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Crossing invariant fiber response function with FORECAST model
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Crossing invariant fiber response function with FORECAST model
We show how to obtain a voxel specific response function in the form of axially symmetric tensor and the fODF using the FORECAST model from [Anderson2005] , [Kaden2016] and [Zucchelli2017].
First import the necessary modules:
import numpy as np
import matplotlib.pyplot as plt
from dipy.reconst.forecast import ForecastModel
from dipy.viz import actor, window
from dipy.data import fetch_hbn, get_sphere
import nibabel as nib
import os.path as op
from dipy.core.gradients import gradient_table
Download and read the data for this tutorial. Our implementation of FORECAST requires multi-shell data.fetch_hbn() provides data that was acquired using b-values of 1000 and 2000 as part of the Healthy Brain Network study [Alexander2017] and was preprocessed and quality controlled in the HBN-POD2 dataset [RichieHalford2022].
data_path = fetch_hbn(["NDARAA948VFH"])[1]
dwi_path = op.join(
data_path, "derivatives", "qsiprep", "sub-NDARAA948VFH",
"ses-HBNsiteRU", "dwi")
img = nib.load(op.join(
dwi_path,
"sub-NDARAA948VFH_ses-HBNsiteRU_acq-64dir_space-T1w_desc-preproc_dwi.nii.gz"))
gtab = gradient_table(
op.join(dwi_path,
"sub-NDARAA948VFH_ses-HBNsiteRU_acq-64dir_space-T1w_desc-preproc_dwi.bval"),
op.join(dwi_path,
"sub-NDARAA948VFH_ses-HBNsiteRU_acq-64dir_space-T1w_desc-preproc_dwi.bvec"))
data = np.asarray(img.dataobj)
mask_img = nib.load(
op.join(dwi_path,
"sub-NDARAA948VFH_ses-HBNsiteRU_acq-64dir_space-T1w_desc-brain_mask.nii.gz"))
brain_mask = mask_img.get_fdata()
Let us consider only a single slice for the FORECAST fitting
data_small = data[:, :, 50:51]
mask_small = brain_mask[:, :, 50:51]
Instantiate the FORECAST Model.
“sh_order” is the spherical harmonics order used for the fODF.
dec_alg is the spherical deconvolution algorithm used for the FORECAST basis fitting, in this case we used the Constrained Spherical Deconvolution (CSD) algorithm.
fm = ForecastModel(gtab, sh_order=6, dec_alg='CSD')
Fit the FORECAST to the data
f_fit = fm.fit(data_small, mask_small)
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28%|##7 | 1425/5148.0 [02:27<05:36, 11.05it/s]
28%|##7 | 1427/5148.0 [02:27<06:31, 9.50it/s]
28%|##7 | 1429/5148.0 [02:27<07:17, 8.51it/s]
28%|##7 | 1430/5148.0 [02:27<07:33, 8.19it/s]
28%|##7 | 1432/5148.0 [02:27<06:38, 9.31it/s]
28%|##7 | 1433/5148.0 [02:28<07:01, 8.82it/s]
