def test_closest_peak_tracker(): """This tests that the Closest Peak Direction Getter plays nice LocalTracking and produces reasonable streamlines in a simple example. """ sphere = HemiSphere.from_sphere(unit_octahedron) # A simple image with three possible configurations, a vertical tract, # a horizontal tract and a crossing pmf_lookup = np.array([[0., 0., 1.], [1., 0., 0.], [0., 1., 0.], [.5, .5, 0.]]) simple_image = np.array([[0, 1, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0], [2, 3, 2, 2, 2, 0], [0, 1, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0], ]) simple_image = simple_image[..., None] pmf = pmf_lookup[simple_image] seeds = [np.array([1., 1., 0.]), np.array([2., 4., 0.])] mask = (simple_image > 0).astype(float) tc = BinaryTissueClassifier(mask) dg = ClosestPeakDirectionGetter.from_pmf(pmf, 90, sphere, pmf_threshold=0.1) streamlines = Streamlines(LocalTracking(dg, tc, seeds, np.eye(4), 1.)) expected = [np.array([[0., 1., 0.], [1., 1., 0.], [2., 1., 0.], [3., 1., 0.], [4., 1., 0.]]), np.array([[2., 0., 0.], [2., 1., 0.], [2., 2., 0.], [2., 3., 0.], [2., 4., 0.], [2., 5., 0.]])] def allclose(x, y): return x.shape == y.shape and np.allclose(x, y) if not allclose(streamlines[0], expected[0]): raise AssertionError() if not allclose(streamlines[1], expected[1]): raise AssertionError()
def test_closest_peak_tracker(): """This tests that the Closest Peak Direction Getter plays nice LocalTracking and produces reasonable streamlines in a simple example. """ sphere = HemiSphere.from_sphere(unit_octahedron) # A simple image with three possible configurations, a vertical tract, # a horizontal tract and a crossing pmf_lookup = np.array([[0., 0., 1.], [1., 0., 0.], [0., 1., 0.], [.5, .5, 0.]]) simple_image = np.array([[0, 1, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0], [2, 3, 2, 2, 2, 0], [0, 1, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0], ]) simple_image = simple_image[..., None] pmf = pmf_lookup[simple_image] seeds = [np.array([1., 1., 0.]), np.array([2., 4., 0.])] mask = (simple_image > 0).astype(float) tc = BinaryTissueClassifier(mask) dg = ClosestPeakDirectionGetter.from_pmf(pmf, 90, sphere, pmf_threshold=0.1) streamlines = Streamlines(LocalTracking(dg, tc, seeds, np.eye(4), 1.)) expected = [np.array([[0., 1., 0.], [1., 1., 0.], [2., 1., 0.], [3., 1., 0.], [4., 1., 0.]]), np.array([[2., 0., 0.], [2., 1., 0.], [2., 2., 0.], [2., 3., 0.], [2., 4., 0.]])] def allclose(x, y): return x.shape == y.shape and np.allclose(x, y) if not allclose(streamlines[0], expected[0]): raise AssertionError() if not allclose(streamlines[1], expected[1]): raise AssertionError()
**Corpus Callosum Bootstrap Probabilistic Direction Getter** We have created a bootstrapped probabilistic set of streamlines. If you repeat the fiber tracking (keeping all inputs the same) you will NOT get exactly the same set of streamlines. """ """ Example #2: Closest peak direction getter with CSD Model ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ """ from dipy.direction import ClosestPeakDirectionGetter pmf = csd_fit.odf(small_sphere).clip(min=0) peak_dg = ClosestPeakDirectionGetter.from_pmf(pmf, max_angle=30., sphere=small_sphere) peak_streamline_generator = LocalTracking(peak_dg, classifier, seeds, affine, step_size=.5) streamlines = Streamlines(peak_streamline_generator) save_trk("closest_peak_dg_CSD.trk", streamlines, affine, labels.shape) if has_fury: r = window.Renderer() r.add(actor.line(streamlines, colormap.line_colors(streamlines))) window.record(r, out_path='tractogram_closest_peak_dg.png',
We have created a bootstrapped probabilistic set of streamlines. If you repeat the fiber tracking (keeping all inputs the same) you will NOT get exactly the same set of streamlines. We can save the streamlines as a Trackvis file so it can be loaded into other software for visualization or further analysis. """ save_trk("bootstrap_dg_CSD.trk", streamlines, affine, labels.shape) """ Example #2: Closest peak direction getter with CSD Model """ from dipy.direction import ClosestPeakDirectionGetter pmf = csd_fit.odf(small_sphere).clip(min=0) peak_dg = ClosestPeakDirectionGetter.from_pmf(pmf, max_angle=30., sphere=small_sphere) peak_streamline_generator = LocalTracking(peak_dg, classifier, seeds, affine, step_size=.5) streamlines = Streamlines(peak_streamline_generator) renderer.clear() renderer.add(actor.line(streamlines, line_colors(streamlines))) window.record(renderer, out_path='closest_peak_dg_CSD.png', size=(600, 600)) """ .. figure:: closest_peak_dg_CSD.png :align: center **Corpus Callosum Closest Peak Deterministic Direction Getter** We have created a set of streamlines using the closest peak direction getter,
def dwi_dipy_run(dwi_dir, node_size, dir_path, conn_model, parc, atlas_select, network, wm_mask=None): from dipy.reconst.dti import TensorModel, quantize_evecs from dipy.reconst.csdeconv import ConstrainedSphericalDeconvModel, recursive_response from dipy.tracking.local import LocalTracking, ActTissueClassifier from dipy.tracking import utils from dipy.direction import peaks_from_model from dipy.tracking.eudx import EuDX from dipy.data import get_sphere, default_sphere from dipy.core.gradients import gradient_table from dipy.io import read_bvals_bvecs from dipy.tracking.streamline import Streamlines from dipy.direction import ProbabilisticDirectionGetter, ClosestPeakDirectionGetter, BootDirectionGetter from nibabel.streamlines import save as save_trk from nibabel.streamlines import Tractogram ## dwi_dir = '/Users/PSYC-dap3463/Downloads/bedpostx_s002' img_pve_csf = nib.load( '/Users/PSYC-dap3463/Downloads/002_all/tmp/reg_a/t1w_vent_csf_diff_dwi.nii.gz' ) img_pve_wm = nib.load( '/Users/PSYC-dap3463/Downloads/002_all/tmp/reg_a/t1w_wm_in_dwi_bin.nii.gz' ) img_pve_gm = nib.load( '/Users/PSYC-dap3463/Downloads/002_all/tmp/reg_a/t1w_gm_mask_dwi.nii.gz' ) labels_img = nib.load( '/Users/PSYC-dap3463/Downloads/002_all/tmp/reg_a/dwi_aligned_atlas.nii.gz' ) num_total_samples = 10000 tracking_method = 'boot' # Options are 'boot', 'prob', 'peaks', 'closest' procmem = [2, 4] ## if parc is True: node_size = 'parc' dwi_img = "%s%s" % (dwi_dir, '/dwi.nii.gz') nodif_brain_mask_path = "%s%s" % (dwi_dir, '/nodif_brain_mask.nii.gz') bvals = "%s%s" % (dwi_dir, '/bval') bvecs = "%s%s" % (dwi_dir, '/bvec') dwi_img = nib.load(dwi_img) data = dwi_img.get_data() [bvals, bvecs] = read_bvals_bvecs(bvals, bvecs) gtab = gradient_table(bvals, bvecs) gtab.b0_threshold = min(bvals) sphere = get_sphere('symmetric724') # Loads mask and ensures it's a true binary mask mask_img = nib.load(nodif_brain_mask_path) mask = mask_img.get_data() mask = mask > 0 # Fit a basic tensor model first model = TensorModel(gtab) ten = model.fit(data, mask) fa = ten.fa # Tractography if conn_model == 'csd': print('Tracking with csd model...') elif conn_model == 'tensor': print('Tracking with tensor model...') else: raise