OSCR

Monte Carlo simulations of time-resolved blood flow index: times-of-flight beyond ∼1 ns are necessary for brain-dominated measurements.

Code ↔ Paper

7 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 7 matches
  1. [1] § Methods › Simulation Setup ↔ mc_analysis/fitting/scos_utils.py, lines 64 to 106 · score 0.64 · squared speckle contrast, coherence factor, autocorrelation, exposure, SCOS · Report a mismatch
  2. [2] § Methods › Evaluation of Pulsatile CBF Recovery ↔ mc_analysis/fitting/correlation_metrics.py, lines 238 to 246 · score 0.60 · rCBF, rSBF, ground truth, correlation, BFi, scalp · Report a mismatch
  3. [3] § Methods › Experimental Validation of the Simulation Stack › In vitro benchmarking ↔ mc_analysis/fitting/fit_funcs.py, lines 9 to 40 · score 0.58 · reduced scattering coefficient, absorption coefficient, R1, refractive, fitted, BFi · Report a mismatch
  4. [4] § Results › Recovery of Pulsatile BFi ↔ mc_analysis/fitting/correlation_metrics.py, lines 238 to 246 · score 0.57 · rCBF, rSBF, ground truth, correlation, pulse, BFi · Report a mismatch
  5. [5] § Methods › Simulation Setup ↔ mc_analysis/fitting/fit_funcs.py, lines 9 to 40 · score 0.54 · reduced scattering coefficients, absorption coefficients, wavelength, BFi · Report a mismatch
  6. [6] § Results › Effects of ToF, SDS, and Acquisition Frequency on Brain Sensitivity in the Multilayer Slab Model ↔ mc_analysis/examples/plot_lag_integrated_flow_sensitivity_vs_lag.py, lines 115 to 154 · score 0.50 · source detector separations, Optical properties, Brain sensitivity, configurations, lags, SDS · Report a mismatch
  7. [7] § Methods › Evaluation of Pulsatile CBF Recovery ↔ mc_analysis/fitting/correlation_metrics.py, lines 297 to 331 · score 0.50 · rCBF, rSBF, metric, correlation, scalp, brain · Report a mismatch

Paper

Loaded from Europe PMC by your browser; OSCR does not store it: doi.org · Europe PMC

