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Impairment of brain short association fibers across clinical stages in amyotrophic lateral sclerosis: a new biomarker mirroring disease progression.

Code ↔ Paper

4 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 4 matches
  1. [1] § Methods › Data processing ↔ scripts/gibbs.py, lines 127–168 · score 0.72 · local subvoxel shifts, Gibbs ringing, artifacts, removal
  2. [2] § Methods › Data processing ↔ scripts/pnl_epi.py, lines 17–150 · score 0.67 · distortion correction, rigid registration, FSL, transformation, affine, mask
  3. [3] § Methods › MRI data acquisition ↔ scripts/gibbs.py, lines 226–303 · score 0.65 · matrix dimensions, gradient directions, slice, weighted, MRI, diffusion
  4. [4] § Methods › Data processing ↔ scripts/fsl_eddy.py, lines 85–190 · score 0.54 · brain mask, FSL, rotated, eddy, BET, space

Paper

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The authors' code

Python · 303 lines · 9.9 KB · other · 2 matches

  1. # https://raw.githubusercontent.com/dipy/dipy/160b202e7cd0ed821e5e1bfc4cf14815700430f3/dipy/denoise/gibbs.py
  2. from __future__ import division, print_function, absolute_import
  4. import numpy as np
  7. def _image_tv(x, axis=0, n_points=3):
  8. """ Computes total variation (TV) of matrix x across a given axis and
  9. along two directions.
  11. Parameters
  12. ----------
  13. x : 2D ndarray
  14. matrix x
  15. axis : int (0 or 1)
  16. Axis which TV will be calculated. Default a is set to 0.
  17. n_points : int
  18. Number of points to be included in TV calculation.
  20. Returns
  21. -------
  22. ptv : 2D ndarray
  23. Total variation calculated from the right neighbours of each point.
  24. ntv : 2D ndarray
  25. Total variation calculated from the left neighbours of each point.
  27. """
  28. xs = x.copy() if axis else x.T.copy()
  30. # Add copies of the data so that data extreme points are also analysed
  31. xs = np.concatenate((xs[:, (-n_points-1):], xs, xs[:, 0:(n_points+1)]),
  32. axis=1)
  34. ptv = np.absolute(xs[:, (n_points+1):(-n_points-1)] -
  35. xs[:, (n_points+2):(-n_points)])
  36. ntv = np.absolute(xs[:, (n_points+1):(-n_points-1)] -
  37. xs[:, (n_points):(-n_points-2)])
  38. for n in range(1, n_points):
  39. ptv = ptv + np.absolute(xs[:, (n_points+1+n):(-n_points-1+n)] -
  40. xs[:, (n_points+2+n):(-n_points+n)])
  41. ntv = ntv + np.absolute(xs[:, (n_points+1-n):(-n_points-1-n)] -
  42. xs[:, (n_points-n):(-n_points-2-n)])
  44. if axis:
  45. return ptv, ntv
  46. else:
  47. return ptv.T, ntv.T
  50. def _gibbs_removal_1d(x, axis=0, n_points=3):
  51. """Suppresses Gibbs ringing along a given axis using fourier sub-shifts.
  53. Parameters
  54. ----------
  55. x : 2D ndarray
  56. Matrix x.
  57. axis : int (0 or 1)
  58. Axis in which Gibbs oscillations will be suppressed.
  59. Default is set to 0.
  60. n_points : int, optional
  61. Number of neighbours to access local TV (see note).
  62. Default is set to 3.
  64. Returns
  65. -------
  66. xc : 2D ndarray
  67. Matrix with suppressed Gibbs oscillations along the given axis.
  69. Notes
  70. -----
  71. This function suppresses the effects of Gibbs oscillations based on the
  72. analysis of local total variation (TV). Although artefact correction is
  73. done based on two adjacent points for each voxel, total variation should be
  74. accessed in a larger range of neighbours. The number of neighbours to be
  75. considered in TV calculation can be adjusted using the parameter n_points.
  77. """
  78. ssamp = np.linspace(0.02, 0.9, num=45)
  80. xs = x.copy() if axis else x.T.copy()
  82. # TV for shift zero (baseline)
  83. tvr, tvl = _image_tv(xs, axis=1, n_points=n_points)
  84. tvp = np.minimum(tvr, tvl)
