OSCR

High-resolution whole-brain magnetic resonance spectroscopic imaging in youth at risk for psychosis.

Code ↔ Paper

8 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 8 matches
  1. [1] § Methods › 3D-MRSI data pre-processing › Spatial filtering ↔ mrsitoolbox/filters/biharmonic.py, lines 130–172 · score 0.84 · smooth_img, spatial filtering, full width, spatially smoothed, nilearn, FWHM
  2. [2] § Methods › 3D-MRSI data pre-processing › Spatial filtering ↔ mrsitoolbox/filters/biharmonic.py, lines 21–81 · score 0.78 · biharmonic inpainting, NaNs, brain mask, median, spike, zero
  3. [3] § Methods › 3D-MRSI data pre-processing › Partial volume effect (PVE) correction ↔ mrsitoolbox/filters/pve.py, lines 38–96 · score 0.68 · CSF partial volume, T1w space, PVE, inverse, CAT12, WM
  4. [4] § Methods › 3D-MRSI reconstruction ↔ scripts/create_BIDS_gui.py, lines 457–473 · score 0.59 · GPC, PCh, PCr, GSH, GABA, NAAG
  5. [5] § Methods › 3D-MRSI data pre-processing ↔ experiments/Preprocessing/preprocess.py, lines 1562–1677 · score 0.54 · partial volume, MRSI map, PVE, preprocessing, T1w, filtering
  6. [6] § Methods › 3D-MRSI data pre-processing › Spatial filtering ↔ mrsitoolbox/filters/biharmonic.py, lines 21–81 · score 0.54 · brain mask, brain regions, inpainting, spikes, filtering, voxels
  7. [7] § Methods › 3D-MRSI data pre-processing › Normalization ↔ experiments/MetSiM_pipeline/map_parcel_image_to_mrsi.py, lines 28–129 · score 0.53 · MNI space, T1w space, MNI152, transformations, maps, MRSI
  8. [8] § Methods › 3D-MRSI data pre-processing › Co-registration with T1w ↔ experiments/Preprocessing/skull_strip_hdbet.py, lines 86–172 · score 0.50 · skull striped, FSL, T1w