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The authors' code

Python · 373 lines · 14 KB · MIT · 3 matches

  1. """Collection of functions to evaluate brain sensitivity selectivity and crossing ToFs
  3. """
  5. from dataclasses import dataclass
  6. from typing import Optional
  8. import numpy as np
  9. from scipy.ndimage import gaussian_filter1d
  10. from scipy.signal import find_peaks
  13. @dataclass
  14. class Metric:
  15. corr_per_pulse_brain: np.ndarray
  16. corr_per_pulse_scalp: np.ndarray
  17. rmse_norm_brain: np.ndarray
  18. rmse_norm_scalp: np.ndarray
  19. bfi: np.ndarray
  20. tof: np.ndarray = None
  21. sds: int = None
  22. file_stem: str = None
  23. fit_method: str = None
  24. db_c: Optional[np.ndarray] = None
  25. db_s: Optional[np.ndarray] = None
  28. def get_rms_error(rcbf_estimate, rcbf_truth):
  29. error = rcbf_estimate - rcbf_truth
  30. rms_error = np.sqrt(np.sum(error**2, axis=1) / len(error))
  31. return error, rms_error
  34. def get_relative_cbf(cbf, cbf_baseline=None):
  35. """Definition of rCBF used in Zhao et al. https://doi.org/10.1364/BOE.438303
  37. rCBFitrue = (CBFitrue − CBFi0)/CBFi0 × 100%,
  39. rCBFiestimated = (CBFiestimated − CBFiestimated,0)/CBFiestimated,0 × 100%
  40. """
  41. if cbf_baseline is None:
  42. cbf_baseline = cbf[0]
  43. rcbf = (cbf - cbf_baseline) / cbf_baseline
  44. return rcbf
  47. def get_crossing_time(
  48. rms_error_brain,
  49. rms_error_scalp,
  50. tof_centered,
  51. i_tof_min=2,
  52. i_tof_max=None,
  53. is_rms_error=True,
  54. ):
  55. diff = rms_error_brain - rms_error_scalp
  56. n_crossings = get_number_crossings(diff[i_tof_min:i_tof_max])
  57. if n_crossings > 1:
  58. print("Warning - more than one crossing detected")
  59. diff = gaussian_filter1d(rms_error_brain, 1) - gaussian_filter1d(rms_error_scalp, 1)
  60. n_crossings = get_number_crossings(diff[i_tof_min:i_tof_max])
  61. indices_crossing_tof = np.argwhere((np.diff(np.sign(diff[i_tof_min:i_tof_max])) != 0)) + i_tof_min
  62. indices_crossing_tof = indices_crossing_tof.flatten()
  64. if len(indices_crossing_tof) == 0:
  65. crossing_time = np.nan
  66. else:
  67. # Neglect crossing ToF if RMS error is greater than 1
  68. if is_rms_error:
  69. mask = rms_error_brain[indices_crossing_tof] < 1.0
  70. else:
  71. mask = np.ones(len(indices_crossing_tof), dtype=bool)
  72. if np.sum(mask) == 0:
  73. crossing_time = np.nan
  74. else:
  75. index_min = int(np.min(indices_crossing_tof[mask]))
  76. crossing_time = tof_centered[index_min]
  78. return crossing_time, n_crossings
  81. def get_crossing_tof_correlation(corr_scalp, corr_brain, tof_centered, i_tof_min=2, i_tof_max=None):
  82. diff = corr_scalp - corr_brain
  83. if np.sum(diff[:4]) < 0:
  84. print("Warning - negative difference detected at early ToF - check corr_brain and corr_scalp ordering correct.")
  85. n_crossings = get_number_crossings(diff[i_tof_min:i_tof_max])
  86. if n_crossings > 1:
  87. print("Warning - more than one crossing detected")
  88. diff = gaussian_filter1d(corr_scalp, 1) - gaussian_filter1d(corr_brain, 1)
  89. n_crossings = get_number_crossings(diff[i_tof_min:i_tof_max])
  90. indices_crossing_tof = np.argwhere((np.diff(np.sign(diff[i_tof_min:i_tof_max])) != 0)) + i_tof_min
  91. indices_crossing_tof = indices_crossing_tof.flatten()
  93. if len(indices_crossing_tof) == 0:
  94. crossing_time = np.nan
  95. else:
  96. # Neglect crossing ToF if RMS error is greater than 1
