""" ================================================= Using Various Stopping Criterion for Tractography ================================================= The stopping criterion determines if the tracking stops or continues at each tracking position. The tracking stops when it reaches an ending region (e.g. low FA, gray matter or corticospinal fluid regions) or exits the image boundaries. The tracking also stops if the direction getter has no direction to follow. Each stopping criterion determines if the stopping is 'valid' or 'invalid'. A streamline is 'valid' when the stopping criterion determines if the streamline stops in a position classified as 'ENDPOINT' or 'OUTSIDEIMAGE'. A streamline is 'invalid' when it stops in a position classified as 'TRACKPOINT' or 'INVALIDPOINT'. These conditions are described below. The 'LocalTracking' generator can be set to output all generated streamlines or only the 'valid' ones. See Girard et al. (2004) [Girard2014]_ and Smith et al.(2012) [Smith2012]_ for more details on these methods. This example is an extension of the :ref:`example_tracking_deterministic` example. We begin by loading the data, creating a seeding mask from white matter voxels of the corpus callosum, fitting a Constrained Spherical Deconvolution (CSD) reconstruction model and creating the maximum deterministic direction getter. """ import matplotlib.pyplot as plt import numpy as np from dipy.core.gradients import gradient_table from dipy.data import get_fnames, default_sphere from dipy.direction import DeterministicMaximumDirectionGetter from dipy.io.gradients import read_bvals_bvecs from dipy.io.image import load_nifti, load_nifti_data from dipy.io.streamline import save_trk from dipy.io.stateful_tractogram import Space, StatefulTractogram from dipy.reconst.csdeconv import (ConstrainedSphericalDeconvModel, auto_response_ssst) from dipy.reconst.dti import fractional_anisotropy, TensorModel from dipy.tracking import utils from dipy.tracking.local_tracking import LocalTracking from dipy.tracking.streamline import Streamlines from dipy.tracking.stopping_criterion import (ActStoppingCriterion, BinaryStoppingCriterion, ThresholdStoppingCriterion) from dipy.viz import window, actor, colormap, has_fury # Enables/disables interactive visualization interactive = False hardi_fname, hardi_bval_fname, hardi_bvec_fname = get_fnames('stanford_hardi') label_fname = get_fnames('stanford_labels') _, _, f_pve_wm = get_fnames('stanford_pve_maps') data, affine, hardi_img = load_nifti(hardi_fname, return_img=True) labels = load_nifti_data(label_fname) bvals, bvecs = read_bvals_bvecs(hardi_bval_fname, hardi_bvec_fname) gtab = gradient_table(bvals, bvecs) white_matter = load_nifti_data(f_pve_wm) seed_mask = (labels == 2) seed_mask[white_matter < 0.5] = 0 seeds = utils.seeds_from_mask(seed_mask, affine, density=2) response, ratio = auto_response_ssst(gtab, data, roi_radii=10, fa_thr=0.7) csd_model = ConstrainedSphericalDeconvModel(gtab, response) csd_fit = csd_model.fit(data, mask=white_matter) dg = DeterministicMaximumDirectionGetter.from_shcoeff(csd_fit.shm_coeff, max_angle=30., sphere=default_sphere) ############################################################################### # Threshold Stopping Criterion # ============================ # A scalar map can be used to define where the tracking stops. The threshold # stopping criterion uses a scalar map to stop the tracking whenever the # interpolated scalar value is lower than a fixed threshold. Here, we show # an example using the fractional anisotropy (FA) map of the DTI model. # The threshold stopping criterion uses a trilinear interpolation at the # tracking position. # # **Parameters** # # - metric_map: numpy array [:, :, :] # - threshold: float # # **Stopping States** # # - 'ENDPOINT': stops at a position where metric_map < threshold; the streamline # reached the target stopping area. # - 'OUTSIDEIMAGE': stops at a position outside of metric_map; the streamline # reached an area outside the image where no direction data is available. # - 'TRACKPOINT': stops at a position because no direction is available; the # streamline is stopping where metric_map >= threshold, but there is no valid # direction to follow. # - 'INVALIDPOINT': N/A. tensor_model = TensorModel(gtab) tenfit = tensor_model.fit(data, mask=labels > 0) FA = fractional_anisotropy(tenfit.evals) threshold_criterion = ThresholdStoppingCriterion(FA, .2) fig = plt.figure() mask_fa = FA.copy() mask_fa[mask_fa < 0.2] = 0 plt.xticks([]) plt.yticks([]) plt.imshow(mask_fa[:, :, data.shape[2] // 2].T, cmap='gray', origin='lower', interpolation='nearest') fig.tight_layout() fig.savefig('threshold_fa.png') ############################################################################### # .. figure:: threshold_fa.png # :align: center # # **Thresholded fractional anisotropy map.