28%|##7 | 1434/5148.0 [02:28<07:43, 8.02it/s]
28%|##7 | 1441/5148.0 [02:28<03:35, 17.16it/s]
28%|##8 | 1444/5148.0 [02:28<03:36, 17.11it/s]
28%|##8 | 1446/5148.0 [02:28<03:59, 15.46it/s]
28%|##8 | 1448/5148.0 [02:28<04:15, 14.48it/s]
28%|##8 | 1450/5148.0 [02:29<05:27, 11.28it/s]
28%|##8 | 1452/5148.0 [02:29<06:33, 9.38it/s]
28%|##8 | 1454/5148.0 [02:29<07:22, 8.35it/s]
28%|##8 | 1455/5148.0 [02:30<07:53, 7.81it/s]
28%|##8 | 1456/5148.0 [02:30<08:19, 7.40it/s]
29%|##8 | 1469/5148.0 [02:30<02:44, 22.40it/s]
29%|##8 | 1472/5148.0 [02:30<04:12, 14.56it/s]
29%|##8 | 1477/5148.0 [02:31<03:26, 17.74it/s]
29%|##8 | 1480/5148.0 [02:31<04:21, 14.00it/s]
29%|##8 | 1482/5148.0 [02:31<04:19, 14.14it/s]
29%|##8 | 1484/5148.0 [02:31<04:27, 13.72it/s]
29%|##8 | 1486/5148.0 [02:31<04:17, 14.23it/s]
29%|##8 | 1489/5148.0 [02:32<03:58, 15.31it/s]
29%|##8 | 1491/5148.0 [02:32<05:06, 11.94it/s]
29%|##9 | 1494/5148.0 [02:32<04:36, 13.22it/s]
29%|##9 | 1496/5148.0 [02:32<05:59, 10.17it/s]
29%|##9 | 1499/5148.0 [02:33<05:12, 11.67it/s]
29%|##9 | 1501/5148.0 [02:33<06:26, 9.43it/s]
29%|##9 | 1503/5148.0 [02:33<07:14, 8.38it/s]
29%|##9 | 1504/5148.0 [02:33<07:30, 8.08it/s]
29%|##9 | 1505/5148.0 [02:33<07:47, 7.79it/s]
29%|##9 | 1506/5148.0 [02:34<08:06, 7.48it/s]
29%|##9 | 1508/5148.0 [02:34<06:53, 8.81it/s]
29%|##9 | 1510/5148.0 [02:34<06:01, 10.07it/s]
29%|##9 | 1512/5148.0 [02:34<05:34, 10.87it/s]
29%|##9 | 1514/5148.0 [02:34<05:15, 11.53it/s]
29%|##9 | 1516/5148.0 [02:34<06:14, 9.70it/s]
29%|##9 | 1518/5148.0 [02:35<06:56, 8.71it/s]
30%|##9 | 1519/5148.0 [02:35<07:14, 8.36it/s]
30%|##9 | 1520/5148.0 [02:35<07:37, 7.92it/s]
30%|##9 | 1521/5148.0 [02:35<08:05, 7.47it/s]
30%|##9 | 1522/5148.0 [02:35<08:35, 7.03it/s]
30%|##9 | 1533/5148.0 [02:36<02:41, 22.36it/s]
30%|##9 | 1536/5148.0 [02:36<03:21, 17.97it/s]
30%|##9 | 1538/5148.0 [02:36<04:21, 13.83it/s]
30%|##9 | 1540/5148.0 [02:36<05:23, 11.16it/s]
30%|##9 | 1542/5148.0 [02:37<05:17, 11.36it/s]
30%|##9 | 1544/5148.0 [02:37<05:13, 11.48it/s]
30%|### | 1552/5148.0 [02:37<03:03, 19.57it/s]
30%|### | 1555/5148.0 [02:38<04:46, 12.53it/s]
30%|### | 1557/5148.0 [02:38<05:39, 10.59it/s]
30%|### | 1559/5148.0 [02:38<05:26, 11.00it/s]
30%|### | 1561/5148.0 [02:38<05:20, 11.18it/s]
30%|### | 1563/5148.0 [02:38<05:31, 10.80it/s]
30%|### | 1566/5148.0 [02:39<04:26, 13.44it/s]
30%|### | 1568/5148.0 [02:39<04:23, 13.57it/s]
31%|### | 1571/5148.0 [02:39<03:59, 14.93it/s]
31%|### | 1574/5148.0 [02:39<03:33, 16.72it/s]
31%|### | 1576/5148.0 [02:39<03:52, 15.34it/s]
