def probal(Threshold=.2, data_list=None, seed='.', one_node=False, two_node=False): time0 = time.time() print("begin loading data, time:", time.time() - time0) data = data_list['DWI'] affine = data_list['affine'] img = data_list['img'] labels = data_list['labels'] gtab = data_list['gtab'] head_mask = data_list['head_mask'] if type(seed) != str: seed_mask = seed else: seed_mask = (labels == 2) * (head_mask == 1) white_matter = (labels == 2) * (head_mask == 1) seeds = utils.seeds_from_mask(seed_mask, affine, density=1) print("begin reconstruction, time:", time.time() - time0) response, ratio = auto_response_ssst(gtab, data, roi_radii=10, fa_thr=0.7) csd_model = ConstrainedSphericalDeconvModel(gtab, response, sh_order=6) csd_fit = csd_model.fit(data, mask=white_matter) csa_model = CsaOdfModel(gtab, sh_order=6) gfa = csa_model.fit(data, mask=white_matter).gfa stopping_criterion = ThresholdStoppingCriterion(gfa, Threshold) print("begin tracking, time:", time.time() - time0) fod = csd_fit.odf(small_sphere) pmf = fod.clip(min=0) prob_dg = ProbabilisticDirectionGetter.from_pmf(pmf, max_angle=30., sphere=small_sphere) streamline_generator = LocalTracking(prob_dg, stopping_criterion, seeds, affine, step_size=.5) streamlines = Streamlines(streamline_generator) sft = StatefulTractogram(streamlines, img, Space.RASMM) if one_node or two_node: sft.to_vox() streamlines = reduct_seed_ROI(sft.streamlines, seed_mask, one_node, two_node) sft = StatefulTractogram(streamlines, img, Space.VOX) sft._vox_to_rasmm() print("begin saving, time:", time.time() - time0) output = 'tractogram_probabilistic.trk' save_trk(sft, output) print("finished, time:", time.time() - time0)
def sfm_tracking(name=None, data_path=None, output_path='.', Threshold=.20, data_list=None, return_streamlines=False, save_track=True, seed='.', minus_ROI_mask='.', one_node=False, two_node=False): time0 = time.time() print("begin loading data, time:", time.time() - time0) if data_list == None: data, affine, img, labels, gtab, head_mask = get_data(name, data_path) else: data = data_list['DWI'] affine = data_list['affine'] img = data_list['img'] labels = data_list['labels'] gtab = data_list['gtab'] head_mask = data_list['head_mask'] if type(seed) != str: seed_mask = seed else: seed_mask = (labels == 2) * (head_mask == 1) white_matter = (labels == 2) * (head_mask == 1) seeds = utils.seeds_from_mask(seed_mask, affine, density=1) print('begin reconstruction, time:', time.time() - time0) from dipy.reconst.csdeconv import auto_response_ssst from dipy.reconst.shm import CsaOdfModel from dipy.data import default_sphere from dipy.direction import peaks_from_model response, ratio = auto_response_ssst(gtab, data, roi_radii=10, fa_thr=0.7) sphere = get_sphere() sf_model = sfm.SparseFascicleModel(gtab, sphere=sphere, l1_ratio=0.5, alpha=0.001, response=response[0]) pnm = peaks_from_model(sf_model, data, sphere, relative_peak_threshold=.5, min_separation_angle=25, mask=white_matter, parallel=True) stopping_criterion = ThresholdStoppingCriterion(pnm.gfa, Threshold) #seeds = utils.seeds_from_mask(white_matter, affine, density=1) print('begin tracking, time:', time.time() - time0) streamline_generator = LocalTracking(pnm, stopping_criterion, seeds, affine, step_size=.5) streamlines = Streamlines(streamline_generator) print('begin saving, time:', time.time() - time0) from dipy.io.stateful_tractogram import Space, StatefulTractogram from dipy.io.streamline import save_trk if save_track: sft = StatefulTractogram(streamlines, img, Space.RASMM) if one_node or two_node: sft.to_vox() streamlines = reduct_seed_ROI(sft.streamlines, seed_mask, one_node, two_node) if type(minus_ROI_mask) != str: streamlines = minus_ROI(streamlines=streamlines, ROI=minus_ROI_mask) sft = StatefulTractogram(streamlines, img, Space.VOX) sft._vox_to_rasmm() output = output_path + '/tractogram_sfm_' + name + '.trk' save_trk(sft, output, streamlines) if return_streamlines: return streamlines
def determine(name=None, data_path=None, output_path='.', Threshold=.20, data_list=None, seed='.', minus_ROI_mask='.', one_node=False, two_node=False): time0 = time.time() print("begin loading data, time:", time.time() - time0) if data_list == None: data, affine, img, labels, gtab, head_mask = get_data(name, data_path) else: data = data_list['DWI'] affine = data_list['affine'] img = data_list['img'] labels = data_list['labels'] gtab = data_list['gtab'] head_mask = data_list['head_mask'] print(type(seed)) if type(seed) != str: seed_mask = seed else: seed_mask = (labels == 2) * (head_mask == 1) white_matter = (labels == 2) * (head_mask == 1) seeds = utils.seeds_from_mask(seed_mask, affine, density=1) print("begin reconstruction, time:", time.time() - time0) response, ratio = auto_response_ssst(gtab, data, roi_radii=10, fa_thr=0.7) csd_model = ConstrainedSphericalDeconvModel(gtab, response, sh_order=6) csd_fit = csd_model.fit(data, mask=white_matter) csa_model = CsaOdfModel(gtab, sh_order=6) gfa = csa_model.fit(data, mask=white_matter).gfa stopping_criterion = ThresholdStoppingCriterion(gfa, Threshold) #from dipy.data import small_sphere print("begin tracking, time:", time.time() - time0) detmax_dg = DeterministicMaximumDirectionGetter.from_shcoeff( csd_fit.shm_coeff, max_angle=30., sphere=default_sphere) streamline_generator = LocalTracking(detmax_dg, stopping_criterion, seeds, affine, step_size=.5) streamlines = Streamlines(streamline_generator) sft = StatefulTractogram(streamlines, img, Space.RASMM) if one_node or two_node: sft.to_vox() streamlines = reduct_seed_ROI(sft.streamlines, seed_mask, one_node, two_node) if type(minus_ROI_mask) != str: streamlines = minus_ROI(streamlines=streamlines, ROI=minus_ROI_mask) sft = StatefulTractogram(streamlines, img, Space.VOX) sft._vox_to_rasmm() print("begin saving, time:", time.time() - time0) output = output_path + '/tractogram_deterministic_' + name + '.trk' save_trk(sft, output) print("finished, time:", time.time() - time0)