  85. tvn = tvp.copy()
  87. # Find optimal shift for gibbs removal
  88. isp = xs.copy()
  89. isn = xs.copy()
  90. sp = np.zeros(xs.shape)
  91. sn = np.zeros(xs.shape)
  92. N = xs.shape[1]
  93. c = np.fft.fftshift(np.fft.fft2(xs))
  94. k = np.linspace(-N/2, N/2-1, num=N)
  95. k = (2.0j * np.pi * k) / N
  96. for s in ssamp:
  97. # Access positive shift for given s
  98. img_p = abs(np.fft.ifft2(np.fft.fftshift(c * np.exp(k*s))))
  99. tvsr, tvsl = _image_tv(img_p, axis=1, n_points=n_points)
  100. tvs_p = np.minimum(tvsr, tvsl)
  102. # Access negative shift for given s
  103. img_n = abs(np.fft.ifft2(np.fft.fftshift(c * np.exp(-k*s))))
  104. tvsr, tvsl = _image_tv(img_n, axis=1, n_points=n_points)
  105. tvs_n = np.minimum(tvsr, tvsl)
  107. # Update positive shift params
  108. isp[tvp > tvs_p] = img_p[tvp > tvs_p]
  109. sp[tvp > tvs_p] = s
  110. tvp[tvp > tvs_p] = tvs_p[tvp > tvs_p]
  112. # Update negative shift params
  113. isn[tvn > tvs_n] = img_n[tvn > tvs_n]
  114. sn[tvn > tvs_n] = s
  115. tvn[tvn > tvs_n] = tvs_n[tvn > tvs_n]
  117. # check non-zero sub-voxel shifts
  118. idx = np.nonzero(sp + sn)
  120. # use positive and negative optimal sub-voxel shifts to interpolate to
  121. # original grid points
  122. xs[idx] = (isp[idx] - isn[idx])/(sp[idx] + sn[idx])*sn[idx] + isn[idx]
  124. return xs if axis else xs.T
  127. def _weights(shape):
  128. """ Computes the weights necessary to combine two images processed by
  129. the 1D Gibbs removal procedure along two different axes [1]_.
  131. Parameters
  132. ----------
  133. shape : tuple
  134. shape of the image.
  136. Returns
  137. -------
  138. G0 : 2D ndarray
  139. Weights for the image corrected along axis 0.
  140. G1 : 2D ndarray
  141. Weights for the image corrected along axis 1.
  143. References
  144. ----------
  145. .. [1] Kellner E, Dhital B, Kiselev VG, Reisert M. Gibbs-ringing artifact
  146. removal based on local subvoxel-shifts. Magn Reson Med. 2016
  147. doi: 10.1002/mrm.26054.
  149. """
  150. G0 = np.zeros(shape)
  151. G1 = np.zeros(shape)
  152. k0 = np.linspace(-np.pi, np.pi, num=shape[0])
  153. k1 = np.linspace(-np.pi, np.pi, num=shape[1])
  155. # Middle points
  156. K1, K0 = np.meshgrid(k1[1:-1], k0[1:-1])
  157. cosk0 = 1.0 + np.cos(K0)
  158. cosk1 = 1.0 + np.cos(K1)
  159. G1[1:-1, 1:-1] = cosk0 / (cosk0 + cosk1)
  160. G0[1:-1, 1:-1] = cosk1 / (cosk0 + cosk1)
  162. # Boundaries
  163. G1[1:-1, 0] = G1[1:-1, -1] = 1
  164. G1[0, 0] = G1[-1, -1] = G1[0, -1] = G1[-1, 0] = 1/2
  165. G0[0, 1:-1] = G0[-1, 1:-1] = 1
  166. G0[0, 0] = G0[-1, -1] = G0[0, -1] = G0[-1, 0] = 1/2
  168. return G0, G1
  171. def _gibbs_removal_2d(image, n_points=3, G0=None, G1=None):
  172. """ Suppress Gibbs ringing of a 2D image.
  174. Parameters
  175. ----------
  176. image : 2D ndarray
  177. Matrix containing the 2D image.
  178. n_points : int, optional
  179. Number of neighbours to access local TV (see note). Default is
  180. set to 3.
  181. G0 : 2D ndarray, optional.
  182. Weights for the image corrected along axis 0. If not given, the
  183. function estimates them using the function :func:`_weights`.
  184. G1 : 2D ndarray
  185. Weights for the image corrected along axis 1. If not given, the
  186. function estimates them using the function :func:`_weights`.
  188. Returns
  189. -------
  190. imagec : 2D ndarray
  191. Matrix with Gibbs oscillations reduced along axis a.
  193. Notes
  194. -----
  195. This function suppresses the effects of Gibbs oscillations based on the
  196. analysis of local total variation (TV). Although artefact correction is
  197. done based on two adjacent points for each voxel, total variation should be
  198. accessed in a larger range of neighbours. The number of neighbours to be
