Journal of the American Heart Association (JAHA).Circ: Cardiovascular Quality & Outcomes.Arteriosclerosis, Thrombosis, and Vascular Biology (ATVB).The streamlines from world coordinates to the subject’s native space show ()Īs an additional challenge, set the color of the streamlines to display the values of theįA map and change the opacity to 0.05. line_colors ( streamlines )) # Generate the figureįig = generate_anatomical_volume_figure ( streamlines_actor ) plt. 5 ) streamlines = Streamlines ( streamlines_generator ) # Plot the tractogram Streamlines_generator = LocalTracking ( peak_indices, stopping_criterion, seeds, affine = affine, step_size =. Stopping_criterion = BinaryStoppingCriterion ( seed_mask = 1 ) # Perform tracking seeds_from_mask ( seed_mask, affine = affine, density = 1 ) # Set stopping criteria copy () seed_mask = 1 seed_mask = 0 seeds = utils. 2, min_separation_angle = 25, mask = dwi_mask, npeaks = 2 ) # Create a binary seed mask evecs sphere = get_sphere ( 'symmetric362' ) peak_indices = peaks_from_model ( model = dti_model, data = dwi_data, sphere = sphere, relative_peak_threshold =. fit ( dwi_data, mask = dwi_mask ) fa_img = dti_fit. TensorModel ( gtab ) dti_fit = dti_model. get_fdata () dwi_data, dwi_mask = median_otsu ( dwi_data, vol_idx =, numpass = 1 ) # Specify the volume index to the b0 volumesĭti_model = dti. affine bvals, bvecs = read_bvals_bvecs ( bval_fname, bvec_fname ) gtab = gradient_table ( bvals, bvecs ) dwi_data = dwi_img. get ( subject = subj, suffix = 'dwi', extension = '.bval', return_type = 'file' ) dwi_img = nib.
get ( subject = subj, extension = '.eddy_rotated_bvecs', return_type = 'file' ) bval_fname = gradient_layout. get ( subject = subj, suffix = 'dwi', extension = '.nii.gz', return_type = 'file' ) bvec_fname = dwi_layout. Import os import nibabel as nib import numpy as np from bids.layout import BIDSLayout from dipy.io.gradients import read_bvals_bvecs from import gradient_table from dipy.data import get_sphere from dipy.direction import peaks_from_model import as dti from import median_otsu from acking import utils from _tracking import LocalTracking from import Streamlines from utils.visualization_utils import generate_anatomical_volume_figure from fury import actor, colormap import matplotlib.pyplot as plt dwi_layout = BIDSLayout ( "././data/ds000221/derivatives/uncorrected_topup_eddy", validate = False ) gradient_layout = BIDSLayout ( "././data/ds000221/", > validate = False ) # Get subject data We will use an evenly distributed sphere of 362 points Therefore, it is necessary to discretize the eigenvectors before One of the inputs of EuDX is the discretized voxel directions on a unit join ( out_dir, "fa.png" ), dpi = 300, bbox_inches = "tight" ) plt. rotate ( fa_img // 2 ], 90, reshape = False )) fig. rotate ( fa_img // 2, :], 90, reshape = False )) ax. Import matplotlib.pyplot as plt from scipy import ndimage # To rotate image for visualization purposes join ( out_dir, 'fa.nii.gz' )) # Plot the FA
evecs fa_img = 0 # Save the FAįa_nii = nib. Out_dir = f "././data/ds000221/derivatives/dwi/tractography/sub- /ses-01/dwi/" if not os. # Create the directory to save the results Note that many steps in the streamline propagation procedure areĬomputationally intensive, and thus may take a while to complete. We will now create a mask and constrain the fitting within the mask. affine bvals, bvecs = read_bvals_bvecs ( bval_fname, bvec_fname ) gtab = gradient_table ( bvals, bvecs ) Import os import nibabel as nib import numpy as np from bids.layout import BIDSLayout from dipy.io.gradients import read_bvals_bvecs from import gradient_table dwi_layout = BIDSLayout ( "././data/ds000221/derivatives/uncorrected_topup_eddy", validate = False ) gradient_layout = BIDSLayout ( "././data/ds000221/", validate = False ) subj = '010006' dwi_fname = dwi_layout.
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