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Reuse the CSD sh coefficients if you already have them. #591

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merged 6 commits into from
Nov 17, 2020
Merged

Reuse the CSD sh coefficients if you already have them. #591

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4aa6f8b
Reuse the CSD sh coefficients if you already have them.
arokem Nov 7, 2020
40eb858
Fix: Don't need to pass `self` twice.
arokem Nov 7, 2020
8bbbde4
Load the CSD sh coefficients from file.
arokem Nov 9, 2020
1fc02af
This function is no longer being used, so we can remove it.
arokem Nov 9, 2020
8a0a046
Remove now unused import
36000 Nov 9, 2020
f682a23
Merge branch 'master' into reuse_sh_apm
arokem Nov 16, 2020
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7 changes: 3 additions & 4 deletions AFQ/api.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,6 +15,7 @@
from dipy.io.stateful_tractogram import StatefulTractogram, Space
from dipy.io.gradients import read_bvals_bvecs
from dipy.stats.analysis import afq_profile, gaussian_weights
from dipy.reconst import shm
from dipy.reconst.dki_micro import axonal_water_fraction

from bids.layout import BIDSLayout
Expand All @@ -25,7 +26,6 @@
from AFQ.models.dti import noise_from_b0
from AFQ.models.dki import _fit as dki_fit
from AFQ.models.csd import _fit as csd_fit
from AFQ.models.csd import fit_anisotropic_power_map
import AFQ.tractography as aft
import dipy.reconst.dti as dpy_dti
import dipy.reconst.dki as dpy_dki
Expand Down Expand Up @@ -786,9 +786,8 @@ def _anisotropic_power_map(self, row):
row, '_model-CSD_APM.nii.gz')
if not op.exists(pmap_file):
dwi_data, gtab, img = self._get_data_gtab(row)
mask = self._brain_mask(row)
pmap = fit_anisotropic_power_map(
dwi_data, gtab, mask)
sh_coeff = nib.load(self._csd(row)).get_fdata()
pmap = shm.anisotropic_power(sh_coeff)
pmap = nib.Nifti1Image(pmap, img.affine)
self.log_and_save_nii(pmap, pmap_file)
meta_fname = self._get_fname(row, '_model-CSD_APM.json')
Expand Down
46 changes: 0 additions & 46 deletions AFQ/models/csd.py
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Original file line number Diff line number Diff line change
Expand Up @@ -127,49 +127,3 @@ def fit_csd(data_files, bval_files, bvec_files, mask=None, response=None,
fname = op.join(out_dir, 'csd_sh_coeff.nii.gz')
nib.save(nib.Nifti1Image(csdfit.shm_coeff, aff), fname)
return fname


def fit_anisotropic_power_map(dwi, gtab, mask=None):
"""
Fits an anisotropic power map.

Parameters
----------
dwi : str, ndarray, or nifti1image
Data to greate map with.

gtab : GradientTable
A GradientTable with all the gradient information.

mask : str or nifti1image, optional
mask to mask the data with.
Default: None.

Returns
-------
ndarray containing an anisotropic power map.
"""

if isinstance(dwi, str):
dwi = nib.load(dwi)
if isinstance(dwi, nib.Nifti1Image):
dwi_data = dwi.get_fdata()
else:
dwi_data = dwi

if isinstance(mask, str):
mask = nib.load(mask)
mask = mask.get_fdata()

model = _model(gtab, dwi_data)
sphere = dpd.get_sphere('symmetric724')
peaks = csd.peaks_from_model(
model=model,
data=dwi_data,
sphere=sphere,
relative_peak_threshold=.5,
min_separation_angle=25,
mask=mask)
ap = shm.anisotropic_power(peaks.shm_coeff)

return ap