Reproducing the FA map

[FouierL]

Hello, thank you for this amazing research and open source codes.

Sorry to take up your time, I have a question not related to this open source code but related to your DDM2 work.

When I was reproducing the FA map in the article, I realized that using only the noisy data didn't come out with the matching figure in the paper.

my code is: import numpy as np import dipy.reconst.dki as dki import dipy.reconst.dti as dti from dipy.core.gradients import gradient_table from dipy.data import get_fnames from dipy.io.gradients import read_bvals_bvecs from dipy.io.image import load_nifti from dipy.segment.mask import median_otsu from dipy.viz.plotting import compare_maps from scipy.ndimage import gaussian_filter import matplotlib.pyplot as plt from warnings import warn import cv2 import numpy as np

hardi_fname, hardi_bval_fname, hardi_bvec_fname = get_fnames( 'stanford_hardi') b0_size = 10 data, affine = load_nifti(hardi_fname) bvals, bvecs = read_bvals_bvecs(hardi_bval_fname, hardi_bvec_fname) gtab = gradient_table(bvals, bvecs)

maskdata, mask = median_otsu(data, vol_idx=[0, 1], median_radius=4, numpass=2, autocrop=False, dilate=1)

slice=40 data = data[:, :, slice:slice+1] mask = mask[:, :, slice:slice+1]

tenmodel = dti.TensorModel(gtab) tenfit = tenmodel.fit(data, mask=mask) fits = [tenfit]

maps = ['fa', 'md', 'ad', 'rd'] fit_labels = ['DTI']

for i in range(len(maps)): attr = getattr(fits[0], maps[i]) attr=(attr - np.min(attr)) / (np.max(attr) - np.min(attr))

plt.imshow(attr,cmap='jet')
plt.axis('off')
plt.savefig('{}.png'.format('origin'+maps[i]))
plt.close()

the map in paper:

the map my code give:

Thank you for taking the time. Good luck with your research.