Clinical DT-MRI estimation, smoothing and fiber tracking with Log-Euclidean metrics

By Pierre Fillard, Vincent Arsigny, Xavier Pennec, Nicholas Ayache. IEEE International Symposium on Biomedical Imaging, 2006

299 citations recorded by the source corpus.

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Noise and Signal Estimation in Magnitude MRI and Rician Distributed Images: A LMMSE Approach (2008, unknown): “[12] recently proposed another ML-based scheme that uses an anisotropic regularization term to exploit the spatial correlation, and apply it to correct the bias in tensor estimation in DT-MRI.”
The Neural Architecture of the Language Comprehension Network: Converging Evidence from Lesion and Connectivity Analyses (2011, methodology): “Using the methods implemented in the MedInria software package(5) (Fillard et al., 2007), the diffusion slice and used for 2D non-linear warping of the DeArmond et al.”
A discrete regression method on manifolds and its application to data on SO(n) (2011, methodology): “The way the material in this section is derived is inspired by [Fillard et al., 2007, appendix] and Bhatia [2007].”
Diffusion Weighted Image Denoising Using Overcomplete Local PCA (2013, background): “…over small local windows instead of the whole image what significantly produces sparser representation (in the extreme case, i.e. in homogeneous areas, it can result in the sparsest representation not requiring any component to represent the data but on only the mean value of the region).”