  199. considered in TV calculation can be adjusted the using the parameter.
  200. n_points.
  202. References
  203. ----------
  204. Please cite the following articles
  205. .. [1] Neto Henriques, R., 2018. Advanced Methods for Diffusion MRI Data
  206. Analysis and their Application to the Healthy Ageing Brain
  207. (Doctoral thesis). https://doi.org/10.17863/CAM.29356
  208. .. [2] Kellner E, Dhital B, Kiselev VG, Reisert M. Gibbs-ringing artifact
  209. removal based on local subvoxel-shifts. Magn Reson Med. 2016
  210. doi: 10.1002/mrm.26054.
  212. """
  213. if np.any(G0) is None or np.any(G1) is None:
  214. G0, G1 = _weights(image.shape)
  216. img_c1 = _gibbs_removal_1d(image, axis=1, n_points=n_points)
  217. img_c0 = _gibbs_removal_1d(image, axis=0, n_points=n_points)
  219. C1 = np.fft.fft2(img_c1)
  220. C0 = np.fft.fft2(img_c0)
  221. imagec = abs(np.fft.ifft2(np.fft.fftshift(C1)*G1 + np.fft.fftshift(C0)*G0))
  223. return imagec
  226. def gibbs_removal(vol, slice_axis=2, n_points=3):
  227. """Suppresses Gibbs ringing artefacts of images volumes.
  229. Parameters
  230. ----------
  231. vol : ndarray ([X, Y]), ([X, Y, Z]) or ([X, Y, Z, g])
  232. Matrix containing one volume (3D) or multiple (4D) volumes of images.
  233. slice_axis : int (0, 1, or 2)
  234. Data axis corresponding to the number of acquired slices.
  235. Default is set to the third axis.
  236. n_points : int, optional
  237. Number of neighbour points to access local TV (see note).
  238. Default is set to 3.
  240. Returns
  241. -------
  242. vol : ndarray ([X, Y]), ([X, Y, Z]) or ([X, Y, Z, g])
  243. Matrix containing one volume (3D) or multiple (4D) volumes of corrected
  244. images.
  246. Notes
  247. -----
  248. For 4D matrix last element should always correspond to the number of
  249. diffusion gradient directions.
  251. References
  252. ----------
  253. Please cite the following articles
  254. .. [1] Neto Henriques, R., 2018. Advanced Methods for Diffusion MRI Data
  255. Analysis and their Application to the Healthy Ageing Brain
  256. (Doctoral thesis). https://doi.org/10.17863/CAM.29356
  257. .. [2] Kellner E, Dhital B, Kiselev VG, Reisert M. Gibbs-ringing artifact
  258. removal based on local subvoxel-shifts. Magn Reson Med. 2016
  259. doi: 10.1002/mrm.26054.
  261. """
  262. nd = vol.ndim
  264. # check the axis corresponding to different slices
  265. # 1) This axis cannot be larger than 2
  266. if slice_axis > 2:
  267. raise ValueError("Different slices have to be organized along" +
  268. "one of the 3 first matrix dimensions")
  270. # 2) If this is not 2, swap axes so that different slices are ordered
  271. # along axis 2. Note that swapping is not required if data is already a
  272. # single image
  273. elif slice_axis < 2 and nd > 2:
  274. vol = np.swapaxes(vol, slice_axis, 2)
  276. # check matrix dimension
  277. if nd == 4:
  278. inishap = vol.shape
  279. vol = vol.reshape((inishap[0], inishap[1], inishap[2] * inishap[3]))
  280. elif nd > 4:
  281. raise ValueError("Data have to be a 4D, 3D or 2D matrix")
  282. elif nd < 2:
  283. raise ValueError("Data is not an image")
  285. # Produce weigthing functions for 2D Gibbs removal
  286. shap = vol.shape
  287. G0, G1 = _weights(shap[:2])
  289. # Run Gibbs removal of 2D images
  290. if nd == 2:
  291. vol = _gibbs_removal_2d(vol, n_points=n_points, G0=G0, G1=G1)
  292. else:
  293. for vi in range(shap[2]):
  294. vol[:, :, vi] = _gibbs_removal_2d(vol[:, :, vi], n_points=n_points,
  295. G0=G0, G1=G1)
  297. # Reshape data to original format
  298. if nd == 4:
  299. vol = vol.reshape(inishap)
  300. if slice_axis < 2 and nd > 2:
  301. vol = np.swapaxes(vol, slice_axis, 2)
  303. return vol

