Tractography Parameters

This page documents the configuration options for controlling tractography in pyAFQ. These parameters can be set in your configuration file or passed as arguments when using the API.

Example Usage

from AFQ.api.group import GroupAFQ
import AFQ.data.fetch as afd
import os.path as op

afd.organize_stanford_data()

myafq = GroupAFQ(
    bids_path=op.join(afd.afq_home, 'stanford_hardi'),
    preproc_pipeline='vistasoft',
    tracking_params=dict(
        n_seeds=25000,
        random_seeds=True,
        rng_seed=2025,
        trx=True)
    )

Tractography Parameter Reference

AFQ.tractography.tractography.track(params_file, directions='prob', max_angle=30.0, sphere=None, seed_mask=None, seed_threshold=0.5, thresholds_as_percentages=False, n_seeds=1, random_seeds=False, rng_seed=None, stop_mask=None, stop_threshold=0.5, step_size=0.5, minlen=50, maxlen=250, odf_model='CSD', basis_type='descoteaux07', legacy=True, tracker='local', trx=False)

Tractography

Parameters

params_filestr, nibabel img.

Full path to a nifti file containing CSD spherical harmonic coefficients, or nibabel img with model params.

directionsstr

How tracking directions are determined. One of: {“det” | “prob”} Default: “prob”

max_anglefloat, optional.

The maximum turning angle in each step. Default: 30

sphereSphere object, optional.

The discretization of direction getting. default: dipy.data.default_sphere.

seed_maskarray, optional.

Float or binary mask describing the ROI within which we seed for tracking. Default to the entire volume (all ones).

seed_thresholdfloat, optional.

A value of the seed_mask above which tracking is seeded. Default to 0.

n_seedsint or 2D array, optional.

The seeding density: if this is an int, it is is how many seeds in each voxel on each dimension (for example, 2 => [2, 2, 2]). If this is a 2D array, these are the coordinates of the seeds. Unless random_seeds is set to True, in which case this is the total number of random seeds to generate within the mask. Default: 1

random_seedsbool

Whether to generate a total of n_seeds random seeds in the mask. Default: False.

rng_seedint

random seed used to generate random seeds if random_seeds is set to True. Default: None

stop_maskarray or str, optional.

If array: A float or binary mask that determines a stopping criterion (e.g. FA). If tuple: it contains a sequence that is interpreted as: (pve_wm, pve_gm, pve_csf), each item of which is either a string (full path) or a nibabel img to be used in particle filtering tractography. A tuple is required if tracker is set to “pft”. Defaults to no stopping (all ones).

stop_thresholdfloat or tuple, optional.

If float, this a value of the stop_mask below which tracking is terminated (and stop_mask has to be an array). If str, “CMC” for Continuous Map Criterion [Girard2014].

“ACT” for Anatomically-constrained tractography [Smith2012].

A string is required if the tracker is set to “pft”. Defaults to 0 (this means that if no stop_mask is passed, we will stop only at the edge of the image).

thresholds_as_percentagesbool, optional

Interpret seed_threshold and stop_threshold as percentages of the total non-nan voxels in the seed and stop mask to include (between 0 and 100), instead of as a threshold on the values themselves. Default: False

step_sizefloat, optional.

The size of a step (in mm) of tractography. Default: 0.5

minlen: int, optional

The miminal length (mm) in a streamline. Default: 20

maxlen: int, optional

The miminal length (mm) in a streamline. Default: 250

odf_modelstr or Definition, optional

Can be either a string or Definition. If a string, it must be one of {“DTI”, “CSD”, “DKI”, “GQ”, “RUMBA”}. If a Definition, we assume it is a definition of a file containing Spherical Harmonics coefficients. Defaults to use “CSD”

basis_typestr, optional

The spherical harmonic basis type used to represent the coefficients. One of {“descoteaux07”, “tournier07”}. Deafult: “descoteaux07”

legacybool, optional

Whether to use the legacy implementation of the direction getter. See Dipy documentation for more details. Default: True

trackerstr, optional

Which strategy to use in tracking. This can be the standard local tracking (“local”) or Particle Filtering Tracking ([Girard2014]). One of {“local”, “pft”}. Default: “local”

trxbool, optional

Whether to return the streamlines compatible with input to TRX file (i.e., as a LazyTractogram class instance). Default: False

Returns

list of streamlines ()

References

Girard2014(1,2)

Girard, G., Whittingstall, K., Deriche, R., & Descoteaux, M. Towards quantitative connectivity analysis: reducing tractography biases. NeuroImage, 98, 266-278, 2014.

Smith2012

Smith RE, Tournier JD, Calamante F, Connelly A. Anatomically-constrained tractography: improved diffusion MRI streamlines tractography through effective use of anatomical information. Neuroimage. 2012 Sep;62(3):1924-38. doi: 10.1016/j.neuroimage.2012.06.005. Epub 2012 Jun 13.