PURPOSE: To establish a general methodology for quantifying streamline-based diffusion fiber tracking methods in terms of probability of connection between points and/or regions. MATERIALS AND METHODS: The commonly used streamline approach is adapted to exploit the uncertainty in the orientation of the principal direction of diffusion defined for each image voxel. Running the streamline process repeatedly using Monte Carlo methods to exploit this inherent uncertainty generates maps of connection probability. Uncertainty is defined by interpreting the shape of the diffusion orientation profile provided by the diffusion tensor in terms of the underlying microstructure. RESULTS: Two candidates for describing the uncertainty in the diffusion tensor are proposed and maps of probability of connection to chosen start points or regions are generated in a number of major tracts. CONCLUSION: The methods presented provide a generic framework for utilizing streamline methods to generate probabilistic maps of connectivity. Copyright 2003 Wiley-Liss, Inc.
PURPOSE: To establish a general methodology for quantifying streamline-based diffusion fiber tracking methods in terms of probability of connection between points and/or regions. MATERIALS AND METHODS: The commonly used streamline approach is adapted to exploit the uncertainty in the orientation of the principal direction of diffusion defined for each image voxel. Running the streamline process repeatedly using Monte Carlo methods to exploit this inherent uncertainty generates maps of connection probability. Uncertainty is defined by interpreting the shape of the diffusion orientation profile provided by the diffusion tensor in terms of the underlying microstructure. RESULTS: Two candidates for describing the uncertainty in the diffusion tensor are proposed and maps of probability of connection to chosen start points or regions are generated in a number of major tracts. CONCLUSION: The methods presented provide a generic framework for utilizing streamline methods to generate probabilistic maps of connectivity. Copyright 2003 Wiley-Liss, Inc.
Authors: Sarah C Mang; Dmitriy Logashenko; Daniel Gembris; Gabriel Wittum; Wolfgang Grodd; Uwe Klose Journal: MAGMA Date: 2010-01-30 Impact factor: 2.310
Authors: Tim B Dyrby; William F C Baaré; Daniel C Alexander; Jacob Jelsing; Ellen Garde; Lise V Søgaard Journal: Hum Brain Mapp Date: 2011-04 Impact factor: 5.038