gibbs.py at commit 7c09432, under other · at the source

Overview

Authors: Nao-Xin Huang1, Zi-Wei Cai1, Shao-Peng Zhuang1, Hong-Yu Lin2, Sheng Chen3, Zhang-Yu Zou3, Ye Wu4, Hua-Jun Chen1
  1. Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, 350001 China
  2. School of Medical Imaging, Fujian Medical University, Fuzhou, 350122 China
  3. Department of Neurology, Fujian Medical University Union Hospital, Fuzhou, 350001 China
  4. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094 China
Institutions: Union Hospital (China); Fujian Medical University (China); Nanjing University of Science and Technology (China)
Journal: BMC medicine, volume 24, issue 1, article 241
Dates: received 1 November 2025; accepted 3 March 2026; published online 6 March 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1186/s12916-026-04770-7 · PMID 41792723 · PMCID PMC13077863 · OpenAlex W7134039366
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), other condition (population), clinical / translational (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, fMRI & imaging, Physiology & signal measures
Keywords: Amyotrophic lateral sclerosis, Short association fiber, Microstructural impairment, Neurite orientation dispersion and density imaging, Clinical stages
MeSH: Amyotrophic Lateral Sclerosis*, Brain*, Adult, Aged, Biomarkers, Diffusion Tensor Imaging, Disease Progression, Female, Humans, Male, Middle Aged, Neurites, Severity of Illness Index (* major topic)
Topic: Amyotrophic Lateral Sclerosis Research (Neurology, Medicine), according to OpenAlex
Funding: Fujian Provincial Health Technology Project (2023CXA009); National Natural Science Foundation of China (82572160); Fujian Province Joint Funds for the Innovation of Science and Technology (2024Y9253, 2024Y9256); Natural Science Foundation of Fujian Province (2024J01625)
Citations: not cited yet (Europe PMC); 59 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above, with 4 matches between paragraphs and lines of code.