31%|### | 1579/5148.0 [02:39<03:36, 16.47it/s]
31%|### | 1583/5148.0 [02:39<03:15, 18.20it/s]
31%|### | 1585/5148.0 [02:40<04:37, 12.82it/s]
31%|### | 1587/5148.0 [02:40<04:47, 12.41it/s]
31%|### | 1589/5148.0 [02:40<04:54, 12.08it/s]
31%|### | 1591/5148.0 [02:40<05:57, 9.94it/s]
31%|### | 1593/5148.0 [02:41<06:34, 9.00it/s]
31%|### | 1595/5148.0 [02:41<06:12, 9.53it/s]
31%|###1 | 1597/5148.0 [02:41<07:03, 8.39it/s]
31%|###1 | 1603/5148.0 [02:41<04:06, 14.40it/s]
31%|###1 | 1605/5148.0 [02:42<05:10, 11.41it/s]
31%|###1 | 1608/5148.0 [02:42<04:35, 12.83it/s]
31%|###1 | 1610/5148.0 [02:42<05:45, 10.25it/s]
31%|###1 | 1615/5148.0 [02:42<04:08, 14.22it/s]
31%|###1 | 1620/5148.0 [02:43<03:22, 17.40it/s]
32%|###1 | 1622/5148.0 [02:43<03:35, 16.37it/s]
32%|###1 | 1625/5148.0 [02:43<03:24, 17.22it/s]
32%|###1 | 1627/5148.0 [02:43<04:32, 12.91it/s]
32%|###1 | 1640/5148.0 [02:43<02:14, 26.10it/s]
32%|###1 | 1643/5148.0 [02:44<03:34, 16.37it/s]
32%|###1 | 1646/5148.0 [02:44<03:31, 16.58it/s]
32%|###2 | 1650/5148.0 [02:44<03:11, 18.24it/s]
32%|###2 | 1653/5148.0 [02:45<03:44, 15.59it/s]
32%|###2 | 1656/5148.0 [02:45<03:40, 15.80it/s]
32%|###2 | 1658/5148.0 [02:45<04:44, 12.26it/s]
32%|###2 | 1661/5148.0 [02:45<04:21, 13.33it/s]
32%|###2 | 1663/5148.0 [02:45<04:16, 13.57it/s]
32%|###2 | 1665/5148.0 [02:45<04:15, 13.63it/s]
32%|###2 | 1668/5148.0 [02:46<04:14, 13.67it/s]
32%|###2 | 1670/5148.0 [02:46<04:09, 13.92it/s]
32%|###2 | 1672/5148.0 [02:46<04:24, 13.12it/s]
33%|###2 | 1674/5148.0 [02:46<04:33, 12.72it/s]
33%|###2 | 1676/5148.0 [02:46<04:33, 12.71it/s]
33%|###2 | 1678/5148.0 [02:47<05:47, 9.98it/s]
33%|###2 | 1680/5148.0 [02:47<06:33, 8.81it/s]
33%|###2 | 1681/5148.0 [02:47<06:55, 8.34it/s]
33%|###2 | 1682/5148.0 [02:47<07:20, 7.87it/s]
33%|###2 | 1684/5148.0 [02:47<06:06, 9.44it/s]
33%|###2 | 1687/5148.0 [02:48<04:45, 12.13it/s]
33%|###2 | 1689/5148.0 [02:48<04:45, 12.12it/s]
33%|###2 | 1691/5148.0 [02:48<05:51, 9.85it/s]
33%|###2 | 1693/5148.0 [02:48<05:27, 10.54it/s]
33%|###2 | 1695/5148.0 [02:48<05:15, 10.94it/s]
33%|###2 | 1697/5148.0 [02:49<06:28, 8.88it/s]
33%|###2 | 1698/5148.0 [02:49<07:01, 8.18it/s]
33%|###3 | 1699/5148.0 [02:49<07:36, 7.56it/s]
33%|###3 | 1706/5148.0 [02:49<03:38, 15.73it/s]
33%|###3 | 1708/5148.0 [02:49<03:57, 14.48it/s]
33%|###3 | 1712/5148.0 [02:50<03:26, 16.66it/s]
33%|###3 | 1715/5148.0 [02:50<03:17, 17.40it/s]
33%|###3 | 1717/5148.0 [02:50<04:33, 12.57it/s]
34%|###3 | 1730/5148.0 [02:50<02:15, 25.18it/s]
34%|###3 | 1733/5148.0 [02:51<03:37, 15.68it/s]