RuntimeError("%s%s" % (conn_model, ' is not a valid model.')) # Combine seed counts from voxel with seed counts total wm_mask_data = img_pve_wm.get_data() wm_mask_data[0, :, :] = False wm_mask_data[:, 0, :] = False wm_mask_data[:, :, 0] = False seeds = utils.seeds_from_mask(wm_mask_data, density=1, affine=dwi_img.get_affine()) seeds_rnd = utils.random_seeds_from_mask(ten.fa > 0.02, seeds_count=num_total_samples, seed_count_per_voxel=True) seeds_all = np.vstack([seeds, seeds_rnd]) # Load tissue maps and prepare tissue classifier (Anatomically-Constrained Tractography (ACT)) background = np.ones(img_pve_gm.shape) background[(img_pve_gm.get_data() + img_pve_wm.get_data() + img_pve_csf.get_data()) > 0] = 0 include_map = img_pve_gm.get_data() include_map[background > 0] = 1 exclude_map = img_pve_csf.get_data() act_classifier = ActTissueClassifier(include_map, exclude_map) if conn_model == 'tensor': ind = quantize_evecs(ten.evecs, sphere.vertices) streamline_generator = EuDX(a=fa, ind=ind, seeds=seeds_all, odf_vertices=sphere.vertices, a_low=0.05, step_sz=.5) elif conn_model == 'csd': print('Tracking with CSD model...') response = recursive_response( gtab, data, mask=img_pve_wm.get_data().astype('bool'), sh_order=8, peak_thr=0.01, init_fa=0.05, init_trace=0.0021, iter=8, convergence=0.001, parallel=True) csd_model = ConstrainedSphericalDeconvModel(gtab, response) if tracking_method == 'boot': dg = BootDirectionGetter.from_data(data, csd_model, max_angle=30., sphere=default_sphere) elif tracking_method == 'prob': try: print( 'First attempting to build the direction getter directly from the spherical harmonic representation of the FOD...' ) csd_fit = csd_model.fit( data, mask=img_pve_wm.get_data().astype('bool')) dg = ProbabilisticDirectionGetter.from_shcoeff( csd_fit.shm_coeff, max_angle=30., sphere=default_sphere) except: print( 'Sphereical harmonic not available for this model. Using peaks_from_model to represent the ODF of the model on a spherical harmonic basis instead...' ) peaks = peaks_from_model( csd_model, data, default_sphere, .5, 25, mask=img_pve_wm.get_data().astype('bool'), return_sh=True, parallel=True, nbr_processes=procmem[0]) dg = ProbabilisticDirectionGetter.from_shcoeff( peaks.shm_coeff, max_angle=30., sphere=default_sphere) elif tracking_method == 'peaks': dg = peaks_from_model(model=csd_model, data=data, sphere=default_sphere, relative_peak_threshold=.5, min_separation_angle=25, mask=img_pve_wm.get_data().astype('bool'), parallel=True, nbr_processes=procmem[0]) elif tracking_method == 'closest': csd_fit = csd_model.fit(data, mask=img_pve_wm.get_data().astype('bool')) pmf = csd_fit.odf(default_sphere).clip(min=0) dg = ClosestPeakDirectionGetter.from_pmf(pmf, max_angle=30., sphere=default_sphere) streamline_generator = LocalTracking(dg, act_classifier, seeds_all, affine=dwi_img.affine, step_size=0.5) del dg try: del csd_fit except: pass try: del response except: pass try: del csd_model except: pass streamlines = Streamlines(streamline_generator, buffer_size=512) save_trk(Tractogram(streamlines, affine_to_rasmm=dwi_img.affine), 'prob_streamlines.trk') tracks = [sl for sl in streamlines if len(sl) > 1] labels_data = labels_img.get_data().astype('int') labels_affine = labels_img.affine conn_matrix, grouping = utils.connectivity_matrix( tracks, labels_data, affine=labels_affine, return_mapping=True, mapping_as_streamlines=True, symmetric=True) conn_matrix[:3, :] = 0 conn_matrix[:, :3] = 0 return conn_matrix