To get started we'll need to have a set of streamlines to work with. We'll use EuDX along with the CsaOdfModel to make some streamlines. Let's import the modules and download the data we'll be using. """ from dipy.tracking.eudx import EuDX from dipy.reconst import peaks, shm from dipy.tracking import utils from dipy.data import read_stanford_labels, fetch_stanford_t1, read_stanford_t1 hardi_img, gtab, labels_img = read_stanford_labels() data = hardi_img.get_data() labels = labels_img.get_data() fetch_stanford_t1() t1 = read_stanford_t1() t1_data = t1.get_data() """ We've loaded an image called ``labels_img`` which is a map of tissue types such that every integer value in the array ``labels`` represents an anatomical structure or tissue type [#]_. For this example, the image was created so that white matter voxels have values of either 1 or 2. We'll use ``peaks_from_model`` to apply the ``CsaOdfModel`` to each white matter voxel and estimate fiber orientations which we can use for tracking. """ white_matter = (labels == 1) | (labels == 2) csamodel = shm.CsaOdfModel(gtab, 6) csapeaks = peaks.peaks_from_model(model=csamodel, data=data,
(150 orientations, b=2000s/mm^2) which is one of the standard example datasets in DIPY. """ import numpy as np from dipy.data import (read_stanford_labels, fetch_stanford_t1, read_stanford_t1) # Fix seed np.random.seed(1) # Read data hardi_img, gtab, labels_img = read_stanford_labels() data = hardi_img.get_data() labels = labels_img.get_data() affine = hardi_img.get_affine() fetch_stanford_t1() t1 = read_stanford_t1() t1_data = t1.get_data() # Select a relevant part of the data (left hemisphere) # Coordinates given in x bounds, y bounds, z bounds dshape = data.shape[:-1] xa, xb, ya, yb, za, zb = [15, 42, 10, 65, 18, 65] data_small = data[xa:xb, ya:yb, za:zb] selectionmask = np.zeros(dshape, 'bool') selectionmask[xa:xb, ya:yb, za:zb] = True """ The data is first fitted to Constant Solid Angle (CDA) ODF Model. CSA is a good choice to estimate general fractional anisotropy (GFA), which the tissue classifier can use to restrict fiber tracking to those areas where the ODF