Notebook

In [1]:

import osmosis

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osmosis.__file__

Out[2]:

'/home/arokem/usr/local/lib/python2.7/site-packages/osmosis/__init__.pyc'

In [3]:

import osmosis.io as oio
import osmosis.model.isotropic as mdm
import nibabel as nib
import os
%matplotlib inline

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Table of Contents¶

[1] Creating white matter masks [2] Remove the ventricles from a white matter mask [3] Aggregating file pieces from parallel computing

Abbreviations¶

Isotropic Models¶

bi_exp_rs - two decaying exponentials fit to the relative diffusion signal (bi-exponential) single_exp_rs - one decaying exponentials fit to the relative diffusion signal (single exponential)

Diffusion Models¶

sfm - sparse fascicle model mfODF_se - multi fODF diffusion model with a single exponential isotropic model mfODF_be - multi fODF diffusion model with a bi-exponential isotropic model sfODF_se - single fODF diffusion model with a single exponential isotropic model sfODF_be - single fODF diffusion model with a bi-exponential isotropic model dtm - diffusion tensor model

Miscellaneous¶

wm - white matter mp - model parameters cod - coefficient of determination ad - axial diffusivity rd - radial diffusivity

Load the data¶

In [4]:

subject_id = "103414"
data_path = "/biac4/wandell/data/klchan13/hcp_data_q3/%s/T1w/Diffusion/"%subject_id

data_file = nib.load(os.path.join(data_path, "data.nii.gz"))
data = data_file.get_data()

wm_data_file = nib.load(os.path.join(data_path,"wm_mask_no_vent.nii.gz"))
wm_data = wm_data_file.get_data()

bvals = np.loadtxt(os.path.join(data_path, "bvals"))
bvecs = np.loadtxt(os.path.join(data_path, "bvecs"))

[1] Creating white matter masks¶

Create a white matter mask using the ribbon file from freesurfer.

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def wm_mask(ribbon_file, data_path, file_name=False):
    """
    Takes a ribbon file and creates a white matter mask.
    """
    ribbon_data = ribbon_file.get_data()
    ribbon_affine = ribbon_file.get_affine()
    
    wm_mask_out = np.zeros(ribbon_data.shape)
    
    # These are labels that should be included in the white matter mask.
    labels = [2, 41, 16, 17, 28, 60, 51, 53, 12, 52, 13, 18, 54, 50, 11, 251, 252, 253, 254, 255, 10, 49, 46, 7]
    for label in labels:
        wm_mask_out[np.where(ribbon_data == label)] = 1
    
    nii = nib.Nifti1Image(wm_mask_out, ribbon_affine)
    if file_name:
        nii.to_filename(os.path.join(data_path, file_name))
    
    return wm_mask_out

Extract a white matter mask from all the brains

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sid_list = ["103414", "105115", "110411", "111312", "113619",
            "115320", "117122", "118730", "118932", "110411"]

for sid in sid_list:
    data_path1 = "/biac4/wandell/data/klchan13/hcp_data_q3/%s/T1w/Diffusion/"%sid
    ribbon_file = nib.load(os.path.join(data_path1, "wm_mask.nii.gz"))
    wm_mask_out = wm_mask(ribbon_file, data_path1, file_name="wm_mask_no_vent.nii.gz")

[2] Remove the ventricles from a white matter mask¶

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wm_mask_no_vent = oio.rm_ventricles(wm_data_file, bvals, bvecs, data, data_path)

[3] Aggregating file pieces from parallel computing¶

SSH to proclus and navigate to the file directory with all the file pieces. Open up ipython and load all the data as well as the io module then enter the following. The COD file pieces are used as an example.

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file_names = ["sfm_cod_multi_se", "sfm_cod_multi_be"]

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missing_files, vol_list = place_files(file_names, 2000, 65, wm_data, data, bvals)