  97. index_min = int(np.min(indices_crossing_tof))
  98. crossing_time = tof_centered[index_min]
  99. return crossing_time, n_crossings
  102. def get_crossing_tof_rmse(rmse_brain, rmse_scalp, tofs, i_tof_min=2, i_tof_max=None):
  103. """Function to get the 'tof' at which the RMSE of the brain and scalp waveforms cross.
  105. Args:
  106. rmse_brain: array, RMSE error brain
  107. rmse_scalp: array, RMSE error scalp
  108. tofs: array, ToF centered
  109. i_tof_min: int, minimum ToF index for search space
  110. i_tof_max: int, maximum ToF index for search space
  112. Returns:
  113. crossing_tof: float, crossing time
  114. n_crossings: int, number of crossings
  115. """
  116. diff = rmse_brain - rmse_scalp
  117. if np.sum(diff[:4]) < 0:
  118. print("Warning - negative difference detected at early ToF - check rmse_brain and rmse_scalp ordering correct.")
  120. n_crossings = get_number_crossings(diff[i_tof_min:i_tof_max])
  121. if n_crossings > 1:
  122. print("Warning - more than one crossing detected, applying smoothing.")
  123. diff = gaussian_filter1d(rmse_brain, 1) - gaussian_filter1d(rmse_scalp, 1)
  124. n_crossings = get_number_crossings(diff[i_tof_min:i_tof_max])
  125. # Find crossing tof indices
  126. indices_crossing_tof = np.argwhere((np.diff(np.sign(diff[i_tof_min:i_tof_max])) != 0)) + i_tof_min
  127. indices_crossing_tof = indices_crossing_tof.flatten()
  129. if len(indices_crossing_tof) == 0:
  130. crossing_tof = np.nan
  131. else:
  132. # Neglect crossing ToF if RMS error is greater than 1
  133. mask = rmse_brain[indices_crossing_tof] < 1.0
  134. if np.sum(mask) == 0:
  135. crossing_tof = np.nan
  136. else:
  137. # select the first crossing tof
  138. index_min = int(np.min(indices_crossing_tof[mask]))
  139. crossing_tof = tofs[index_min]
  140. return crossing_tof, n_crossings
  143. def get_number_crossings(diff):
  144. return np.sum(np.diff(np.sign(diff)) != 0)
  147. def normalise(bfi, allow_nan=False):
  148. _initial_shape = bfi.shape
  149. if bfi.ndim == 1:
  150. bfi = bfi.reshape(1, -1)
  151. if np.any(np.isnan(bfi)) and not allow_nan:
  152. raise ValueError("bfi contains NaNs")
  153. bfi = bfi - np.nanmin(bfi, axis=1)[:, None]
  154. bfi = bfi / np.nanmax(bfi, axis=1)[:, None]
  155. return bfi.reshape(_initial_shape)
  158. def compute_normalised_rmse(bfi_estimate, bfi_truth):
  159. """Compute the normalised RMSE. RMSE of waveforms whose amplitdues are normalised from 0 to 1
  161. Args:
  162. bfi_estimate: array, BFI estimate
  163. bfi_truth: array, BFI truth
  165. Returns:
  166. rms_error_cbf: array, RMS error
  168. """
  169. if bfi_estimate.ndim != 2:
  170. raise ValueError(f"bfi_estimate should be 2D, got {bfi_estimate.shape}")
  171. bfi_est_norm = normalise(bfi_estimate, allow_nan=True)
  172. bfi_est_truth = normalise(bfi_truth, allow_nan=True)
  173. error, rms_error_cbf = get_rms_error(bfi_est_norm, bfi_est_truth)
  174. return rms_error_cbf
  177. def _get_per_pulse_correlation(bfi_estimate, i_end_list, i_start_list, rcbf_truth, rsbf_truth):
  178. """Compute the correlation of bfi with ground truth SBF and CBF per pulse then take the average.
  180. Args:
  181. bfi_estimate: array, BFI estimate
  182. i_end_list: list, list of end indices for each pulse
  183. i_start_list: list, list of start indices for each pulse
  184. rcbf_truth: array, ground truth relative CBF
  185. rsbf_truth: array, ground truth relative SBF
