in FAST-FSL and ANTS-atropos, respectively. Here we will use a T1-weighted image, that has been previously skull-stripped and bias field corrected. """ import numpy as np import matplotlib.pyplot as plt from dipy.data import fetch_tissue_data, read_tissue_data from dipy.segment.tissue import TissueClassifierHMRF """ First we fetch the T1 volume from the Syn dataset and determine its shape. """ fetch_tissue_data() t1_img = read_tissue_data() t1 = t1_img.get_data() print('t1.shape (%d, %d, %d)' % t1.shape) """ We have fetched the T1 volume. Now we will look at the axial and the coronal slices of the image. """ fig = plt.figure() a = fig.add_subplot(1, 2, 1) img_ax = np.rot90(t1[..., 89]) imgplot = plt.imshow(img_ax, cmap="gray") a.axis('off') a.set_title('Axial') a = fig.add_subplot(1, 2, 2) img_cor = np.rot90(t1[:, 128, :])
The algorithm to suppress Gibbs oscillations can be imported from the denoise module of dipy: """ from dipy.denoise.gibbs import gibbs_removal """ We first apply this algorithm to T1-weighted dataset which can be fetched using the following code: """ from dipy.data import fetch_tissue_data, read_tissue_data fetch_tissue_data() t1_img = read_tissue_data(contrast='T1 denoised') t1 = t1_img.get_data() """ Let's plot a slice of this dataset. """ import matplotlib.pyplot as plt import numpy as np axial_slice = 88 t1_slice = t1[..., axial_slice] fig = plt.figure(figsize=(15, 4)) fig.subplots_adjust(wspace=0.2)
Here we will use a T1-weighted image, that has been previously skull-stripped and bias field corrected. """ import numpy as np import matplotlib.pyplot as plt from dipy.data import fetch_tissue_data, read_tissue_data from dipy.segment.tissue import TissueClassifierHMRF """ First we fetch the T1 volume from the Syn dataset and determine its shape. """ fetch_tissue_data() t1_img = read_tissue_data() t1 = t1_img.get_data() print('t1.shape (%d, %d, %d)' % t1.shape) """ We have fetched the T1 volume. Now we will look at the axial and the coronal slices of the image. """ fig = plt.figure() a = fig.add_subplot(1, 2, 1) img_ax = np.rot90(t1[..., 89]) imgplot = plt.imshow(img_ax, cmap="gray") a.axis('off') a.set_title('Axial') a = fig.add_subplot(1, 2, 2)