AFQ.tractography.gputractography#

Module Contents#

Functions#

gpu_track(data, gtab, seed_img, stop_img, odf_model, ...)

Perform GPU tractography on DWI data.

Attributes#

logger

AFQ.tractography.gputractography.logger[source]#
AFQ.tractography.gputractography.gpu_track(data, gtab, seed_img, stop_img, odf_model, seed_threshold, stop_threshold, thresholds_as_percentages, max_angle, step_size, n_seeds, random_seeds, rng_seed, ngpus, chunk_size)[source]#

Perform GPU tractography on DWI data.

Parameters
datandarray

DWI data.

gtabGradientTable

The gradient table.

seed_imgNifti1Image

Float or binary mask describing the ROI within which we seed for tracking.

stop_imgNifti1Image

A float or binary mask that determines a stopping criterion (e.g. FA).

odf_modelstr, optional

One of {“OPDT”, “CSA”}

seed_thresholdfloat

The value of the seed_img above which tracking is seeded.

stop_thresholdfloat

The value of the stop_img below which tracking is terminated.

thresholds_as_percentagesbool

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.

max_anglefloat

The maximum turning angle in each step.

step_sizefloat

The size of a step (in mm) of tractography.

n_seedsint

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

If True, n_seeds is total number of random seeds to generate. If False, n_seeds encodes the density of seeds to generate.

rng_seedint

random seed used to generate random seeds if random_seeds is set to True. Default: None ngpus : int Number of GPUs to use.

chunk_sizeint

Chunk size for GPU tracking.

Returns
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