Read/Write streamline files

Overview

DIPY_ can read and write many different file formats. In this example we give a short introduction on how to use it for loading or saving streamlines. The new statefull tractogram class was made to reduce errors caused by spatial transformation and complex file format convention.

Read Frequently Asked Questions

import os

import nibabel as nib
import numpy as np
from dipy.io.stateful_tractogram import Space, StatefulTractogram
from dipy.io.streamline import load_tractogram, save_tractogram
from dipy.io.utils import (create_nifti_header, get_reference_info,
                           is_header_compatible)
from dipy.tracking.streamline import select_random_set_of_streamlines
from dipy.tracking.utils import density_map

from dipy.data.fetcher import (fetch_file_formats,
                               get_file_formats)

First fetch the dataset that contains 5 tractography file of 5 file formats:

  • cc_m_sub.trk

  • laf_m_sub.tck

  • lpt_m_sub.fib

  • raf_m_sub.vtk

  • rpt_m_sub.dpy

And their reference anatomy, common to all 5 files:

  • template0.nii.gz

fetch_file_formats()
bundles_filename, ref_anat_filename = get_file_formats()
for filename in bundles_filename:
    print(os.path.basename(filename))
reference_anatomy = nib.load(ref_anat_filename)

Load tractogram will support 5 file formats, functions like load_trk or load_tck will simply be restricted to one file format

TRK files contain their own header (when writen properly), so they technically do not need a reference. (See how below)

cc_trk = load_tractogram(bundles_filename[0], 'same')

cc_sft = load_tractogram(bundles_filename[0], reference_anatomy)
print(cc_sft)
laf_sft = load_tractogram(bundles_filename[1], reference_anatomy)
raf_sft = load_tractogram(bundles_filename[3], reference_anatomy)

These files contain invalid streamlines (negative values once in voxel space) This is not considered a valid tractography file, but it is possible to load it anyway.

lpt_sft = load_tractogram(bundles_filename[2], reference_anatomy,
                          bbox_valid_check=False)
rpt_sft = load_tractogram(bundles_filename[4], reference_anatomy,
                          bbox_valid_check=False)

The function load_tractogram requires a reference, any of the following inputs is considered valid (as long as they are in the same share space) - Nifti filename - Trk filename - nib.nifti1.Nifti1Image - nib.streamlines.trk.TrkFile - nib.nifti1.Nifti1Header - Trk header (dict) - Stateful Tractogram

The reason why this parameters is required is to guarantee all informations related to space attribute are always present.

affine, dimensions, voxel_sizes, voxel_order = get_reference_info(
    reference_anatomy)
print(affine)
print(dimensions)
print(voxel_sizes)
print(voxel_order)

If you have a Trk file that was generated using a particular anatomy, to be considered valid all fields must correspond between the headers. It can be easily verified using this function, which also accept the same variety of input as get_reference_info

print(is_header_compatible(reference_anatomy, bundles_filename[0]))

If a TRK was generated with a valid header, but the reference NIFTI was lost a header can be generated to then generate a fake NIFTI file.

If you wish to manually save Trk and Tck file using nibabel streamlines API for more freedom of action (not recommended for beginners) you can create a valid header using create_tractogram_header

nifti_header = create_nifti_header(affine, dimensions, voxel_sizes)
nib.save(nib.Nifti1Image(np.zeros(dimensions), affine, nifti_header),
         'fake.nii.gz')
nib.save(reference_anatomy, os.path.basename(ref_anat_filename))

Once loaded, no matter the original file format, the stateful tractogram is self-contained and maintains a valid state. By requiring a reference the tractogram’s spatial transformation can be easily manipulated.

Let’s save all files as TRK to visualize in TrackVis for example. However, when loaded the lpt and rpt files contain invalid streamlines and for particular operations/tools/functions it is safer to remove them

save_tractogram(cc_sft, 'cc.trk')
save_tractogram(laf_sft, 'laf.trk')
save_tractogram(raf_sft, 'raf.trk')

print(lpt_sft.is_bbox_in_vox_valid())
lpt_sft.remove_invalid_streamlines()
print(lpt_sft.is_bbox_in_vox_valid())
save_tractogram(lpt_sft, 'lpt.trk')

print(rpt_sft.is_bbox_in_vox_valid())
rpt_sft.remove_invalid_streamlines()
print(rpt_sft.is_bbox_in_vox_valid())
save_tractogram(rpt_sft, 'rpt.trk')

Some functions in DIPY require streamlines to be in voxel space so computation can be perfomed on a grid (connectivity matrix, ROIs masking, density map). The stateful tractogram class provides safe functions for such manipulation. These functions can be called safely over and over, by knowing in which state the tractogram is operating, and compute only necessary transformations

No matter the state, functions such as save_tractogram or removing_invalid_coordinates can be called safely and the transformations are handled internally when needed.

cc_sft.to_voxmm()
print(cc_sft.space)
cc_sft.to_rasmm()
print(cc_sft.space)

Now lets move them all to voxel space, subsample them to 100 streamlines, compute a density map and save everything for visualisation in another software such as Trackvis or MI-Brain.