34%|###3 | 1735/5148.0 [02:51<04:25, 12.86it/s]
34%|###3 | 1737/5148.0 [02:51<04:32, 12.51it/s]
34%|###3 | 1745/5148.0 [02:52<03:03, 18.57it/s]
34%|###3 | 1748/5148.0 [02:52<03:02, 18.61it/s]
34%|###4 | 1751/5148.0 [02:52<03:44, 15.11it/s]
34%|###4 | 1753/5148.0 [02:52<03:49, 14.76it/s]
34%|###4 | 1755/5148.0 [02:52<03:47, 14.89it/s]
34%|###4 | 1757/5148.0 [02:52<04:04, 13.87it/s]
34%|###4 | 1761/5148.0 [02:53<03:33, 15.88it/s]
34%|###4 | 1763/5148.0 [02:53<04:44, 11.91it/s]
34%|###4 | 1765/5148.0 [02:53<04:44, 11.88it/s]
34%|###4 | 1767/5148.0 [02:53<05:51, 9.63it/s]
34%|###4 | 1769/5148.0 [02:54<05:26, 10.34it/s]
34%|###4 | 1771/5148.0 [02:54<06:12, 9.07it/s]
34%|###4 | 1773/5148.0 [02:54<05:52, 9.58it/s]
34%|###4 | 1776/5148.0 [02:54<04:48, 11.70it/s]
35%|###4 | 1778/5148.0 [02:55<05:44, 9.78it/s]
35%|###4 | 1783/5148.0 [02:55<03:51, 14.53it/s]
35%|###4 | 1785/5148.0 [02:55<04:55, 11.38it/s]
35%|###4 | 1787/5148.0 [02:55<05:52, 9.52it/s]
35%|###4 | 1795/5148.0 [02:56<03:16, 17.10it/s]
35%|###4 | 1798/5148.0 [02:56<04:39, 12.00it/s]
35%|###4 | 1800/5148.0 [02:56<04:41, 11.90it/s]
35%|###5 | 1802/5148.0 [02:56<04:40, 11.95it/s]
35%|###5 | 1804/5148.0 [02:57<05:42, 9.77it/s]
35%|###5 | 1807/5148.0 [02:57<04:53, 11.37it/s]
35%|###5 | 1820/5148.0 [02:57<02:20, 23.65it/s]
35%|###5 | 1823/5148.0 [02:57<03:00, 18.38it/s]
35%|###5 | 1825/5148.0 [02:58<03:52, 14.31it/s]
36%|###5 | 1828/5148.0 [02:58<03:42, 14.94it/s]
36%|###5 | 1833/5148.0 [02:58<03:08, 17.62it/s]
36%|###5 | 1835/5148.0 [02:58<03:23, 16.24it/s]
36%|###5 | 1844/5148.0 [02:59<02:21, 23.38it/s]
36%|###5 | 1847/5148.0 [02:59<02:56, 18.74it/s]
36%|###5 | 1849/5148.0 [02:59<03:09, 17.39it/s]
36%|###5 | 1851/5148.0 [02:59<04:12, 13.06it/s]
36%|###6 | 1854/5148.0 [02:59<03:51, 14.25it/s]
36%|###6 | 1856/5148.0 [03:00<04:01, 13.63it/s]
36%|###6 | 1858/5148.0 [03:00<05:10, 10.61it/s]
36%|###6 | 1860/5148.0 [03:00<05:39, 9.68it/s]
36%|###6 | 1862/5148.0 [03:01<06:13, 8.79it/s]
36%|###6 | 1864/5148.0 [03:01<05:46, 9.49it/s]
36%|###6 | 1866/5148.0 [03:01<05:16, 10.38it/s]
36%|###6 | 1868/5148.0 [03:01<05:02, 10.83it/s]
36%|###6 | 1870/5148.0 [03:01<05:58, 9.14it/s]
36%|###6 | 1872/5148.0 [03:01<05:35, 9.77it/s]
36%|###6 | 1874/5148.0 [03:02<05:16, 10.34it/s]
36%|###6 | 1876/5148.0 [03:02<06:22, 8.56it/s]
36%|###6 | 1877/5148.0 [03:02<06:53, 7.90it/s]
37%|###6 | 1881/5148.0 [03:02<04:42, 11.55it/s]
37%|###6 | 1885/5148.0 [03:03<03:46, 14.39it/s]
37%|###6 | 1887/5148.0 [03:03<03:57, 13.72it/s]
37%|###6 | 1889/5148.0 [03:03<04:03, 13.38it/s]