** streamline_generator = LocalTracking(dg, threshold_criterion, seeds, affine, step_size=.5, return_all=True) streamlines = Streamlines(streamline_generator) sft = StatefulTractogram(streamlines, hardi_img, Space.RASMM) save_trk(sft, "tractogram_probabilistic_thresh_all.trk") if has_fury: scene = window.Scene() scene.add(actor.line(streamlines, colormap.line_colors(streamlines))) window.record(scene, out_path='tractogram_deterministic_thresh_all.png', size=(800, 800)) if interactive: window.show(scene) ############################################################################### # .. figure:: tractogram_deterministic_thresh_all.png # :align: center # # **Corpus Callosum using deterministic tractography with a thresholded # fractional anisotropy mask.** ############################################################################### # Binary Stopping Criterion # ========================= # A binary mask can be used to define where the tracking stops. The binary # stopping criterion stops the tracking whenever the tracking position is outside # the mask. Here, we show how to obtain the binary stopping criterion from # the white matter mask defined above. The binary stopping criterion uses a # nearest-neighborhood interpolation at the tracking position. # # **Parameters** # # - mask: numpy array [:, :, :] # # **Stopping States** # # - 'ENDPOINT': stops at a position where mask = 0; the streamline # reached the target stopping area. # - 'OUTSIDEIMAGE': stops at a position outside of metric_map; the streamline # reached an area outside the image where no direction data is available. # - 'TRACKPOINT': stops at a position because no direction is available; the # streamline is stopping where mask > 0, but there is no valid direction to # follow. # - 'INVALIDPOINT': N/A. binary_criterion = BinaryStoppingCriterion(white_matter == 1) fig = plt.figure() plt.xticks([]) plt.yticks([]) fig.tight_layout() plt.imshow(white_matter[:, :, data.shape[2] // 2].T, cmap='gray', origin='lower', interpolation='nearest') fig.savefig('white_matter_mask.png') ############################################################################### # .. figure:: white_matter_mask.png # :align: center # # **White matter binary mask.** streamline_generator = LocalTracking(dg, binary_criterion, seeds, affine, step_size=.5, return_all=True) streamlines = Streamlines(streamline_generator) sft = StatefulTractogram(streamlines, hardi_img, Space.RASMM) save_trk(sft, "tractogram_deterministic_binary_all.trk") if has_fury: scene = window.Scene() scene.add(actor.line(streamlines, colormap.line_colors(streamlines))) window.record(scene, out_path='tractogram_deterministic_binary_all.png', size=(800, 800)) if interactive: window.show(scene) ############################################################################### # .. figure:: tractogram_deterministic_binary_all.png # :align: center # # **Corpus Callosum using deterministic tractography with a binary white # matter mask.** ############################################################################### # ACT Stopping Criterion # ====================== # Anatomically-constrained tractography (ACT) [Smith2012]_ uses information from # anatomical images to determine when the tractography stops. The ``include_map`` # defines when the streamline reached a 'valid' stopping region (e.g. gray # matter partial volume estimation (PVE) map) and the ``exclude_map`` defines # when the streamline reached an 'invalid' stopping region (e.g. corticospinal # fluid PVE map). The background of the anatomical image should be added to the # ``include_map`` to keep streamlines exiting the brain (e.g. through the # brain stem). The ACT stopping criterion uses a trilinear interpolation # at the tracking position. # # **Parameters** # # - ``include_map``: numpy array ``[:, :, :]``, # - ``exclude_map``: numpy array ``[:, :, :]``, # # **Stopping States** # # - 