pnlbwh/pnlNipype

License: other
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 7c094328e9470a078f2d6e5540c6ceb9817258c3, 10 July 2026
Languages: Python (26), Shell (1)
Size: 90 files, 27 scripts
Software Heritage: not archived
Found in: the text, “Data processing”
Holds: license file, CITATION.cff, environment (requirements.txt), documentation
Not found: README, tests, continuous integration
Tools: NumPy (14 files), NiBabel (5 files), pandas (3 files), FreeSurfer (2 files), ANTs (1 file), DIPY (1 file), FSL (1 file), Matplotlib (1 file), scikit-image (1 file), SciPy (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
28 files
At the source: github.com/pnlbwh/pnlNipype

Tracing map

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 27 scripts, each with its path and the digest of its content;
  • 4 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

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Data availability statement

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  • it says that the data are available on request

Read it in the paper: doi.org/10.1186/s12916-026-04770-7.

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 5 keywords, 13 MeSH terms, 4 funders, 56 references.

Cite

This paper

Huang, N.-X., Cai, Z.-W., Zhuang, S.-P., Lin, H.-Y., Chen, S., Zou, Z.-Y., Wu, Y., & Chen, H.-J. (2026). Impairment of brain short association fibers across clinical stages in amyotrophic lateral sclerosis: a new biomarker mirroring disease progression. BMC medicine, 24(1), 241. https://doi.org/10.1186/s12916-026-04770-7

BibTeX

@article{huang2026impairment,
author = {Huang, Nao-Xin and Cai, Zi-Wei and Zhuang, Shao-Peng and Lin, Hong-Yu and Chen, Sheng and Zou, Zhang-Yu and Wu, Ye and Chen, Hua-Jun},
title = {{Impairment of brain short association fibers across clinical stages in amyotrophic lateral sclerosis: a new biomarker mirroring disease progression}},
journal = {BMC medicine},
year = {2026},
month = mar,
volume = {24},
number = {1},
pages = {241},
publisher = {BioMed Central},
issn = {1741-7015},
doi = {10.1186/s12916-026-04770-7},
url = {https://doi.org/10.1186/s12916-026-04770-7},
pmid = {41792723},
pmcid = {PMC13077863}
}

RIS

TY - JOUR
AU - Huang, Nao-Xin
AU - Cai, Zi-Wei
AU - Zhuang, Shao-Peng
AU - Lin, Hong-Yu
AU - Chen, Sheng
AU - Zou, Zhang-Yu
AU - Wu, Ye
AU - Chen, Hua-Jun
TI - Impairment of brain short association fibers across clinical stages in amyotrophic lateral sclerosis: a new biomarker mirroring disease progression
T2 - BMC medicine
J2 - BMC Med
PY - 2026
DA - 2026/03/06
VL - 24
IS - 1
SP - 241
SN - 1741-7015
PB - BioMed Central
DO - 10.1186/s12916-026-04770-7
UR - https://doi.org/10.1186/s12916-026-04770-7
LA - en
ER -

CSL-JSON

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"id": "10.1186/s12916-026-04770-7",
"type": "article-journal",
"title": "Impairment of brain short association fibers across clinical stages in amyotrophic lateral sclerosis: a new biomarker mirroring disease progression",
"container-title": "BMC medicine",
"author": [
{
"family": "Huang",
"given": "Nao-Xin"
},
{
"family": "Cai",
"given": "Zi-Wei"
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{
"family": "Zhuang",
"given": "Shao-Peng"
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{
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{
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{
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"given": "Hua-Jun"
}
],
"container-title-short": "BMC Med",
"volume": "24",
"issue": "1",
"page": "241",
"DOI": "10.1186/s12916-026-04770-7",
"PMID": "41792723",
"PMCID": "PMC13077863",
"ISSN": "1741-7015",
"publisher": "BioMed Central",
"URL": "https://doi.org/10.1186/s12916-026-04770-7",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
6
]
]
}
}

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