37%|###6 | 1891/5148.0 [03:03<05:12, 10.43it/s]
37%|###6 | 1893/5148.0 [03:03<05:14, 10.34it/s]
37%|###6 | 1896/5148.0 [03:04<04:46, 11.37it/s]
37%|###7 | 1909/5148.0 [03:04<02:07, 25.32it/s]
37%|###7 | 1912/5148.0 [03:04<02:45, 19.60it/s]
37%|###7 | 1916/5148.0 [03:04<02:39, 20.21it/s]
37%|###7 | 1919/5148.0 [03:04<02:40, 20.11it/s]
37%|###7 | 1922/5148.0 [03:05<03:23, 15.85it/s]
37%|###7 | 1926/5148.0 [03:05<03:04, 17.47it/s]
37%|###7 | 1928/5148.0 [03:05<03:47, 14.15it/s]
37%|###7 | 1930/5148.0 [03:06<04:45, 11.29it/s]
38%|###7 | 1934/5148.0 [03:06<03:39, 14.65it/s]
38%|###7 | 1938/5148.0 [03:06<03:11, 16.80it/s]
38%|###7 | 1940/5148.0 [03:06<03:27, 15.47it/s]
38%|###7 | 1944/5148.0 [03:06<02:59, 17.87it/s]
38%|###7 | 1946/5148.0 [03:06<04:11, 12.72it/s]
38%|###7 | 1948/5148.0 [03:07<05:06, 10.43it/s]
38%|###7 | 1952/5148.0 [03:07<03:55, 13.59it/s]
38%|###7 | 1954/5148.0 [03:07<04:04, 13.06it/s]
38%|###7 | 1956/5148.0 [03:07<04:05, 13.02it/s]
38%|###8 | 1958/5148.0 [03:08<05:11, 10.25it/s]
38%|###8 | 1960/5148.0 [03:08<05:01, 10.57it/s]
38%|###8 | 1962/5148.0 [03:08<05:36, 9.47it/s]
38%|###8 | 1964/5148.0 [03:08<05:17, 10.04it/s]
38%|###8 | 1966/5148.0 [03:09<06:13, 8.52it/s]
38%|###8 | 1967/5148.0 [03:09<06:38, 7.98it/s]
38%|###8 | 1971/5148.0 [03:09<04:33, 11.62it/s]
38%|###8 | 1976/5148.0 [03:09<03:13, 16.38it/s]
38%|###8 | 1978/5148.0 [03:09<04:26, 11.90it/s]
38%|###8 | 1980/5148.0 [03:10<05:19, 9.92it/s]
39%|###8 | 1982/5148.0 [03:10<06:03, 8.70it/s]
39%|###8 | 1989/5148.0 [03:10<03:37, 14.50it/s]
39%|###8 | 2000/5148.0 [03:10<02:13, 23.62it/s]
39%|###8 | 2003/5148.0 [03:11<02:51, 18.29it/s]
39%|###8 | 2006/5148.0 [03:11<03:27, 15.14it/s]
39%|###9 | 2008/5148.0 [03:11<03:36, 14.51it/s]
39%|###9 | 2010/5148.0 [03:11<03:38, 14.34it/s]
39%|###9 | 2015/5148.0 [03:12<02:50, 18.38it/s]
39%|###9 | 2018/5148.0 [03:12<02:45, 18.94it/s]
39%|###9 | 2021/5148.0 [03:12<04:02, 12.88it/s]
39%|###9 | 2023/5148.0 [03:13<04:57, 10.52it/s]
39%|###9 | 2028/5148.0 [03:13<03:41, 14.09it/s]
39%|###9 | 2031/5148.0 [03:13<03:26, 15.11it/s]
39%|###9 | 2033/5148.0 [03:13<03:35, 14.44it/s]
40%|###9 | 2035/5148.0 [03:13<03:43, 13.93it/s]
40%|###9 | 2037/5148.0 [03:13<04:40, 11.07it/s]
40%|###9 | 2041/5148.0 [03:14<03:30, 14.75it/s]
40%|###9 | 2043/5148.0 [03:14<03:31, 14.66it/s]
40%|###9 | 2047/5148.0 [03:14<02:56, 17.58it/s]
40%|###9 | 2049/5148.0 [03:14<04:04, 12.68it/s]
40%|###9 | 2051/5148.0 [03:14<04:04, 12.66it/s]
40%|###9 | 2053/5148.0 [03:15<04:50, 10.67it/s]
40%|###9 | 2057/5148.0 [03:15<03:43, 13.83it/s]
