Parametric spherical deconvolution: Inferring anatomical connectivity using diffusion MR imaging
暂无分享,去创建一个
[1] Ajay Jasra,et al. Markov Chain Monte Carlo Methods and the Label Switching Problem in Bayesian Mixture Modeling , 2005 .
[2] WahbaGrace. Spline Interpolation and Smoothing on the Sphere , 1981 .
[3] Carl-Fredrik Westin,et al. A Bayesian approach for stochastic white matter tractography , 2006, IEEE Transactions on Medical Imaging.
[4] P. Basser,et al. Statistical artifacts in diffusion tensor MRI (DT‐MRI) caused by background noise , 2000, Magnetic resonance in medicine.
[5] Dennis M. Healy,et al. An empirical Bayes approach to directional data and efficient computation on the sphere , 1996 .
[6] N. Makris,et al. High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity , 2002, Magnetic resonance in medicine.
[7] A. Anderson. Measurement of fiber orientation distributions using high angular resolution diffusion imaging , 2005, Magnetic resonance in medicine.
[8] H. Robbins. A Stochastic Approximation Method , 1951 .
[9] Alan Connelly,et al. Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution , 2004, NeuroImage.
[10] J. E. Tanner,et al. Spin diffusion measurements : spin echoes in the presence of a time-dependent field gradient , 1965 .
[11] H. Haario,et al. An adaptive Metropolis algorithm , 2001 .
[12] Daniel C. Alexander,et al. Probabilistic Monte Carlo Based Mapping of Cerebral Connections Utilising Whole-Brain Crossing Fibre Information , 2003, IPMI.
[13] M. Newton. Approximate Bayesian-inference With the Weighted Likelihood Bootstrap , 1994 .
[14] A. Einstein. Über die von der molekularkinetischen Theorie der Wärme geforderte Bewegung von in ruhenden Flüssigkeiten suspendierten Teilchen [AdP 17, 549 (1905)] , 2005, Annalen der Physik.
[15] Daniel C Alexander,et al. Multiple‐Fiber Reconstruction Algorithms for Diffusion MRI , 2005, Annals of the New York Academy of Sciences.
[16] Karl J. Friston,et al. Classical and Bayesian Inference in Neuroimaging: Theory , 2002, NeuroImage.
[17] M. Raichle,et al. Tracking neuronal fiber pathways in the living human brain. , 1999, Proceedings of the National Academy of Sciences of the United States of America.
[18] C. Beaulieu,et al. The basis of anisotropic water diffusion in the nervous system – a technical review , 2002, NMR in biomedicine.
[19] Jean-Michel Marin,et al. Bayesian Modelling and Inference on Mixtures of Distributions , 2005 .
[20] V. Swayze,et al. Two Hemispheres—One Brain: Functions of the Corpus Callosum , 1987 .
[21] H. Jeffreys,et al. Theory of probability , 1896 .
[22] J. Sijbers,et al. Maximum likelihood estimation of signal amplitude and noise variance from MR data , 2004, Magnetic resonance in medicine.
[23] David G. Norris,et al. An Investigation of Functional and Anatomical Connectivity Using Magnetic Resonance Imaging , 2002, NeuroImage.
[24] Bengt Jönsson,et al. Restricted Diffusion in Cylindrical Geometry , 1995 .
[25] Stephen M. Smith,et al. Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.
[26] G Lohmann,et al. LIPSIA--a new software system for the evaluation of functional magnetic resonance images of the human brain. , 2001, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.
[27] Timothy Edward John Behrens,et al. Characterization and propagation of uncertainty in diffusion‐weighted MR imaging , 2003, Magnetic resonance in medicine.
[28] G. Wahba. Spline Interpolation and Smoothing on the Sphere , 1981 .
[29] C. Herz. BESSEL FUNCTIONS OF MATRIX ARGUMENT , 1955 .
[30] P. Basser,et al. Estimation of the effective self-diffusion tensor from the NMR spin echo. , 1994, Journal of magnetic resonance. Series B.
