Alexander, D.; Gee, J. (2000). Elastic matching of diffusion tensor images. Computer Vision and Image Understanding, 77(2), 233-250.
Alexander, D. C. P.; Basser, P.; Gee, J. (2001). Spatial transformations of diffusion tensor magnetic resonance images. IEEE Transactions on Medical Imaging, 20(11), 1131-1139.
Ando, T.; Li, C. K.; Mathias, R. (2004). Geometric means. Linear Algebra and its Applications, 385, 305-334.
Arsigny, V.; Fillard, P.; Pennec, X.; Ayache, N. (2006). Log-Euclidean metrics for fast and simple calculs on diffusion tensors. Magnetic Resonance in Medicine, 56, 411-421.
Arsigny, V.; Fillard, P.; Pennec, X.; Ayache, N. (2007). Geometric means in a novel vector space structure on symmetric positive-definite matrices. SIAM Journal on Matrix Analysis and Applications, 29(1), 328-347.
Awate, S.; Zhang, H.; Gee, J. (2007). Fuzzy nonparametric DTI segmentation for robust cingulum-tract extraction. MICCAI pp. 294-301.
Basser, P.; Pierpaoli, C. (1996). Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. Journal of Magnetic Resonance, Series B, 111(3), 209-219.
Basser, P.; Mattiello, J.; Bihan, D. L. (1994). MR diffusion tensor spectroscopy and imaging. Biophysical Journal, 66(1), 259-267.
Batchelor, P. G.; Moakher, M.; Atkinson, D.; Calamante, F.; Connelly, A. (2005). A rigorous framework for diffusion tensor calculus. Magnetic Resonance in Medicine, 53(1), 221-225.
Birkhoff, G. (1957). Extensions of Jentzsch's theorem. Transactions of the American Mathematical Society, 85(1), 219-227.
Bonnabel, S.; Sepulchre, R. (2009). Riemannian metric and geometric mean for positive semidefinite matrices of fixed rank. SIAM Journal on Matrix Analysis and Applications, 31(3), 1055-1070.
Bonnabel, S.; Collard, A.; Sepulchre, R. (2013). Rank-preserving geometric means of positive semi-definite matrices. Linear Algebra and its Applications, 438(8), 3202-3216.
Burbea, J.; Rao, C. (1982). Entropy differential metric, distance and divergence measures in probability spaces: A unified approach. Journal of Multivariate Analysis, 12(4), 575-596.
Castaño-Moraga, C.; Lenglet, C.; Deriche, R.; Ruiz-Alzola, J. (2006). A fast and rigorous anisotropic smoothing method for DT-MRI. In Proceedings of the IEEE International Symposium on Biomedical Imaging: From Nano to Macro pp. 93-96.
Castro, F.; Clatz, O.; Dauguet, J.; Archip, N.; Thiran, J. P.; Warfield, S. (2007). Evaluation of brain image nonrigid registration algorithms based on Log-Euclidean MR-DTI consistency measures. In Proceedings of the IEEE International Symposium on Biomedical Imaging: From Nano to Macro pp. 45-48.
Cetingül, H. E.; Afsari, B.; Wright, M. J.; Thompson, P. M.; Vidal, R. (2012). Group action induced averaging for HARDI processing. In Proceedings of the IEEE International Symposium on Biomedical Imaging pp. 1389-1392.
Chefd'hotel, C.; Tschumperlé, D.; Deriche, R.; Faugeras, O. (2004). Regularizing flows for constrained matrix-valued images. Journal of Mathematical Imaging and Vision, 20, 147-162.
Cheng, J.; Ghosh, A.; Jiang, T.; Deriche, R. (2009). A Riemannian framework for orientation distribution function computing, vol. 12. MICCAI pp. 911-918.
Chiang, M. C.; Leow, A. D.; Klunder, A. D.; Dutton, R. A.; Barysheva, M.; Rose, S.; et al. (2008). Fluid registration of diffusion tensor images using information theory. IEEE Transactions on Medical Imaging, 27(4), 442-456.
Dai, Y.; Trumpf, J.; Li, H.; Barnes, N.; Hartley, R. (2010). Rotation averaging with application to camera-rig calibration. In: Computer vision - ACCV 2009 (pp. 335-346).
Dryden, I. L.; Koloydenko, A.; Zhou, D. (2009). Non-Euclidean statistics for covariance matrices, with applications to diffusion tensor imaging. The Annals of Applied Statistics, 3(3), 1102-1123.
Fillard, P.; Pennec, X.; Arsigny, V.; Ayache, N. (2007). Clinical DT-MRI estimation, smoothing, and fiber tracking with log-Euclidean metrics. IEEE Transactions on Medical Imaging, 26(11), 1472-1482.
Fletcher, P. T.; Joshi, S. (2007). Riemannian geometry for the statistical analysis of diffusion tensor data. Signal Processing, 87, 250-262.
Fletcher, P. T.; Venkatasubramanian, S.; Joshi, S. (2009). The geometric median on Riemannian manifolds with application to robust atlas estimation. NeuroImage, 45(1 Suppl), S143-S152.
Goh, A.; Lenglet, C.; Thompson, P. M.; Vidal, R. (2011). A nonparametric Riemannian framework for processing high angular resolution diffusion images and its applications to ODF-based morphometry. Neuroimage, 56(3), 1181-1201.
Goodlett, C.; Fletcher, P. T.; Gilmore, J. H.; Gerig, G. (2009). Group analysis of DTI fiber tract statistics with application to neurodevelopment. NeuroImage, 45(1, Supplement 1), S133-S142.