40%|###9 | 2059/5148.0 [03:15<03:53, 13.21it/s]
40%|#### | 2066/5148.0 [03:15<02:32, 20.17it/s]
40%|#### | 2069/5148.0 [03:16<03:17, 15.58it/s]
40%|#### | 2071/5148.0 [03:16<04:01, 12.72it/s]
40%|#### | 2073/5148.0 [03:16<04:53, 10.49it/s]
40%|#### | 2075/5148.0 [03:16<04:45, 10.76it/s]
40%|#### | 2077/5148.0 [03:16<04:44, 10.80it/s]
41%|#### | 2092/5148.0 [03:17<01:52, 27.25it/s]
41%|#### | 2097/5148.0 [03:17<01:49, 27.97it/s]
41%|#### | 2100/5148.0 [03:17<02:29, 20.42it/s]
41%|#### | 2103/5148.0 [03:17<02:32, 20.03it/s]
41%|#### | 2107/5148.0 [03:18<02:28, 20.53it/s]
41%|#### | 2110/5148.0 [03:18<02:57, 17.14it/s]
41%|####1 | 2113/5148.0 [03:18<02:58, 16.98it/s]
41%|####1 | 2116/5148.0 [03:18<03:14, 15.57it/s]
41%|####1 | 2119/5148.0 [03:18<03:09, 15.96it/s]
41%|####1 | 2122/5148.0 [03:19<03:03, 16.45it/s]
41%|####1 | 2124/5148.0 [03:19<03:59, 12.61it/s]
41%|####1 | 2127/5148.0 [03:19<03:37, 13.89it/s]
41%|####1 | 2129/5148.0 [03:19<04:31, 11.11it/s]
41%|####1 | 2131/5148.0 [03:19<04:10, 12.02it/s]
41%|####1 | 2133/5148.0 [03:20<03:55, 12.78it/s]
41%|####1 | 2136/5148.0 [03:20<03:34, 14.02it/s]
42%|####1 | 2138/5148.0 [03:20<04:48, 10.42it/s]
42%|####1 | 2140/5148.0 [03:20<04:35, 10.92it/s]
42%|####1 | 2142/5148.0 [03:21<05:22, 9.31it/s]
42%|####1 | 2144/5148.0 [03:21<05:58, 8.37it/s]
42%|####1 | 2147/5148.0 [03:21<04:41, 10.68it/s]
42%|####1 | 2149/5148.0 [03:21<04:31, 11.03it/s]
42%|####1 | 2157/5148.0 [03:21<02:31, 19.75it/s]
42%|####1 | 2160/5148.0 [03:22<03:53, 12.78it/s]
42%|####2 | 2163/5148.0 [03:22<03:33, 14.01it/s]
42%|####2 | 2175/5148.0 [03:22<02:01, 24.53it/s]
42%|####2 | 2178/5148.0 [03:23<02:37, 18.88it/s]
42%|####2 | 2181/5148.0 [03:23<03:26, 14.35it/s]
42%|####2 | 2185/5148.0 [03:23<03:03, 16.12it/s]
42%|####2 | 2187/5148.0 [03:23<03:08, 15.71it/s]
43%|####2 | 2192/5148.0 [03:23<02:34, 19.17it/s]
43%|####2 | 2195/5148.0 [03:24<03:17, 14.99it/s]
43%|####2 | 2200/5148.0 [03:24<02:40, 18.40it/s]
43%|####2 | 2203/5148.0 [03:24<02:45, 17.78it/s]
43%|####2 | 2205/5148.0 [03:24<02:56, 16.69it/s]
43%|####2 | 2208/5148.0 [03:24<02:52, 17.09it/s]
43%|####2 | 2211/5148.0 [03:25<02:49, 17.34it/s]
43%|####2 | 2213/5148.0 [03:25<03:57, 12.37it/s]
43%|####3 | 2215/5148.0 [03:25<04:51, 10.07it/s]
43%|####3 | 2217/5148.0 [03:26<05:49, 8.38it/s]
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Calculate the crossing invariant tensor indices [Kaden2016] : the parallel diffusivity, the perpendicular diffusivity, the fractional anisotropy and the mean diffusivity.