Paper

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The authors' code

Python · 244 lines · 11 KB · other · 3 matches

  1. import numpy as np
  2. import nibabel as nib
  3. import math
  4. from scipy.ndimage import median_filter
  5. from skimage.restoration import inpaint_biharmonic
  6. from nilearn import image as nil_img
  7. from scipy.ndimage import generic_filter
  9. from ..tools.filetools import FileTools
  12. ftools = FileTools()
  14. class BiHarmonic:
  15. def __init__(self):
  16. """
  17. Initialize the BiHarmonic processing object.
  18. """
  19. pass
  21. def proc(self, image_og, brain_mask, fwhm=8, channel=0,percentile=95):
  22. """
  23. Process the input image by detecting spikes, inpainting defects, and applying spatial filtering.
  25. Parameters:
  26. image_og (nib.Nifti1Image): The original image data.
  27. brain_mask (np.ndarray): Boolean mask to specify the brain region. Voxels outside this mask will be set to zero.
  28. fwhm (float, optional): Full width at half maximum (FWHM) in mm for the spatial filter. If None, it is computed
  29. from the header voxel dimensions. Default is 8.
  30. channel (int, optional): Channel index to use if image_og is a 4D array. Default is 0.
  32. Returns:
  33. np.ndarray: The processed image data after spike removal, inpainting, and spatial filtering.
  34. """
  35. # If input is a Nifti image, extract data and header.
  36. if isinstance(image_og, nib.Nifti1Image):
  37. image_og_np = image_og.get_fdata()
  38. header = image_og.header
  39. # If input is a NumPy array, use it directly. If 4D, select the specified channel.
  40. else:
  41. raise TypeError("image_og must be a nib.Nifti1Image")
  42. if image_og_np.ndim == 4:
  43. image_og_np = image_og_np.squeeze()
  45. # If input is a Nifti image, extract data and header.
  46. if isinstance(brain_mask, nib.Nifti1Image):
  47. brain_mask_np = brain_mask.get_fdata().astype(bool)
  48. # If input is a NumPy array, use it directly. If 4D, select the specified channel.
  49. else:
  50. raise TypeError("brain_mask must be a nib.Nifti1Image")
  51. if brain_mask_np.ndim == 4:
  52. brain_mask_np = brain_mask_np[:, :, :].squeeze().astype(bool)
  56. # Detect spikes based on a sigma threshold converted to a percentile.
  57. spike_mask = self.get_spike_mask(image_og_np, percentile=percentile, bnd_np=None)
  58. self.spike_mask = spike_mask
  59. image_unspiked_np = self.inpaint_voxels_with_median(image_og_np, spike_mask)
  60. # Detect holes (NaNs) in the image.
  61. nan_mask = np.isnan(image_unspiked_np)
  62. # Detect missing values in image wrt brain mask in the image.
  63. missing_mask = np.zeros_like(image_unspiked_np).astype(bool)
  64. missing_mask[(image_unspiked_np==0) & (brain_mask_np==1)]=True
  65. # Combine masks for inpainting.
  66. inpaint_mask = (nan_mask | spike_mask) | missing_mask
  67. # Inpaint the detected defects using biharmonic inpainting.
  68. image_inpaint_np = self.biharmonic(image_unspiked_np, mask=missing_mask)
  69. image_inpaint_np = self.inpaint_voxels_with_median(image_unspiked_np, missing_mask)
  71. # If fwhm is None and header information is available, compute fwhm based on voxel dimensions.
  72. if fwhm is None and header is not None:
  73. voxel_dims = np.array(header.get_zooms()[:3])
  74. fwhm = np.round(voxel_dims.mean() * np.sqrt(2))
  76. # Apply spatial filtering (e.g., smoothing) to the inpainted image.
  77. image_inpaint_nifti = ftools.numpy_to_nifti(image_inpaint_np,header)
  78. image_smoothed_np = self.spatial_filter(image_inpaint_nifti, fwhm=fwhm, mask=inpaint_mask)
  79. # Zero out voxels outside the brain mask.
  80. image_smoothed_np[~brain_mask_np] = 0
  81. return ftools.numpy_to_nifti(image_smoothed_np,header)
  84. @staticmethod
  85. def get_spike_mask(data_np, percentile=95, bnd_np=None):
  86. """
  87. Generate a boolean mask for spike detection based on a sigma threshold converted to a percentile.
  89. Parameters:
  90. data_np (np.ndarray): The image data.
  91. sigma (float): The sigma value to convert into a percentile threshold (default is 3.5).
  92. bnd_np (np.ndarray, optional): A boolean mask to restrict the data region. Defaults to data > 0.
  94. Returns:
  95. np.ndarray: A boolean mask where True indicates a spike.
  96. """
  97. # Compute the one-sided percentile corresponding to the given sigma value.
  98. # Multiply by 100 because np.percentile expects a value in the range [0, 100].
  100. # Default to a mask where data is positive if no boundary mask is provided.
  101. if bnd_np is None:
  102. bnd_np = data_np > 0
  104. # Compute the threshold value at the given percentile.
  105. threshold_val = np.percentile(data_np[bnd_np], percentile)
  106. # Create the spike mask: True for values exceeding the threshold.
  107. spike_mask = data_np > threshold_val
  109. return spike_mask
  111. @staticmethod
  112. def biharmonic(image_orig, mask):
  113. """
  114. Apply biharmonic inpainting to repair defects in the image.
  116. Parameters:
  117. image_orig (np.ndarray): The original image data.
  118. mask (np.ndarray): Boolean mask indicating defective regions (True where defects exist).
  120. Returns:
  121. np.ndarray: The inpainted image.
  122. """
  123. # Create an image with defects removed (set to 0 outside the mask).
  124. image_defect = image_orig.copy()
  125. image_defect[mask] = 0
  126. # Inpaint the defects using biharmonic inpainting.