  186. Returns:
  187. corr_scalp: array, correlation with SBF
  188. corr_brain: array, correlation with CBF
  191. """
  192. corr_brain = np.empty(bfi_estimate.shape[0])
  193. corr_scalp = np.empty(bfi_estimate.shape[0])
  194. depth_corr = np.empty(bfi_estimate.shape[0])
  195. for itof in range(bfi_estimate.shape[0]):
  196. rms_brain_seg_corrs = []
  197. rms_scalp_seg_corrs = []
  198. for (i_start, i_end) in zip(i_start_list, i_end_list):
  199. try:
  200. rms_brain_seg_corrs.append(
  201. np.corrcoef(
  202. np.asarray(rcbf_truth)[i_start:i_end],
  203. np.asarray(bfi_estimate)[itof, i_start:i_end],
  204. )[0, 1]
  205. )
  206. rms_scalp_seg_corrs.append(
  207. np.corrcoef(
  208. np.asarray(rsbf_truth)[i_start:i_end],
  209. np.asarray(bfi_estimate)[itof, i_start:i_end],
  210. )[0, 1]
  211. )
  212. except ValueError:
  213. print(i_start, i_end, rms_scalp_seg_corrs, rms_brain_seg_corrs)
  214. print(
  215. np.corrcoef(
  216. np.asarray(rcbf_truth)[i_start:i_end],
  217. bfi_estimate[itof, i_start:i_end],
  218. )[0, 1]
  219. )
  220. print(
  221. np.corrcoef(
  222. np.asarray(rsbf_truth)[i_start:i_end],
  223. bfi_estimate[itof, i_start:i_end],
  224. )[0, 1]
  225. )
  226. print(
  227. "Error in sequence",
  228. np.asarray(rcbf_truth).shape,
  229. np.asarray(rsbf_truth).shape,
  230. np.asarray(bfi_estimate).shape,
  231. )
  232. corr_brain[itof] = np.mean(rms_brain_seg_corrs)
  233. corr_scalp[itof] = np.mean(rms_scalp_seg_corrs)
  234. depth_corr[itof] = (1 - np.abs(corr_scalp[itof])) / (1 - np.abs(corr_brain[itof]))
  235. return corr_scalp, corr_brain
  238. def get_per_pulse_correlation(bfi_estimate, rcbf_truth, rsbf_truth, peak_indices=None):
  239. """Get correlation per/pulse of bfi with ground truth rCBF and rSBF."""
  240. if peak_indices is None:
  241. peak_indices = segment_bfi(rcbf_truth)
  243. i_end_list = peak_indices[0][1:]
  244. i_start_list = peak_indices[0][:-1]
  245. corr_scalp, corr_brain = _get_per_pulse_correlation(bfi_estimate, i_end_list, i_start_list, rcbf_truth, rsbf_truth)
  246. return corr_scalp, corr_brain
  249. def segment_bfi(ground_truth):
  250. """Segment ground truth BFI into pulses.
  252. Only tested for ground truth simulation data with clean peaks. Will likely brake for real data.
  254. Args:
  255. ground_truth: array, ground truth BFI
  256. Returns:
  257. index_peaks: list, list of pulse peak indices
  258. """
  259. # smooth
  260. bfi_filt = gaussian_filter1d(ground_truth, sigma=10)
  261. index_peaks = find_peaks(-1 * bfi_filt, distance=50)
  262. return index_peaks
  265. def get_normalised_metrics(adb, brain_db, scalp_db, peak_indices=None):
  266. n_time = adb.shape[1]
  267. rcbf_truth = get_relative_cbf(brain_db)[:n_time]
  268. rsbf_truth = get_relative_cbf(scalp_db)[:n_time]
  269. _mask = None
  270. if np.any(adb[:, 0] == 0):
  271. _mask = adb[:, 0] == 0.0
  272. print("Warning - First time point of ADB is zero - this will cause division by zero")
  273. print(np.sum(adb == 0.0), np.sum(np.isnan(adb)))
  274. if adb.ndim == 3 and adb.shape[0] == 1 and adb.shape[-1] == 1:
  275. bfi_estimate = get_relative_cbf(adb[:, :, 0], cbf_baseline=adb[:, 0, :])
  276. elif adb.ndim == 2 and adb.shape[0] == 1:
  277. bfi_estimate = get_relative_cbf(adb[:, :], cbf_baseline=adb[:, 0])
  278. elif adb.ndim == 1:
  279. bfi_estimate = get_relative_cbf(np.squeeze(adb), cbf_baseline=np.squeeze(adb)[:, 0, None])
  280. else:
