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Merge pull request #1004 from karanphil/bundle_fixel
[ENH] Bundle fixel analysis script
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import itertools | ||
import multiprocessing | ||
import numpy as np | ||
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from dipy.io.streamline import load_tractogram | ||
from scilpy.tractanalysis.grid_intersections import grid_intersections | ||
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def _fixel_density_parallel(args): | ||
peaks = args[0] | ||
max_theta = args[1] | ||
dps_key = args[2] | ||
bundle = args[3] | ||
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sft = load_tractogram(bundle, 'same') | ||
sft.to_vox() | ||
sft.to_corner() | ||
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fixel_density_maps = np.zeros((peaks.shape[:-1]) + (5,)) | ||
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min_cos_theta = np.cos(np.radians(max_theta)) | ||
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all_crossed_indices = grid_intersections(sft.streamlines) | ||
for i, crossed_indices in enumerate(all_crossed_indices): | ||
segments = crossed_indices[1:] - crossed_indices[:-1] | ||
seg_lengths = np.linalg.norm(segments, axis=1) | ||
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# Remove points where the segment is zero. | ||
# This removes numpy warnings of division by zero. | ||
non_zero_lengths = np.nonzero(seg_lengths)[0] | ||
segments = segments[non_zero_lengths] | ||
seg_lengths = seg_lengths[non_zero_lengths] | ||
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# Those starting points are used for the segment vox_idx computations | ||
seg_start = crossed_indices[non_zero_lengths] | ||
vox_indices = (seg_start + (0.5 * segments)).astype(int) | ||
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normalized_seg = np.reshape(segments / seg_lengths[..., None], (-1, 3)) | ||
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weight = 1 | ||
if dps_key: | ||
weight = sft.data_per_streamline[dps_key][i] | ||
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for vox_idx, seg_dir in zip(vox_indices, normalized_seg): | ||
vox_idx = tuple(vox_idx) | ||
peaks_at_idx = peaks[vox_idx].reshape((5, 3)) | ||
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cos_theta = np.abs(np.dot(seg_dir.reshape((-1, 3)), | ||
peaks_at_idx.T)) | ||
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if (cos_theta > min_cos_theta).any(): | ||
lobe_idx = np.argmax(np.squeeze(cos_theta), axis=0) # (n_segs) | ||
fixel_density_maps[vox_idx][lobe_idx] += weight | ||
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return fixel_density_maps | ||
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def fixel_density(peaks, bundles, dps_key=None, max_theta=45, | ||
nbr_processes=None): | ||
"""Compute the fixel density map per bundle. Can use parallel processing. | ||
Parameters | ||
---------- | ||
peaks : np.ndarray (x, y, z, 15) | ||
Five principal fiber orientations for each voxel. | ||
bundles : list or np.array (N) | ||
List of (N) paths to bundles. | ||
dps_key : string, optional | ||
Key to the data_per_streamline to use as weight instead of the number | ||
of streamlines. | ||
max_theta : int, optional | ||
Maximum angle between streamline and peak to be associated. | ||
nbr_processes : int, optional | ||
The number of subprocesses to use. | ||
Default: multiprocessing.cpu_count() | ||
Returns | ||
------- | ||
fixel_density : np.ndarray (x, y, z, 5, N) | ||
Density per fixel per bundle. | ||
""" | ||
nbr_processes = multiprocessing.cpu_count() \ | ||
if nbr_processes is None or nbr_processes <= 0 \ | ||
else nbr_processes | ||
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pool = multiprocessing.Pool(nbr_processes) | ||
results = pool.map(_fixel_density_parallel, | ||
zip(itertools.repeat(peaks), | ||
itertools.repeat(max_theta), | ||
itertools.repeat(dps_key), | ||
bundles)) | ||
pool.close() | ||
pool.join() | ||
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fixel_density = np.moveaxis(np.asarray(results), 0, -1) | ||
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return fixel_density | ||
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def maps_to_masks(maps, abs_thr, rel_thr, norm, nb_bundles): | ||
"""Compute the fixel density masks from fixel density maps. | ||
Parameters | ||
---------- | ||
maps : np.ndarray (x, y, z, 5, N) | ||
Density per fixel per bundle. | ||
abs_thr : float | ||
Value of density maps threshold to obtain density masks, in number of | ||
streamlines or streamline weighting. | ||
rel_thr : float | ||
Value of density maps threshold to obtain density masks, as a ratio of | ||
the normalized density. Must be between 0 and 1. | ||
norm : string, ["fixel", "voxel"] | ||
Way of normalizing the density maps. If fixel, will normalize the maps | ||
per fixel, in each voxel. If voxel, will normalize the maps per voxel. | ||
nb_bundles : int (N) | ||
Number of bundles (N). | ||
Returns | ||
------- | ||
masks : np.ndarray (x, y, z, 5, N) | ||
Density masks per fixel per bundle. | ||
maps : np.ndarray (x, y, z, 5, N) | ||
Normalized density maps per fixel per bundle. | ||
""" | ||
# Apply a threshold on the number of streamlines | ||
masks_abs = maps > abs_thr | ||
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# Normalizing the density maps per voxel or fixel | ||
fixel_sum = np.sum(maps, axis=-1) | ||
voxel_sum = np.sum(fixel_sum, axis=-1) | ||
for i in range(nb_bundles): | ||
if norm == "voxel": | ||
maps[..., 0, i] /= voxel_sum | ||
maps[..., 1, i] /= voxel_sum | ||
maps[..., 2, i] /= voxel_sum | ||
maps[..., 3, i] /= voxel_sum | ||
maps[..., 4, i] /= voxel_sum | ||
elif norm == "fixel": | ||
maps[..., i] /= fixel_sum | ||
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# Apply a threshold on the normalized density | ||
masks_rel = maps > rel_thr | ||
# Compute the fixel density masks from the rel and abs versions | ||
masks = masks_rel * masks_abs | ||
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return masks.astype(np.uint8), maps |
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# -*- coding: utf-8 -*- | ||
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def test_fixel_density(): | ||
# toDO | ||
pass | ||
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def test_maps_to_masks(): | ||
# toDO | ||
pass |
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