d_par = f_fit.dpar
d_perp = f_fit.dperp
fa = f_fit.fractional_anisotropy()
md = f_fit.mean_diffusivity()
Show the indices and save them in FORECAST_indices.png.
fig = plt.figure(figsize=(6, 6))
ax1 = fig.add_subplot(2, 2, 1, title='parallel diffusivity')
ax1.set_axis_off()
ind = ax1.imshow(d_par[:, :, 0].T, interpolation='nearest',
origin='lower', cmap=plt.cm.gray)
plt.colorbar(ind, shrink=0.6)
ax2 = fig.add_subplot(2, 2, 2, title='perpendicular diffusivity')
ax2.set_axis_off()
ind = ax2.imshow(d_perp[:, :, 0].T, interpolation='nearest',
origin='lower', cmap=plt.cm.gray)
plt.colorbar(ind, shrink=0.6)
ax3 = fig.add_subplot(2, 2, 3, title='fractional anisotropy')
ax3.set_axis_off()
ind = ax3.imshow(fa[:, :, 0].T, interpolation='nearest',
origin='lower', cmap=plt.cm.gray)
plt.colorbar(ind, shrink=0.6)
ax4 = fig.add_subplot(2, 2, 4, title='mean diffusivity')
ax4.set_axis_off()
ind = ax4.imshow(md[:, :, 0].T, interpolation='nearest',
origin='lower', cmap=plt.cm.gray)
plt.colorbar(ind, shrink=0.6)
plt.savefig('FORECAST_indices.png', dpi=300, bbox_inches='tight')
Load an ODF reconstruction sphere
sphere = get_sphere('repulsion724')
Compute the fODFs.
odf = f_fit.odf(sphere)
print('fODF.shape (%d, %d, %d, %d)' % odf.shape)
fODF.shape (108, 129, 1, 724)
Display a part of the fODFs
odf_actor = actor.odf_slicer(odf[30:60, 30:60, :], sphere=sphere,
colormap='plasma', scale=0.6)
scene = window.Scene()
scene.add(odf_actor)
window.record(scene, out_path='fODFs.png', size=(600, 600), magnification=4)
Fiber Orientation Distribution Functions, in a small ROI of the brain.
References
[Anderson2005]
Anderson A. W., “Measurement of Fiber Orientation Distributions Using High Angular Resolution Diffusion Imaging”, Magnetic Resonance in Medicine, 2005.
[Kaden2016]
(1,2)
Kaden E. et al., “Quantitative Mapping of the Per-Axon Diffusion Coefficients in Brain White Matter”, Magnetic Resonance in Medicine, 2016.
[Zucchelli2017]
Zucchelli E. et al., “A generalized SMT-based framework for Diffusion MRI microstructural model estimation”, MICCAI Workshop on Computational DIFFUSION MRI (CDMRI), 2017.
[Alexander2017]
Alexander LM, Escalera J, Ai L, et al. An open resource for transdiagnostic research in pediatric mental health and learning disorders. Sci Data. 2017;4:170181.
[RichieHalford2022]
Richie-Halford A, Cieslak M, Ai L, et al. An analysis-ready and quality controlled resource for pediatric brain white-matter research. Scientific Data. 2022;9(1):1-27.
Total running time of the script: ( 6 minutes 41.848 seconds)