  281. bfi_estimate = get_relative_cbf(np.squeeze(adb), cbf_baseline=np.squeeze(adb)[:, 0, None])
  282. if bfi_estimate.ndim != 2:
  283. raise ValueError(f"bfi_estimate should be 2D, got {bfi_estimate.shape}")
  284. corr_per_pulse_scalp, corr_per_pulse_brain = get_per_pulse_correlation(
  285. bfi_estimate, rcbf_truth, rsbf_truth, peak_indices=peak_indices
  286. )
  287. rmse_norm_brain = compute_normalised_rmse(bfi_estimate, rcbf_truth)
  288. rmse_norm_scalp = compute_normalised_rmse(bfi_estimate, rsbf_truth)
  289. if _mask is not None:
  290. rmse_norm_brain[_mask] = np.nan
  291. rmse_norm_scalp[_mask] = np.nan
  292. corr_per_pulse_scalp[_mask] = np.nan
  293. corr_per_pulse_brain[_mask] = np.nan
  294. return corr_per_pulse_brain, corr_per_pulse_scalp, rmse_norm_brain, rmse_norm_scalp
  297. def get_td_metrics(bfi, brain_db, scalp_db, return_metric_obj=False):
  298. """Get time domain metrics. - Correlation with rCBF/rSBF and normalised RMSE with rCBF/rSBF
  300. Args:
  301. bfi: array, BFI estimate
  302. brain_db: array, ground truth brain DB
  303. scalp_db: array, ground truth scalp DB
  304. return_metric_obj: bool, if True return Metric object
  306. Returns:
  307. either metric object
  308. or..
  309. corr_per_pulse_brain: array, correlation with rCBF
  310. corr_per_pulse_scalp: array, correlation with rSBF
  311. rmse_norm_brain: array, normalised RMSE with rCBF
  312. rmse_norm_scalp: array, normalised RMSE with rSBF
  314. """
  315. (
  316. corr_per_pulse_brain,
  317. corr_per_pulse_scalp,
  318. rmse_norm_brain,
  319. rmse_norm_scalp,
  320. ) = get_normalised_metrics(bfi, brain_db, scalp_db, peak_indices=None)
  321. if return_metric_obj:
  322. return Metric(
  323. corr_per_pulse_brain=corr_per_pulse_brain,
  324. corr_per_pulse_scalp=corr_per_pulse_scalp,
  325. rmse_norm_brain=rmse_norm_brain,
  326. rmse_norm_scalp=rmse_norm_scalp,
  327. bfi=bfi,
  328. db_c=brain_db,
  329. db_s=scalp_db,
  330. )
  331. return corr_per_pulse_brain, corr_per_pulse_scalp, rmse_norm_brain, rmse_norm_scalp
  334. def get_cw_metrics(bfi_cw, brain_db, scalp_db, return_metric_obj=False):
  335. """Get continuous wave metrics. - Correlation with groundtruth rCBF/rSBF
  336. and normalised RMSE with rCBF/rSBF
  338. Args:
  339. bfi_cw: array, BFI estimate
  340. brain_db: array, ground truth brain DB
  341. scalp_db: array, ground truth scalp DB
  342. return_metric_obj: bool, if True return Metric object
  344. Returns:
  345. either metric object
  346. or..
  347. corr_per_pulse_brain_cw: float, correlation with rCBF
  348. corr_per_pulse_scalp_cw: float, correlation with rSBF
  349. rmse_norm_brain_cw: float, normalised RMSE with rCBF
  350. rmse_norm_scalp_cw: float, normalised RMSE with rSBF
  351. """
  352. (
  353. corr_per_pulse_brain_cw,
  354. corr_per_pulse_scalp_cw,
  355. rmse_norm_brain_cw,
  356. rmse_norm_scalp_cw,
  357. ) = get_normalised_metrics(np.tile(bfi_cw, (2, 1)), brain_db, scalp_db, peak_indices=None)
  358. if return_metric_obj:
  359. return Metric(
  360. corr_per_pulse_brain=corr_per_pulse_brain_cw,
  361. corr_per_pulse_scalp=corr_per_pulse_scalp_cw,
  362. rmse_norm_brain=rmse_norm_brain_cw,
  363. rmse_norm_scalp=rmse_norm_scalp_cw,
  364. bfi=bfi_cw,
  365. db_c=brain_db,
  366. db_s=scalp_db,
  367. )
  368. return (
  369. corr_per_pulse_brain_cw[0],
  370. corr_per_pulse_scalp_cw[0],
  371. rmse_norm_brain_cw[0],
  372. rmse_norm_scalp_cw[0],
  373. )