  127. image_result = inpaint_biharmonic(image_defect, mask)
  128. return image_result
  130. @staticmethod
  131. def spatial_filter(image, fwhm=5, mask=None, filter_size=3, channel=0):
  132. """
  133. Apply a spatial filter to the image by replacing defective voxels with local median values,
  134. followed by smoothing.
  136. Parameters:
  137. image_np (np.ndarray): The image data (3D NumPy array).
  138. fwhm (float, optional): Full width at half maximum for the smoothing filter in mm.
  139. mask (np.ndarray, optional): Boolean mask indicating voxels to be replaced. If provided,
  140. those voxels will be set to the local median.
  141. filter_size (int, optional): The size of the neighborhood for the median filter. Default is 3.
  143. Returns:
  144. np.ndarray: The spatially filtered (smoothed) image data.
  145. """
  146. if isinstance(image, nib.Nifti1Image):
  147. image_og_np = image.get_fdata()
  148. header = image.header
  149. # If input is a NumPy array, use it directly. If 4D, select the specified channel.
  150. elif isinstance(image, np.ndarray):
  151. image_og_np = image
  152. if image_og_np.ndim == 4:
  153. image_og_np = image_og_np[:, :, :, channel]
  154. header = None # No header available when image_og is a NumPy array.
  155. else:
  156. raise TypeError("image_og must be a nib.Nifti1Image or a NumPy array.")
  158. # # Compute the local median using a median filter.
  159. # local_median = median_filter(image_og_np, size=filter_size)
  160. # # Replace voxels specified in the mask with the local median values.
  161. # if mask is not None:
  162. # image_og_np = image_og_np.copy()
  163. # image_og_np[mask] = local_median[mask]
  165. # Create a Nifti image for smoothing. If no header/affine is provided, assume identity affine.
  166. nifti_img = ftools.numpy_to_nifti(image_og_np,header)
  167. # Apply spatial smoothing using nilearn's smooth_img function.
  168. smoothed_img = nil_img.smooth_img(nifti_img, fwhm=fwhm)
  169. # Extract the smoothed data as a NumPy array.
  170. image_smoothed_np = smoothed_img.get_fdata()
  171. image_og_np[mask] = image_smoothed_np[mask]
  172. return image_smoothed_np
  174. @staticmethod
  175. def create_surrounded_holes_mask(mask_img):
  176. """
  177. Create a binary mask of voxels that are 0 and are completely surrounded by voxels with a value of 1.
  178. This function is intended for binary brain masks where 1 indicates the brain and 0 indicates missing values.
  180. Parameters:
  181. mask_img (np.ndarray): A 3D numpy array representing the binary brain mask.
  183. Returns:
  184. np.ndarray: A binary (bool) 3D mask where True indicates that the voxel is 0 (a hole)
  185. and all its 6-connected neighbors (in the x, y, z directions) are 1.
  187. Note:
  188. Boundary voxels are not considered since they do not have a full set of 6 neighbors.
  189. """
  190. # Ensure the input is a numpy array.
  191. mask_img = np.asarray(mask_img)
  193. # Create boolean arrays for holes (0) and valid brain (1) voxels.
  194. hole_voxels = (mask_img == 0)
  195. valid_voxels = (mask_img == 1)
  197. # Initialize the output mask with False.
  198. surrounded_holes = np.zeros_like(mask_img, dtype=bool)
  200. # For interior voxels, check if a voxel is a hole and all its 6 orthogonal neighbors are valid.
  201. surrounded_holes[1:-1, 1:-1, 1:-1] = (
  202. hole_voxels[1:-1, 1:-1, 1:-1] &
  203. valid_voxels[2: , 1:-1, 1:-1] & # neighbor in +x
  204. valid_voxels[:-2, 1:-1, 1:-1] & # neighbor in -x
  205. valid_voxels[1:-1, 2: , 1:-1] & # neighbor in +y
  206. valid_voxels[1:-1, :-2, 1:-1] & # neighbor in -y
  207. valid_voxels[1:-1, 1:-1, 2: ] & # neighbor in +z
  208. valid_voxels[1:-1, 1:-1, :-2] # neighbor in -z
  209. )
  210. brain_mask_restored = mask_img | surrounded_holes
  211. return brain_mask_restored, surrounded_holes
  213. @staticmethod
  214. def __median_exclude_center(values):
  215. """
  216. Given a flattened neighborhood (e.g., from a 3x3x3 block),
  217. compute the median of the surrounding voxels while excluding the center voxel.
  218. """
  219. center_index = len(values) // 2 # For a 3x3x3, index 13 in a flattened 27-element array.
  220. # Exclude the center element.
  221. neighbors = np.concatenate((values[:center_index], values[center_index+1:]))
  222. return np.median(neighbors)
  224. def inpaint_voxels_with_median(self,image, binary_mask, filter_size=3):
  225. """
  226. For voxels where binary_mask is True, replace the image value with the median
  227. of the surrounding voxels (excluding the voxel itself).
  229. Args:
  230. image (numpy.ndarray): A 3D NumPy array representing the brain image.
  231. binary_mask (numpy.ndarray): A boolean mask of the same shape as image where True indicates voxels to filter.
  232. filter_size (int): Size of the neighborhood to compute the median (default is 3 for a 3x3x3 cube).
  234. Returns:
  235. numpy.ndarray: A new 3D image with the specified voxels replaced.
  236. """
  237. # Compute a median-filtered image for every voxel using the custom function.
  238. # The mode 'mirror' handles border voxels gracefully.
  239. median_image = generic_filter(image, self.__median_exclude_center, size=filter_size, mode='mirror')
  241. # Copy the original image so that we only update the specified voxels.
  242. filtered_image = image.copy()
  243. filtered_image[binary_mask] = median_image[binary_mask]
  244. return filtered_image