[31] N. Metropolis,et al. Equation of State Calculations by Fast Computing Machines , 1953, Resonance.
[32] P. V. van Zijl,et al. Three‐dimensional tracking of axonal projections in the brain by magnetic resonance imaging , 1999, Annals of neurology.
[33] Daniel C. Alexander,et al. Maximum Entropy Spherical Deconvolution for Diffusion MRI , 2005, IPMI.
[34] G. S. Watson,et al. Equatorial distributions on a sphere , 1965 .
[35] M. Horsfield,et al. Optimal strategies for measuring diffusion in anisotropic systems by magnetic resonance imaging , 1999, Magnetic resonance in medicine.
[36] S. Shipp,et al. The functional logic of cortical connections , 1988, Nature.
[37] Calyampudi Radhakrishna Rao,et al. Bayesian thinking : modeling and computation , 2005 .
[38] A. Anwander,et al. Connectivity-Based Parcellation of Broca's Area. , 2006, Cerebral cortex.
[39] M. Torrens. Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268 , 1990 .
[40] D. Tuch. Q‐ball imaging , 2004, Magnetic resonance in medicine.
[41] A. Gelman,et al. Weak convergence and optimal scaling of random walk Metropolis algorithms , 1997 .
[42] Brian B. Avants,et al. An Automated Approach to Connectivity-Based Partitioning of Brain Structures , 2005, MICCAI.
[43] C. Robert,et al. Controlled MCMC for Optimal Sampling , 2001 .
[44] Michael Brady,et al. Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.
[45] Denis Le Bihan,et al. Looking into the functional architecture of the brain with diffusion MRI , 2003, Nature Reviews Neuroscience.
[46] Christopher Bingham. An Antipodally Symmetric Distribution on the Sphere , 1974 .
[47] Yu-Chung N. Cheng,et al. Magnetic Resonance Imaging: Physical Principles and Sequence Design , 1999 .
[48] Guy B. Williams,et al. Inference of multiple fiber orientations in high angular resolution diffusion imaging , 2005, Magnetic resonance in medicine.
[49] Karl J. Friston,et al. Classical and Bayesian Inference in Neuroimaging: Applications , 2002, NeuroImage.
[50] A. Wood,et al. Saddlepoint approximations for the Bingham and Fisher–Bingham normalising constants , 2005 .
[51] Derek K. Jones,et al. Diffusion‐tensor MRI: theory, experimental design and data analysis – a technical review , 2002 .
[52] G Tononi,et al. Theoretical neuroanatomy: relating anatomical and functional connectivity in graphs and cortical connection matrices. , 2000, Cerebral cortex.
[53] Baba C. Vemuri,et al. Resolution of complex tissue microarchitecture using the diffusion orientation transform (DOT) , 2006, NeuroImage.
[54] Philip A. Cook,et al. Modelling noise-induced fibre-orientation error in diffusion-tensor MRI , 2004, 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821).
[55] W. K. Hastings,et al. Monte Carlo Sampling Methods Using Markov Chains and Their Applications , 1970 .
[56] Geoffrey J M Parker,et al. A framework for a streamline‐based probabilistic index of connectivity (PICo) using a structural interpretation of MRI diffusion measurements , 2003, Journal of magnetic resonance imaging : JMRI.
[57] P. Hagmann,et al. Mapping complex tissue architecture with diffusion spectrum magnetic resonance imaging , 2005, Magnetic resonance in medicine.
[58] H. Pfeifer. Principles of Nuclear Magnetic Resonance Microscopy , 1992 .
[59] Timothy Edward John Behrens,et al. Changes in connectivity profiles define functionally distinct regions in human medial frontal cortex. , 2004, Proceedings of the National Academy of Sciences of the United States of America.
[60] Kalvis M. Jansons,et al. Persistent angular structure: new insights from diffusion magnetic resonance imaging data , 2003 .