correlation_metrics.py at commit dc8dea1, under MIT · at the source

Overview

Authors: Dominic W. Hill1, Anurag Behera1, Octave Etard1, Yuqian Zhang1, Alexandra Tran-Van-Minh1, Alexander Ruesch1, Lior Ness1, Chloe Maine1, Amir Salehi Lashkajani1, Veronika Parfentyeva1, Navjit Singh1, Alexander Antrobus1, Rachel Prudden1, Pablo Villar Sanjurjo1, Stella Avtzi1, Tanja Dragojević1, Dawid Borycki1, Matthew T. Valley1, Robert J. Cooper1,2
ORCID iDs: Octave Etard, Alexandra Tran-Van-Minh, Pablo Villar Sanjurjo, Tanja Dragojević, Dawid Borycki, Matthew T. Valley
  1. CoMind Technologies, London, United Kingdom
  2. UCL, Department of Medical Physics and Biomedical Engineering, Biomedical Optics Research Laboratory, London, United Kingdom
Journal: Neurophotonics, volume 13, issue 2, article 025003
Dates: received 20 August 2025; accepted 3 December 2025; published online 30 March 2026; in print April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1117/1.nph.13.2.025003 · PMID 41930223 · PMCID PMC13041720 · OpenAlex W7143279539
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: computational modeling (no new data) (modality), none (in silico) (organism), computational (subfield)
Methods: Connectivity, Spectral & time-frequency, Graphs
Keywords: Monte Carlo simulation, cerebral blood flow, time-resolved, diffuse correlation spectroscopy, speckle contrast optical spectroscopy
Topic: Optical Imaging and Spectroscopy Techniques (Radiology, Nuclear Medicine and Imaging, Medicine), according to OpenAlex
Citations: not cited yet (Europe PMC); 87 references in the paper

Abstract

Significance: Achieving adequate brain sensitivity remains a significant obstacle to noninvasive optical measurements of pulsatile cerebral blood flow. Increasing source-detector separation (SDS), utilizing time-of-flight (ToF) information, and/or increasing acquisition frequency can increase brain sensitivity. However, optimizing these parameters is nontrivial and must balance the need to achieve sufficient signal-to-noise ratio.

Aim: We aim to guide hardware optimization by evaluating the benefits of ToF gating, autocorrelation time-lag gating, and increased source-detector separation in recovering pulsatile cerebral blood flow.

Approach: Monte Carlo simulations of ToF-resolved pulsatile blood flow index at 1064 nm were performed. A simulation and analysis pipeline is presented and validated against phantom and in vivo measurements.

Results: Brain sensitivity deteriorates for autocorrelation time-lags >1 to 10 μs. Continuous-wave diffuse correlation spectroscopy (CW-DCS) at 40 mm SDS achieves equivalent brain sensitivity to time-resolved measurements with a 1.2 ns ToF gate. For median adult brain depths, a 31 mm SDS (CW-DCS), 35 mm SDS (speckle contrast optical spectroscopy), or 0.9 ns ToF are needed for brain-dominated signals. For the 85th percentile of brain depth, these thresholds increase to 40 mm, 46 mm, and 1.2 ns, respectively.

Conclusions: We quantitatively compare source-detector separation, ToF gating, and acquisition frequency and identify crucial performance requirements for clinically viable optical monitors of cerebral blood flow.