biharmonic.py at commit 20b82a1, under other · at the source

Overview

Authors: Edgar Céléreau1, Federico Lucchetti1, Yasser Alemán-Gómez2, Daniella Dwir1, Martine Cleusix1, Jean-Baptiste Ledoux2,3, Raoul Jenni1, Caroline Conchon4, Meritxell Bach Cuadra2,3, Zoé Schilliger1,5, Alessandra Solida6, Marco Armando5, Kerstin Jessica Plessen5, Patric Hagmann2, Philippe Conus4, Antoine Klauser7, Paul Klauser1,5
ORCID iDs: Edgar Céléreau, Federico Lucchetti, Yasser Alemán-Gómez, Daniella Dwir, Jean-Baptiste Ledoux, Raoul Jenni, Meritxell Bach Cuadra, Zoé Schilliger, Alessandra Solida, Marco Armando, Kerstin Jessica Plessen, Patric Hagmann, Philippe Conus, Antoine Klauser, Paul Klauser
  1. Centre for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
  2. Service of Diagnostic and Interventional Radiology, Department of Medical Radiology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
  3. CIBM Center for BioMedical Imaging, Lausanne, Switzerland
  4. Service of General Psychiatry, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
  5. Division of child and adolescent psychiatry, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
  6. Centre Neuchatelois de Psychiatrie, Neuchatel, Switzerland
  7. Swiss Innovation Hub, Siemens Healthineers International AG, Lausanne, Switzerland
Institutions: Centre Hospitalier Universitaire Vaudois (Switzerland); University of Lausanne (Switzerland); Centre d'Imagerie BioMedicale (Switzerland)
Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1276
Dates: received 18 June 2025; accepted 21 May 2026; published online 17 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/imag.a.1276 · PMID 42326559 · PMCID PMC13277781 · OpenAlex W4411576030
Open access: diamond, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.06.22.660965
Status: code verified
Categories: human (organism), schizophrenia / psychosis (population), clinical / translational (subfield)
Methods: Connectivity, Statistics, Machine learning, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: magnetic resonance spectroscopic imaging, clinical high risk, schizotypal personality disorder, N-acetylasparate, inositol, voxel-based analyses
Topic: Advanced MRI Techniques and Applications (Radiology, Nuclear Medicine and Imaging, Medicine), according to OpenAlex
Funding: Faculté de Biologie et de Médecine, Université de Lausanne; Swiss National Science Foundation (215728); Adrian & Simone Frutiger Foundation; Fondation Leenaards
Citations: cited by 1 paper (Europe PMC); 105 references in the paper

Abstract

Advances in three-dimensional magnetic resonance spectroscopic imaging (3D-MRSI) allow for the high-resolution mapping of multiple neurometabolites throughout the entire brain in vivo and within clinically compatible time frames. Leveraging this capability, we created a voxel-based pipeline that corrects and spatially normalizes whole-brain maps of total N-acetylaspartate (tNAA), myo-inositol (Ins), choline compounds (Cho), glutamate + glutamine (Glx) and creatine + phosphocreatine (tCr). We examined 2 different 3D-MRSI dataset: first, a clinical sample of adolescents and young adults at risk for psychosis (n = 21) meeting DSM-5 criteria for Attenuated Psychosis Syndrome (APS) or Schizotypal Personality Disorder (SCZT), and age-/sex-matched healthy controls (n = 13); and second, a non-clinical sample of adolescents (n = 61) scanned on a different site. The objective of the study was threefold: first, to assess the reproducibility of 3D-MRSI measures across datasets and scanning sites; second, to validate the feasibility of whole-brain, voxel-based analyses on 3D-MRSI data; and third, to test the sensitivity of this approach. Metabolite distributions showed reproducible regional variation in standard space between the two independent samples and scanning sites (r ranging from 0.82 to 0.99). Relative to controls, at-risk participants exhibited higher tNAA levels in frontal grey matter; the SCZT subgroup additionally displayed widespread cortical and subcortical elevations of Ins levels compared with both APS and controls. Voxel-based analyses of structural (i.e., gray and white matter volumes or densities) and diffusion (i.e., generalized fractional anisotropy) parameters yielded no significant differences between patients and controls. These preliminary findings suggest that high-resolution 3D-MRSI may be sensitive enough to detect subtle neurometabolic alterations at the group level in the early stages of psychotic disorders when structural or diffusion measures show no difference. High-resolution whole-brain metabolic mapping may have the potential to help with early identification of young people at risk for psychosis or other mental disorders.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 8 matches between paragraphs and lines of code.

