""" ================================================= Using Various Tissue Classifiers for Tractography ================================================= The tissue classifier 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 tissue classifier determines if the stopping is 'valid' or 'invalid'. A streamline is 'valid' when the tissue classifier 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. This example is an extension of the :ref:`example_deterministic_fiber_tracking` 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 numpy as np from dipy.data import (read_stanford_labels, default_sphere, read_stanford_pve_maps) from dipy.direction import DeterministicMaximumDirectionGetter from dipy.io.streamline import save_trk from dipy.reconst.csdeconv import (ConstrainedSphericalDeconvModel, auto_response) from dipy.tracking.local import LocalTracking from dipy.tracking.streamline import Streamlines from dipy.tracking import utils from dipy.viz import window, actor, colormap as cmap, have_fury # Enables/disables interactive visualization interactive = False ren = window.Renderer() hardi_img, gtab, labels_img = read_stanford_labels() _, _, img_pve_wm = read_stanford_pve_maps() data = hardi_img.get_data() labels = labels_img.get_data() affine = hardi_img.affine white_matter = img_pve_wm.get_data() seed_mask = np.logical_and(labels == 2, white_matter == 1) seeds = utils.seeds_from_mask(seed_mask, density=2, affine=affine) response, ratio = auto_response(gtab, data, roi_radius=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 Tissue Classifier --------------------------- A scalar map can be used to define where the tracking stops. The threshold tissue classifier 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 tissue classifier uses a trilinear interpolation at the tracking position. **Parameters** - metric_map: numpy array [:, :, :] - threshold: float **Stopping criterion** - 'ENDPOINT': metric_map < threshold, - 'OUTSIDEIMAGE': tracking point outside of metric_map, - 'TRACKPOINT': stop because no direction is available, - 'INVALIDPOINT': N/A. """ import matplotlib.pyplot as plt import dipy.reconst.dti as dti from dipy.reconst.dti import fractional_anisotropy from dipy.tracking.local import ThresholdTissueClassifier tensor_model = dti.TensorModel(gtab) tenfit = tensor_model.fit(data, mask=labels > 0) FA = fractional_anisotropy(tenfit.evals) threshold_classifier = ThresholdTissueClassifier(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.** """ all_streamlines_threshold_classifier = LocalTracking(dg, threshold_classifier, seeds, affine, step_size=.5, return_all=True) save_trk("deterministic_threshold_classifier_all.trk", all_streamlines_threshold_classifier, affine, labels.shape) streamlines = Streamlines(all_streamlines_threshold_classifier) if have_fury: window.clear(ren) ren.add(actor.line(streamlines, cmap.line_colors(streamlines))) window.record(ren, out_path='all_streamlines_threshold_classifier.png', size=(600, 600)) if interactive: window.show(ren) """ .. figure:: all_streamlines_threshold_classifier.png :align: center **Deterministic tractography using a thresholded fractional anisotropy.** """ """ Binary Tissue Classifier ------------------------ A binary mask can be used to define where the tracking stops. The binary tissue classifier stops the tracking whenever the tracking position is outside the mask. Here, we show how to obtain the binary tissue classifier from the white matter mask defined above. The binary tissue classifier uses a nearest-neighborhood interpolation at the tracking position. **Parameters** - mask: numpy array [:, :, :] **Stopping criterion** - 'ENDPOINT': mask = 0 - 'OUTSIDEIMAGE': tracking point outside of mask - 'TRACKPOINT': no direction is available - 'INVALIDPOINT': N/A """ from dipy.tracking.local import BinaryTissueClassifier binary_classifier = BinaryTissueClassifier(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.** """ all_streamlines_binary_classifier = LocalTracking(dg, binary_classifier, seeds, affine, step_size=.5, return_all=True) save_trk("deterministic_binary_classifier_all.trk", all_streamlines_binary_classifier, affine, labels.shape) streamlines = Streamlines(all_streamlines_binary_classifier) if have_fury: window.clear(ren) ren.add(actor.line(streamlines, cmap.line_colors(streamlines))) window.record(ren, out_path='all_streamlines_binary_classifier.png', size=(600, 600)) if interactive: window.show(ren) """ .. figure:: all_streamlines_binary_classifier.png :align: center **Deterministic tractography using a binary white matter mask.** """ """ ACT Tissue Classifier --------------------- 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 tissue classifier uses a trilinear interpolation at the tracking position. **Parameters** - ``include_map``: numpy array ``[:, :, :]``, - ``exclude_map``: numpy array ``[:, :, :]``, **Stopping criterion** - 'ENDPOINT': ``include_map`` > 0.5, - 'OUTSIDEIMAGE': tracking point outside of ``include_map`` or ``exclude_map``, - 'TRACKPOINT': no direction is available, - 'INVALIDPOINT': ``exclude_map`` > 0.5. """ from dipy.tracking.local import ActTissueClassifier img_pve_csf, img_pve_gm, img_pve_wm = read_stanford_pve_maps() 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) 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.** """ all_streamlines_act_classifier = LocalTracking(dg, act_classifier, seeds, affine, step_size=.5, return_all=True) save_trk("deterministic_act_classifier_all.trk", all_streamlines_act_classifier, affine, labels.shape) streamlines = Streamlines(all_streamlines_act_classifier) if have_fury: window.clear(ren) ren.add(actor.line(streamlines, cmap.line_colors(streamlines))) window.record(ren, out_path='all_streamlines_act_classifier.png', size=(600, 600)) if interactive: window.show(ren) """ .. figure:: all_streamlines_act_classifier.png :align: center **Deterministic tractography using ACT stopping criterion.** """ valid_streamlines_act_classifier = LocalTracking(dg, act_classifier, seeds, affine, step_size=.5, return_all=False) save_trk("deterministic_act_classifier_valid.trk", valid_streamlines_act_classifier, affine, labels.shape) streamlines = Streamlines(valid_streamlines_act_classifier) if have_fury: window.clear(ren) ren.add(actor.line(streamlines, cmap.line_colors(streamlines))) window.record(ren, out_path='valid_streamlines_act_classifier.png', size=(600, 600)) if interactive: window.show(ren) """ .. figure:: valid_streamlines_act_classifier.png :align: center **Deterministic tractography using a anatomically-constrained tractography stopping criterion. Streamlines ending in gray matter region only.** """ """ The threshold and binary tissue classifiers use respectively a scalar map and a binary mask to stop the tracking. The ACT tissue classifier use partial volume fraction (PVE) maps from an anatomical image to stop the tracking. Additionally, the ACT tissue classifier 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 in ACT 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. 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. """