Gur, Y.; Sochen, N. (2007). Fast invariant Riemannian DT-MRI regularization. In Proceedings of the International Conference on Computer Vision pp. 1-7.
Ingalhalikar, M.; Yang, J.; Davatzikos, C.; Verma, R. (2010). DTI-DROID: Diffusion tensor imaging-deformable registration using orientation and intensity descriptors. International Journal of Imaging Systems and Technology, 20(2), 99-107.
Jensen, J. H.; Helpern, J. A. (2010). MRI quantification of non-Gaussian water diffusion by kurtosis analysis. NMR in Biomedicine, 23(7), 698-710.
Karcher, H. (1977). Riemannian center of mass and mollifier smoothing. Communications on Pure and Applied Mathematics, 30(5), 509-541.
Kindlmann, G.; San Jose Estepar, R.; Niethammer, M.; Haker, S.; Westin, C. F. (2007). Geodesic-loxodromes for diffusion tensor interpolation and difference measurement. MICCAI pp. 1-9.
Lenglet, C.; Rousson, M.; Deriche, R.; Faugeras, O. (2006). Statistics on the manifold of multivariate normal distributions: Theory and application to diffusion tensor MRI processing. Journal of Mathematical Imaging and Vision, 25, 423-444.
Lenglet, C.; Campbell, J.; Descoteaux, M.; Haro, G.; Savadjiev, P.; Wassermann, D.; et al. (2009). Mathematical methods for diffusion MRI processing. NeuroImage, 45(1, Supplement 1), S111-S122.
Lepore, N.; Brun, C.; Chiang, M. C.; Chou, Y. Y.; Dutton, R.; Hayashi, K.; Lopez, O.; Aizenstein, H.; Toga, A.; Becker, J.; Thompson, P. (2006). Multivariate statistics of the Jacobian matrices in tensor based morphometry and their application to HIV/AIDS. MICCAI pp. 191-198.
Moakher, M. (2005). A differential geometric approach to the geometric mean of symmetric positive-definite matrices. SIAM Journal on Matrix Analysis and Applications, 26(3), 735-747.
Moakher, M.; Zéraï, M. (2011). The Riemannian geometry of the space of positive-definite matrices and its application to the regularization of positive-definite matrix-valued data. Journal of Mathematical Imaging and Vision, 40, 171-187.
Ncube, S.; Srivastava, A. (2011). A novel Riemannian metric for analyzing HARDI data. In Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, vol. 7962 p. 7.
Nesterov, Y.; Nemirovskii, A. S.; Ye, Y. (1994). Interior-point polynomial algorithms in convex programming. SIAM, 13.
Parker, G. J. M.; Haroon, H. A.; Wheeler-Kingshott, C. A. M. (2003). A framework for a streamline-based probabilistic index of connectivity (PICo) using a structural interpretation of MRI diffusion measurments. Journal of Magnetic Resonance Imaging, 18, 245-254.
Pennec, X.; Fillard, P.; Ayache, N. (2006). A Riemannian framework for tensor computing. International Journal of Computer Vision, 66(1), 41-66.
Perona, P.; Malik, J. (1990). Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(7), 629-639.
Petz, D.; Temesi, R. (2005). Means of positive numbers and matrices. SIAM Journal on Matrix Analysis and Applications, 27(3), 712-720.
Sarlette, A.; Sepulchre, R.; Leonard, N. (2007). Cooperative attitude synchronization in satellite swarms: A consensus approach. In Proceedings of the 17th IFAC Symposium on Automatic Control in Aerospace.
Skovgaard, L. T. (1984). A Riemannian geometry of the multivariate normal model. Scandinavian Journal of Statistics, 11(4), 211-223.
Smith, S. T. (2005). Covariance, subspace, and intrinsic Cramér-Rao bounds. IEEE Transactions on Signal Processing, 53, 1610-1630.
Thévenaz, P.; Blu, T.; Unser, M. (2000). Interpolation revisited [medical images application]. IEEE Transactions on Medical Imaging, 19(7), 739-758.
Tschumperlé, D.; Deriche, R. (2001). Diffusion tensor regularization with constraints preservation. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1, 948.
Tuch, D. (2004). Q-ball imaging. Magnetic Resonance in Medicine, 52(6), 1358-1372.
Weldeselassie, Y.; Hamarneh, G. (2007). DT-MRI segmentation using graph cuts. SPIE, 6512.
Weldeselassie, Y.; Hamarneh, G.; Beg, F.; Atkins, S. (2009). Novel decomposition of tensor distance into shape and orientation distances. MICCAI pp. 173-180.
Yeo, B.; Vercautern, T.; Fillard, P.; Pennec, X.; Gotland, P.; Ayache, N.; et al. (2008). DTI registration with exact finite-strain differential. In Proceedings of the IEEE International Symposium on Biomedical Imaging: From Nano to Macro.
Yeo, B.; Vercauteren, T.; Fillard, P.; Peyrat, J. M.; Pennec, X.; Golland, P.; et al. (2009). DT-REFinD: Diffusion Tensor registration with exact finite-strain differential. IEEE Transactions on Medical Imaging, 28(12), 1914-1928.
Zhang, H.; Yushkevich, P. A.; Alexander, D.; Gee, J. (2006). Deformable registration of diffusion tensor MR images with explicit orientation optimization. Medical Image Analysis, 10(5), 764-785.
Zhou, D. (2010). Statistical analysis of diffusion tensor imaging. PhD thesis, University of Nottingham.
Zhou, D.; Dryden, I. L.; Koloydenko, A.; Bai, L. (2013). Procrustes analysis for diffusion tensor image processing. International Journal of Computer Theory and Engineering, 5(1), 108-113.