'ENDPOINT': stops at a position where ``include_map`` > 0.5; the streamline # reached the target stopping area. # - 'OUTSIDEIMAGE': stops at a position outside of ``include_map`` or # ``exclude_map``; the streamline reached an area outside the image where no # direction data is available. # - 'TRACKPOINT': stops at a position because no direction is available; the # streamline is stopping where ``include_map`` < 0.5 and ``exclude_map`` < 0.5, # but there is no valid direction to follow. # - 'INVALIDPOINT': ``exclude_map`` > 0.5; the streamline reach a position which # is anatomically not plausible. f_pve_csf, f_pve_gm, f_pve_wm = get_fnames('stanford_pve_maps') pve_csf_data = load_nifti_data(f_pve_csf) pve_gm_data = load_nifti_data(f_pve_gm) pve_wm_data = load_nifti_data(f_pve_wm) background = np.ones(pve_gm_data.shape) background[(pve_gm_data + pve_wm_data + pve_csf_data) > 0] = 0 include_map = pve_gm_data include_map[background > 0] = 1 exclude_map = pve_csf_data act_criterion = ActStoppingCriterion(include_map, exclude_map) fig = plt.figure() plt.subplot(121) plt.xticks([]) plt.yticks([]) plt.imshow(include_map[:, :, data.shape[2] // 2].T, cmap='gray', origin='lower', interpolation='nearest') plt.subplot(122) plt.xticks([]) plt.yticks([]) plt.imshow(exclude_map[:, :, data.shape[2] // 2].T, cmap='gray', origin='lower', interpolation='nearest') fig.tight_layout() fig.savefig('act_maps.png') ############################################################################### # .. figure:: act_maps.png # :align: center # # **Include (left) and exclude (right) maps for ACT.** streamline_generator = LocalTracking(dg, act_criterion, seeds, affine, step_size=.5, return_all=True) streamlines = Streamlines(streamline_generator) sft = StatefulTractogram(streamlines, hardi_img, Space.RASMM) save_trk(sft, "tractogram_deterministic_act_all.trk") if has_fury: scene = window.Scene() scene.add(actor.line(streamlines, colormap.line_colors(streamlines))) window.record(scene, out_path='tractogram_deterministic_act_all.png', size=(800, 800)) if interactive: window.show(scene) ############################################################################### # .. figure:: tractogram_deterministic_act_all.png # :align: center # # **Corpus Callosum using deterministic tractography with ACT stopping # criterion.** streamline_generator = LocalTracking(dg, act_criterion, seeds, affine, step_size=.5, return_all=False) streamlines = Streamlines(streamline_generator) sft = StatefulTractogram(streamlines, hardi_img, Space.RASMM) save_trk(sft, "tractogram_deterministic_act_valid.trk") if has_fury: scene = window.Scene() scene.add(actor.line(streamlines, colormap.line_colors(streamlines))) window.record(scene, out_path='tractogram_deterministic_act_valid.png', size=(800, 800)) if interactive: window.show(scene) ############################################################################### # .. figure:: tractogram_deterministic_act_valid.png # :align: center # # **Corpus Callosum using deterministic tractography with ACT stopping # criterion. Streamlines ending in gray matter region only.** ############################################################################### # The threshold and binary stopping criterion use respectively a scalar map and a # binary mask to stop the tracking. The ACT stopping criterion use partial volume # fraction (PVE) maps from an anatomical image to stop the tracking. # Additionally, the ACT stopping criterion determines if the tracking stopped in # expected regions (e.g. gray matter) and allows the user to get only # streamlines stopping in those regions. # # Notes # ----- # Currently,the proposed method that cuts streamlines going through # subcortical gray matter regions is not implemented. The # backtracking technique for streamlines reaching INVALIDPOINT is not # implemented either [Smith2012]_. # # # References # ---------- # # .. [Smith2012] Smith, R. E., Tournier, J.-D., Calamante, F., & Connelly, A. # Anatomically-constrained tractography: Improved diffusion MRI # streamlines tractography through effective use of anatomical # information. NeuroImage, 63(3), 1924-1938, 2012. # # .. [Girard2014] Girard, G., Whittingstall, K., Deriche, R., & Descoteaux, M. # Towards quantitative connectivity analysis: reducing tractography biases. # NeuroImage, 98, 266-278, 2014. # # .. include:: ../links_names.inc