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1 #!/usr/bin/env python
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2 import argparse
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3 import shutil
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4
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5 from dipy.data import fetch_sherbrooke_3shell
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6 from dipy.data import fetch_stanford_hardi
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7 from dipy.data import get_sphere
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8 from dipy.data import read_sherbrooke_3shell
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9 from dipy.data import read_stanford_hardi
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10 from dipy.direction import peaks_from_model
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11 from dipy.io.image import save_nifti
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12 from dipy.reconst.csdeconv import (ConstrainedSphericalDeconvModel, auto_response)
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13 from dipy.reconst.dti import TensorModel
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14 from dipy.segment.mask import median_otsu
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15 from dipy.tracking.local import LocalTracking, ThresholdTissueClassifier
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16 from dipy.tracking.streamline import Streamlines
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17 from dipy.tracking.utils import random_seeds_from_mask
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18 from dipy.viz import actor, window
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19
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20 import numpy as np
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21
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22 parser = argparse.ArgumentParser()
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23 parser.add_argument('--drmi_dataset', dest='drmi_dataset', help='Input dataset')
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24 parser.add_argument('--output_csd_direction_field', dest='output_csd_direction_field', help='Output csd direction field dataset')
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25 parser.add_argument('--output_det_streamlines', dest='output_det_streamlines', help='Output det streamlines dataset')
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26 parser.add_argument('--output_fa_map', dest='output_fa_map', help='Output fa map dataset')
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27
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28 args = parser.parse_args()
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29
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30 interactive = False
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31
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32 # Get input data.
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33 input_dir = args.drmi_dataset
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34 if input_dir == 'sherbrooke_3shell':
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35 fetch_sherbrooke_3shell()
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36 img, gtab = read_sherbrooke_3shell()
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37 elif input_dir == 'stanford_hardi':
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38 fetch_stanford_hardi()
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39 img, gtab = read_stanford_hardi()
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40
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41 data = img.get_data()
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42 maskdata, mask = median_otsu(data, 3, 1, False, vol_idx=range(10, 50), dilate=2)
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43
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44 response, ratio = auto_response(gtab, data, roi_radius=10, fa_thr=0.7)
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45 csd_model = ConstrainedSphericalDeconvModel(gtab, response)
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46 sphere = get_sphere('symmetric724')
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47 csd_peaks = peaks_from_model(model=csd_model, data=data, sphere=sphere, mask=mask, relative_peak_threshold=.5, min_separation_angle=25, parallel=True)
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48
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49 tensor_model = TensorModel(gtab, fit_method='WLS')
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50 tensor_fit = tensor_model.fit(data, mask)
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51 fa = tensor_fit.fa
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52
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53 tissue_classifier = ThresholdTissueClassifier(fa, 0.1)
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54 seeds = random_seeds_from_mask(fa > 0.3, seeds_count=1)
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55
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56 ren = window.Renderer()
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57 ren.add(actor.peak_slicer(csd_peaks.peak_dirs, csd_peaks.peak_values, colors=None))
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58 window.record(ren, out_path='csd_direction_field.png', size=(900, 900))
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59 shutil.move('csd_direction_field.png', args.output_csd_direction_field)
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60
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61 streamline_generator = LocalTracking(csd_peaks, tissue_classifier, seeds, affine=np.eye(4), step_size=0.5)
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62 streamlines = Streamlines(streamline_generator)
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63
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64 ren.clear()
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65 ren.add(actor.line(streamlines))
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66 window.record(ren, out_path='det_streamlines.png', size=(900, 900))
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67 shutil.move('det_streamlines.png', args.output_det_streamlines)
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68
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69 save_nifti('fa_map.nii', fa, img.affine)
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70 shutil.move('fa_map.nii', args.output_fa_map)
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