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1 #!/usr/bin/env python
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2 import argparse
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3 import os
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4 import shutil
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5
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14
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6 from dipy.core.gradients import gradient_table
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22
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7 from dipy.data import fetch_sherbrooke_3shell, fetch_stanford_hardi
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14
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8 from dipy.io import read_bvals_bvecs
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11
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9 from matplotlib import pyplot
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0
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10
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26
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11 import nibabel
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12
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13 parser = argparse.ArgumentParser()
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21
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14 parser.add_argument('--drmi_dataset', dest='drmi_dataset', help='Input dataset')
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18
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15 parser.add_argument('--output_nifti1', dest='output_nifti1', help='Output Nifti1 dataset')
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28
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16 parser.add_argument('--output_nifti1_extra_files', dest='output_nifti1_extra_files', help='Output Nifti1 extra files')
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14
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17 parser.add_argument('--output_png', dest='output_png', help='Output dataset')
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18
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19 args = parser.parse_args()
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20
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28
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21 def move_directory_files(source_dir, destination_dir, copy=False, remove_source_dir=False):
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22 source_directory = os.path.abspath(source_dir)
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23 destination_directory = os.path.abspath(destination_dir)
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24 if not os.path.isdir(destination_directory):
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25 os.makedirs(destination_directory)
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26 for dir_entry in os.listdir(source_directory):
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27 source_entry = os.path.join(source_directory, dir_entry)
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28 if copy:
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29 shutil.copy(source_entry, destination_directory)
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30 else:
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31 shutil.move(source_entry, destination_directory)
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32 if remove_source_dir:
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33 os.rmdir(source_directory)
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34
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9
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35 # Get input data.
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21
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36 input_dir = args.drmi_dataset
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28
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37 if input_dir == 'stanford_hardi':
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38 fetch_stanford_hardi()
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39 fdwi = os.path.join(input_dir, 'HARDI150.nii.gz')
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40 fbval = os.path.join(input_dir, 'HARDI150.bval')
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41 fbvec = os.path.join(input_dir, 'HARDI150.bvec')
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42 elif input_dir == 'sherbrooke_3shell':
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43 fetch_sherbrooke_3shell()
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23
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44 fdwi = os.path.join(input_dir, 'HARDI193.nii.gz')
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45 fbval = os.path.join(input_dir, 'HARDI193.bval')
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46 fbvec = os.path.join(input_dir, 'HARDI193.bvec')
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47 # Load the dMRI datasets.
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48 img = nibabel.load(fdwi)
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49 data = img.get_data()
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50 # data is a 4D array where the first 3 dimensions are the i, j,
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51 # k voxel coordinates and the last dimension is the number of
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52 # non-weighted (S0s) and diffusion-weighted volumes.
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53 # Visualize the results using matplotlib.
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54 axial_middle = data.shape[2] // 2
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55 pyplot.subplot(1, 2, 1).set_axis_off()
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56 pyplot.imshow(data[:, :, axial_middle, 0].T, cmap='gray', origin='lower')
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57 pyplot.subplot(1, 2, 2).set_axis_off()
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58 pyplot.imshow(data[:, :, axial_middle, 10].T, cmap='gray', origin='lower')
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59 pyplot.savefig('data.png', bbox_inches='tight')
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60 shutil.move('data.png', args.output_png)
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61 # Load the b-values and b-vectors.
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62 bvals, bvecs = read_bvals_bvecs(fbval, fbvec)
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63 gtab = gradient_table(bvals, bvecs)
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64 # gtab can be used to tell what part of the data is the S0
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65 # volumes (volumes which correspond to b-values of 0).
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66 S0s = data[:, :, :, gtab.b0s_mask]
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67 # Save this in a new Nifti file.
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68 nibabel.save(nibabel.Nifti1Image(S0s, img.affine), 'output.nii')
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69 shutil.move('output.nii', args.output_nifti1)
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70 # Move the entire contents of input_dir to output_nifti1_extra_files.
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71 move_directory_files(input_dir, args.output_nifti1_extra_files)
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