with Voxel-Wise Reconstruction of the Diffusion Gradient Direction. In: Yap, PT., Liu, T., Shen, D., Westin, CF., Shen, L. (eds) Multimodal Brain Image Analysis. MBIA 2012. Lecture Notes in Computer Science, vol 7509. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33530-3_11
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DOI: https://doi.org/10.1007/978-3-642-33530-3_11
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