""" ==================================================== Extracting AFQ tract profiles from segmented bundles ==================================================== In this example, we will extract the values of a statistic from a volume, using the coordinates along the length of a bundle. These are called `tract profiles` One of the challenges of extracting tract profiles is that some of the streamlines in a bundle may diverge significantly from the bundle in some locations. To overcome this challenge, we will use a strategy similar to that described in [Yeatman2012]_: We will weight the contribution of each streamline to the bundle profile based on how far this streamline is from the mean trajectory of the bundle at that location. """ import dipy.stats.analysis as dsa import dipy.tracking.streamline as dts from dipy.segment.clustering import QuickBundles from dipy.segment.metricspeed import AveragePointwiseEuclideanMetric from dipy.segment.featurespeed import ResampleFeature from dipy.data.fetcher import get_two_hcp842_bundles import dipy.data as dpd from dipy.io.streamline import load_trk from dipy.io.image import load_nifti import matplotlib.pyplot as plt import numpy as np import os.path as op ############################################################################### # # To get started, we will grab the bundles. bundles_path = dpd.fetch_bundles_2_subjects() bundles_folder = bundles_path[1] cst_l_file = op.join(bundles_folder, "bundles_2_subjects", "subj_2", "bundles", "bundles_cst.left.trk") af_l_file = op.join(bundles_folder, "bundles_2_subjects", "subj_2", "bundles", "bundles_af.left.trk") ############################################################################### # # Either way, we can use the `dipy.io` API to read in the bundles from file. # `load_trk` returns both the streamlines, as well as header information. # cst_l = load_trk(cst_l_file, "same", bbox_valid_check=False).streamlines af_l = load_trk(af_l_file, "same", bbox_valid_check=False).streamlines ############################################################################### # # In the next step, we need to make sure that all the streamlines in each bundle # are oriented the same way. For example, for the CST, we want to make sure that # all the bundles have their cortical termination at one end of the streamline. # This is that when we later extract values from a volume, we won't have # different streamlines going in opposite directions. # # To orient all the streamlines in each bundles, we will create standard # streamlines, by finding the centroids of the left AF and CST bundle models. # # The advantage of using the model bundles is that we can use the same standard # for different subjects, which means that we'll get roughly the same orientation model_af_l_file, model_cst_l_file = get_two_hcp842_bundles() model_af_l = load_trk(model_af_l_file, "same", bbox_valid_check=False).streamlines model_cst_l = load_trk(model_cst_l_file, "same", bbox_valid_check=False).streamlines feature = ResampleFeature(nb_points=100) metric = AveragePointwiseEuclideanMetric(feature) ############################################################################### # Since we are going to include all of the streamlines in the single cluster # from the streamlines, we set the threshold to `np.inf`. We pull out the # centroid as the standard. qb = QuickBundles(np.inf, metric=metric) cluster_cst_l = qb.cluster(model_cst_l) standard_cst_l = cluster_cst_l.centroids[0] cluster_af_l = qb.cluster(model_af_l) standard_af_l = cluster_af_l.centroids[0] ############################################################################### # We use the centroid streamline for each atlas bundle as the standard to orient # all of the streamlines in each bundle from the individual subject. Here, the # affine used is the one from the transform between the atlas and individual # tractogram. This is so that the orienting is done relative to the space of the # individual, and not relative to the atlas space. oriented_cst_l = dts.orient_by_streamline(cst_l, standard_cst_l) oriented_af_l = dts.orient_by_streamline(af_l, standard_af_l) ############################################################################### # Read volumetric data from an image corresponding to this subject. # # For the purpose of this, we've extracted only the FA within the bundles in # question, but in real use, this is where you would add the FA map of your # subject. files, folder = dpd.fetch_bundle_fa_hcp() fa, fa_affine = load_nifti(op.join(folder, "hcp_bundle_fa.nii.gz")) ############################################################################### # Calculate weights for each bundle: w_cst_l = dsa.gaussian_weights(oriented_cst_l) w_af_l = dsa.gaussian_weights(oriented_af_l) ############################################################################### # And then use the weights to calculate the tract profiles for each bundle profile_cst_l = dsa.afq_profile(fa, oriented_cst_l, fa_affine, weights=w_cst_l) profile_af_l = dsa.afq_profile(fa, oriented_af_l, fa_affine, weights=w_af_l) fig, (ax1, ax2) = plt.subplots(1, 2) ax1.plot(profile_cst_l) ax1.set_ylabel("Fractional anisotropy") ax1.set_xlabel("Node along CST") ax2.plot(profile_af_l) ax2.set_xlabel("Node along AF") fig.savefig("tract_profiles") ############################################################################### # .. figure:: tract_profiles.png # :align: center # # Bundle profiles for the fractional anisotropy in the left CST (left) and # left AF (right). ############################################################################### # References # ---------- # # .. [Yeatman2012] Yeatman, Jason D., Robert F. Dougherty, Nathaniel J. Myall, # Brian A. Wandell, and Heidi M. Feldman. 2012. "Tract Profiles of White # Matter Properties: Automating Fiber-Tract Quantification" PloS One 7 (11): # e49790. # # .. [Garyfallidis17] Garyfallidis et al. Recognition of white matter bundles # using local and global streamline-based registration and clustering, # Neuroimage, 2017. # # .. [Garyfallidis12] Garyfallidis E. et al., QuickBundles a method for # tractography simplification, Frontiers in Neuroscience, vol 6, no 175, 2012.