To access volume information in a grid, the corner of the voxel must be considered the origin in order to prevent negative values. Any operation doing interpolation or accessing a grid must use the function ‘to_vox()’ and ‘to_corner()’

cc_sft.to_vox()
laf_sft.to_vox()
raf_sft.to_vox()
lpt_sft.to_vox()
rpt_sft.to_vox()

cc_sft.to_corner()
laf_sft.to_corner()
raf_sft.to_corner()
lpt_sft.to_corner()
rpt_sft.to_corner()

cc_streamlines_vox = select_random_set_of_streamlines(cc_sft.streamlines,
                                                      1000)
laf_streamlines_vox = select_random_set_of_streamlines(laf_sft.streamlines,
                                                       1000)
raf_streamlines_vox = select_random_set_of_streamlines(raf_sft.streamlines,
                                                       1000)
lpt_streamlines_vox = select_random_set_of_streamlines(lpt_sft.streamlines,
                                                       1000)
rpt_streamlines_vox = select_random_set_of_streamlines(rpt_sft.streamlines,
                                                       1000)

# Same dimensions for every stateful tractogram, can be re-use
affine, dimensions, voxel_sizes, voxel_order = cc_sft.space_attributes
cc_density = density_map(cc_streamlines_vox, np.eye(4), dimensions)
laf_density = density_map(laf_streamlines_vox, np.eye(4), dimensions)
raf_density = density_map(raf_streamlines_vox, np.eye(4), dimensions)
lpt_density = density_map(lpt_streamlines_vox, np.eye(4), dimensions)
rpt_density = density_map(rpt_streamlines_vox, np.eye(4), dimensions)

Replacing streamlines is possible, but if the state was modified between operations such as this one is not recommended: -> cc_sft.streamlines = cc_streamlines_vox

It is recommended to re-create a new StatefulTractogram object and explicitly specify in which space the streamlines are. Be careful to follow the order of operations.

If the tractogram was from a Trk file with metadata, this will be lost. If you wish to keep metadata while manipulating the number or the order look at the function StatefulTractogram.remove_invalid_streamlines() for more details

It is important to mention that once the object is created in a consistent state the save_tractogram function will save a valid file. And then the function load_tractogram will load them in a valid state.

cc_sft = StatefulTractogram(cc_streamlines_vox, reference_anatomy, Space.VOX)
laf_sft = StatefulTractogram(laf_streamlines_vox, reference_anatomy, Space.VOX)
raf_sft = StatefulTractogram(raf_streamlines_vox, reference_anatomy, Space.VOX)
lpt_sft = StatefulTractogram(lpt_streamlines_vox, reference_anatomy, Space.VOX)
rpt_sft = StatefulTractogram(rpt_streamlines_vox, reference_anatomy, Space.VOX)

print(len(cc_sft), len(laf_sft), len(raf_sft), len(lpt_sft), len(rpt_sft))
save_tractogram(cc_sft, 'cc_1000.trk')
save_tractogram(laf_sft, 'laf_1000.trk')
save_tractogram(raf_sft, 'raf_1000.trk')
save_tractogram(lpt_sft, 'lpt_1000.trk')
save_tractogram(rpt_sft, 'rpt_1000.trk')

nib.save(nib.Nifti1Image(cc_density, affine, nifti_header),
         'cc_density.nii.gz')
nib.save(nib.Nifti1Image(laf_density, affine, nifti_header),
         'laf_density.nii.gz')
nib.save(nib.Nifti1Image(raf_density, affine, nifti_header),
         'raf_density.nii.gz')
nib.save(nib.Nifti1Image(lpt_density, affine, nifti_header),
         'lpt_density.nii.gz')
nib.save(nib.Nifti1Image(rpt_density, affine, nifti_header),
         'rpt_density.nii.gz')

Example source code

You can download the full source code of this example. This same script is also included in the dipy source distribution under the doc/examples/ directory.