Implemention of various Tractography methods
these tools are meant to be paired with diffusion reconstruction methods from dipy.reconst
This module uses the trackvis coordinate system, for more information about this coordinate system please see dipy.tracking.utils The following modules also use this coordinate system: dipy.tracking.utils dipy.tracking.integration dipy.reconst.interpolate
Steps along a direction past the closest voxel boundary
Parameters : | voxel_size : array-like
overstep : float
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Methods
__call__ |
ClosestDirectionTracker optimized for NearestNeighbor interpolator
For use with Nearest Neighbor interpolation, directions at each voxel are remembered to avoid recalculating.
Parameters : | model : model
interpolator : interpolator
angle_limit : float [0, 90]
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Methods
reset_cache |
Clear saved directions
A stepper that uses a fixed step size
Methods
__call__ |
An abstract class for fiber-tracking
A stochastic (probabilistic) fiber tracking method
Stochastically tracks streamlines by randomly choosing directions from sphere. The likelihood of a direction being chosen is taken from model.fit(data).odf(sphere). Negative values are set to 0. If no directions less than angle_limit degrees are from the incoming direction have a positive likelihood, the streamline is terminated.
Parameters : | model : model
interpolator : interpolator
mask : array, 3D
take_step : callable
angle_limit : float [0, 90]
seeds : array (N, 3)
sphere : Sphere
max_cross : int or None
maxlen : int
mask_voxel_size : array (3,)
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Creates a streamline from seed
Parameters : | get_direction : callable
take_step : callable
seed : array (3,)
first_step : array (3,)
maxlen : int
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Returns : | streamline : array (N, 3)
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