Diffusion tensor fiber tracking of human brain connectivity: aquisition methods, reliability analysis and biological results
Tóm tắt
We present a description, biological results and a reliability analysis for the method of diffusion tensor tracking (DTT) of white matter fiber pathways. In DTT, diffusion‐tensor MRI (DT‐MRI) data are collected and processed to visualize the line trajectories of fiber bundles within white matter (WM) pathways of living humans. A detailed description of the data acquisition is given. Technical aspects and experimental results are illustrated for the geniculo‐calcarine tract with broad projections to visual cortex, occipital and parietal U‐fibers, and the temporo‐calcarine ventral pathway. To better understand sources of error and to optimize the method, accuracy and precision were analyzed by computer simulations. In the simulations, noisy DT‐MRI data were computed that would be obtained for a WM pathway having a helical trajectory passing through gray matter. The error vector between the real and ideal track was computed, and random errors accumulated with the square root of track length consistent with a random‐walk process. Random error was most dependent on signal‐to‐noise ratio, followed by number of averages, pathway anisotropy and voxel size, in decreasing order. Systematic error only occurred for a few conditions, and was most dependent on the stepping algorithm, anisotropy of the surrounding tissue, and non‐equal voxel dimensions. Both random and systematic errors were typically below the voxel dimension. Other effects such as track rebound and track recovery also depended on experimental conditions. The methods, biological results and error analysis herein may improve the understanding and optimization of DTT for use in various applications in neuroscience and medicine. Copyright © 2002 John Wiley & Sons, Ltd.
Từ khóa
Tài liệu tham khảo
Lori NF, 1999, Tracking neuronal fibers in the living human brain with diffusion MRI
LaVail JH, 1975, The retrograde transport method, Fed. Proc., 34, 1618
Steinbusch HW, 1988, Immunohistochemical localization of monoamines and cyclic nucleotides. Their application in quantitative immunofluorescence studies and tracing monoaminergic neuronal connections, Acta Histochem. Suppl., 35, 86
Dejerine J, 1895
Critchley M, 1966, The Parietal Lobe
BasserPJ MattielloJ Le BihanD.Anisotropic diffusion: MR diffusion tensor imaging. InDiffusion and Perfusion Magnetic Resonance Imaging: Applications to Functional MRI Le Bihan D (ed.). Raven Press: New York 1995;140–149.
Moseley ME, 1990, Diffusion‐weighted MR imaging of acute stroke: correlation with T2‐weighted and magnetic susceptibility‐enhanced MR imaging in cats, Am. J. Neuroradiol., 11, 423
Muthupallai R, 1999, Navigator aided, multishot EPI diffusion images of brain with complete orientation and anisotropy information
Lori NF, 2001, Diffusion Tensor Tracking of Neuronal Fiber Pathways in the Living Human Brain. PhD Thesis, Physics
Lori NF, 2001, Diffusion NMR and MRI: From the Single Molecule to the Entire Human Brain, 26
Lori NF, 2001, Diffusion MRI tracking of amygdalo‐calcarine pathways: replication and detailed error study, 83
Snyder AZ, 1995, Quantification of Brain Function Using PET, 131
Press WH, 1992, Numerical Recipes in C: the Art of Scientific Computing
Crank J, 1975, The Mathematics of Diffusion
Tuch DS, 1999, High angular resolution diffusion imaging of the human brain
WedeenVJ ReeseTG TuchDS WeigelMR DouJ‐G WeiskoffRM ChesslerD.Mapping fiber orientation spectra in cerebral white matter with Fourier‐transform diffusion MRI. In Proceedings of the International Society for Magnetic Resonance in Medicine Denver CO 2000;82.
LoriNF ConturoTE.Diffusion time assignment and q‐space formalism for finite‐duration diffusion‐encoding gradients: simulation of 3D random walks. InDiffusion MRI: Biophysical Issues Saint‐Malo France 10–12 March 2002;9–12.
PierpaoliC BasserPJ.Toward a quantitative assessment of diffusion anisotropyMagn. Reson. Med.1996; 36:893–906[Published erratum appears inMagn. Reson. Med. 1997;37: 972].
Lori NF, 2000, Diffusion tensor tracking of human neuronal fiber bundles: simulation of effects of noise, voxel size, and data interpolation, 775
Lazar M, 2001, Error analysis of white matter tracking algorithms (streamlines and tensorlines) for DT‐MRI, 506
Cantor CR, 1980, Biophysical Chemistry Part I: the Conformation of Biological Macromolecules
Leon‐Garcia A, 1994, Probability and Random Processes for Electrical Engineers