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

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CoMind-Technologies/mc_analysis

License: MIT
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: dc8dea1d08ad874d9df7ba8a5a42baa39ec1afa6, 25 March 2026
Languages: Python (28)
Size: 49 files, 28 scripts
Software Heritage: not archived
Found in: “Code and Data Availability”
Holds: README, license file, environment (pyproject.toml), tests
Not found: CITATION.cff, continuous integration, documentation
Tools: NumPy (18 files), SciPy (7 files), pandas (6 files), Matplotlib (5 files), h5py (2 files)
Availability: 1 check, the latest on 30 September 2026: the link answers
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30 files
At the source: github.com/CoMind-Technologies/mc_analysis

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Code and Data Availability

Code used to produce pulsatile simulations and a subset of figures presented in this paper is available at: https://github.com/CoMind-Technologies/mc_analysis. Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.

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

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Hill, D. W., Behera, A., Etard, O., Zhang, Y., Tran-Van-Minh, A., Ruesch, A., Ness, L., Maine, C., Salehi Lashkajani, A., Parfentyeva, V., Singh, N., Antrobus, A., Prudden, R., Sanjurjo, P. V., Avtzi, S., Dragojević, T., Borycki, D., Valley, M. T., & Cooper, R. J. (2026). Monte Carlo simulations of time-resolved blood flow index: times-of-flight beyond ∼1 ns are necessary for brain-dominated measurements. Neurophotonics, 13(2), 025003. https://doi.org/10.1117/1.nph.13.2.025003

BibTeX

@article{hill2026monte,
author = {Hill, Dominic W. and Behera, Anurag and Etard, Octave and Zhang, Yuqian and Tran-Van-Minh, Alexandra and Ruesch, Alexander and Ness, Lior and Maine, Chloe and Salehi Lashkajani, Amir and Parfentyeva, Veronika and Singh, Navjit and Antrobus, Alexander and Prudden, Rachel and Sanjurjo, Pablo Villar and Avtzi, Stella and Dragojević, Tanja and Borycki, Dawid and Valley, Matthew T. and Cooper, Robert J.},
title = {{Monte Carlo simulations of time-resolved blood flow index: times-of-flight beyond ∼1 ns are necessary for brain-dominated measurements}},
journal = {Neurophotonics},
year = {2026},
month = mar,
volume = {13},
number = {2},
pages = {025003},
publisher = {Society of Photo-Optical Instrumentation Engineers},
issn = {2329-423X},
doi = {10.1117/1.nph.13.2.025003},
url = {https://doi.org/10.1117/1.nph.13.2.025003},
pmid = {41930223},
pmcid = {PMC13041720}
}

RIS

TY - JOUR
AU - Hill, Dominic W.
AU - Behera, Anurag
AU - Etard, Octave
AU - Zhang, Yuqian
AU - Tran-Van-Minh, Alexandra
AU - Ruesch, Alexander
AU - Ness, Lior
AU - Maine, Chloe
AU - Salehi Lashkajani, Amir
AU - Parfentyeva, Veronika
AU - Singh, Navjit
AU - Antrobus, Alexander
AU - Prudden, Rachel
AU - Sanjurjo, Pablo Villar
AU - Avtzi, Stella
AU - Dragojević, Tanja
AU - Borycki, Dawid
AU - Valley, Matthew T.
AU - Cooper, Robert J.
TI - Monte Carlo simulations of time-resolved blood flow index: times-of-flight beyond ∼1 ns are necessary for brain-dominated measurements
T2 - Neurophotonics
J2 - Neurophotonics
PY - 2026
DA - 2026/03/30
VL - 13
IS - 2
SP - 025003
SN - 2329-423X
PB - Society of Photo-Optical Instrumentation Engineers
DO - 10.1117/1.nph.13.2.025003
UR - https://doi.org/10.1117/1.nph.13.2.025003
LA - en
ER -

CSL-JSON

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"author": [
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{
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{
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{
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{
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},
{
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{
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"container-title-short": "Neurophotonics",
"volume": "13",
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"DOI": "10.1117/1.nph.13.2.025003",
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"issued": {
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2026,
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}

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