MRSI-Psychosis-UP/MRSI-Metabolic-Connectome

License: other
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 20b82a12a75e690f559c91b948d839d2ebd2d23b, 4 September 2026
Languages: Python (52), Shell (2)
Size: 150 files, 54 scripts
Software Heritage: not archived
Found in: “Data and Code Availability”
Holds: README, license file, environment (environment.yaml, pyproject.toml, requirements.txt, setup.py)
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (39 files), NiBabel (25 files), Matplotlib (15 files), Nilearn (15 files), SciPy (11 files), pandas (8 files), NetworkX (7 files), scikit-learn (5 files), DIPY (3 files), FSL (3 files), seaborn (3 files), Brain Connectivity Toolbox (2 files), CuPy (2 files), UMAP (2 files), ANTs (1 file), BrainSpace (1 file), MNE-Python (1 file), Numba (1 file), Pillow (1 file), Plotly (1 file), scikit-image (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
56 files
At the source: github.com/MRSI-Psychosis-UP/MRSI-Metabolic-Connectome

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 54 scripts, each with its path and the digest of its content;
  • 8 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

No dataset and no data link were found in the paper.

Data and Code Availability

Because of the highly sensitive nature of the clinical data used in this study, researchers interested in accessing the dataset should contact the authors with a well-motivated request detailing their research aims and the intended use of the data. The code used to correct and normalize 3D-MRSI maps presented in this paper is available at the following GitHub repository: https://github.com/MRSI-Psychosis-UP/MRSI-Metabolic-Connectome.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 2, 28 September 2026

  • Authors: added Jean-Baptiste Ledoux (0000-0003-0447-5073); Alessandra Solida (0000-0001-5080-366X); removed Jean-Baptiste Ledoux; Alessandra Solida

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 17 authors, 6 keywords, 4 funders, 102 references.

Cite

This paper

Céléreau, E., Lucchetti, F., Alemán-Gómez, Y., Dwir, D., Cleusix, M., Ledoux, J.-B., Jenni, R., Conchon, C., Bach Cuadra, M., Schilliger, Z., Solida, A., Armando, M., Plessen, K. J., Hagmann, P., Conus, P., Klauser, A., & Klauser, P. (2026). High-resolution whole-brain magnetic resonance spectroscopic imaging in youth at risk for psychosis. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1276. https://doi.org/10.1162/imag.a.1276

BibTeX

@article{celereau2026high,
author = {Céléreau, Edgar and Lucchetti, Federico and Alemán-Gómez, Yasser and Dwir, Daniella and Cleusix, Martine and Ledoux, Jean-Baptiste and Jenni, Raoul and Conchon, Caroline and Bach Cuadra, Meritxell and Schilliger, Zoé and Solida, Alessandra and Armando, Marco and Plessen, Kerstin Jessica and Hagmann, Patric and Conus, Philippe and Klauser, Antoine and Klauser, Paul},
title = {{High-resolution whole-brain magnetic resonance spectroscopic imaging in youth at risk for psychosis}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = jun,
volume = {4},
pages = {IMAG.a.1276},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1276},
url = {https://doi.org/10.1162/imag.a.1276},
pmid = {42326559},
pmcid = {PMC13277781}
}

RIS

TY - JOUR
AU - Céléreau, Edgar
AU - Lucchetti, Federico
AU - Alemán-Gómez, Yasser
AU - Dwir, Daniella
AU - Cleusix, Martine
AU - Ledoux, Jean-Baptiste
AU - Jenni, Raoul
AU - Conchon, Caroline
AU - Bach Cuadra, Meritxell
AU - Schilliger, Zoé
AU - Solida, Alessandra
AU - Armando, Marco
AU - Plessen, Kerstin Jessica
AU - Hagmann, Patric
AU - Conus, Philippe
AU - Klauser, Antoine
AU - Klauser, Paul
TI - High-resolution whole-brain magnetic resonance spectroscopic imaging in youth at risk for psychosis
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/06/17
VL - 4
SP - IMAG.a.1276
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1276
UR - https://doi.org/10.1162/imag.a.1276
LA - en
ER -

CSL-JSON

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"URL": "https://doi.org/10.1162/imag.a.1276",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
17
]
]
}
}

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