116 lines
3.9 KiB
Python
116 lines
3.9 KiB
Python
# Authors: The MNE-Python contributors.
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# License: BSD-3-Clause
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# Copyright the MNE-Python contributors.
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import os.path as op
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import numpy as np
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from scipy.interpolate import interp1d
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from scipy.io import loadmat
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from ..._fiff.constants import FIFF
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from ...io import BaseRaw
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from ...utils import _validate_type, pinv, warn
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from ..nirs import _validate_nirs_info, source_detector_distances
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def beer_lambert_law(raw, ppf=6.0):
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r"""Convert NIRS optical density data to haemoglobin concentration.
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Parameters
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----------
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raw : instance of Raw
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The optical density data.
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ppf : tuple | float
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The partial pathlength factors for each wavelength.
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.. versionchanged:: 1.7
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Support for different factors for the two wavelengths.
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Returns
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-------
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raw : instance of Raw
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The modified raw instance.
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"""
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raw = raw.copy().load_data()
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_validate_type(raw, BaseRaw, "raw")
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_validate_type(ppf, ("numeric", "array-like"), "ppf")
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ppf = np.array(ppf, float)
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if ppf.ndim == 0: # upcast single float to shape (2,)
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ppf = np.array([ppf, ppf])
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if ppf.shape != (2,):
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raise ValueError(
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f"ppf must be float or array-like of shape (2,), got shape {ppf.shape}"
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)
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ppf = ppf[:, np.newaxis] # shape (2, 1)
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picks = _validate_nirs_info(raw.info, fnirs="od", which="Beer-lambert")
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# This is the one place we *really* need the actual/accurate frequencies
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freqs = np.array([raw.info["chs"][pick]["loc"][9] for pick in picks], float)
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abs_coef = _load_absorption(freqs)
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distances = source_detector_distances(raw.info, picks="all")
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bad = ~np.isfinite(distances[picks])
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bad |= distances[picks] <= 0
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if bad.any():
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warn(
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"Source-detector distances are zero on NaN, some resulting "
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"concentrations will be zero. Consider setting a montage "
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"with raw.set_montage."
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)
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distances[picks[bad]] = 0.0
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if (distances[picks] > 0.1).any():
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warn(
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"Source-detector distances are greater than 10 cm. "
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"Large distances will result in invalid data, and are "
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"likely due to optode locations being stored in a "
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" unit other than meters."
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)
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rename = dict()
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for ii, jj in zip(picks[::2], picks[1::2]):
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EL = abs_coef * distances[ii] * ppf
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iEL = pinv(EL)
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raw._data[[ii, jj]] = iEL @ raw._data[[ii, jj]] * 1e-3
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# Update channel information
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coil_dict = dict(hbo=FIFF.FIFFV_COIL_FNIRS_HBO, hbr=FIFF.FIFFV_COIL_FNIRS_HBR)
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for ki, kind in zip((ii, jj), ("hbo", "hbr")):
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ch = raw.info["chs"][ki]
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ch.update(coil_type=coil_dict[kind], unit=FIFF.FIFF_UNIT_MOL)
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new_name = f'{ch["ch_name"].split(" ")[0]} {kind}'
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rename[ch["ch_name"]] = new_name
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raw.rename_channels(rename)
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# Validate the format of data after transformation is valid
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_validate_nirs_info(raw.info, fnirs="hb")
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return raw
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def _load_absorption(freqs):
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"""Load molar extinction coefficients."""
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# Data from https://omlc.org/spectra/hemoglobin/summary.html
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# The text was copied to a text file. The text before and
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# after the table was deleted. The the following was run in
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# matlab
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# extinct_coef=importdata('extinction_coef.txt')
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# save('extinction_coef.mat', 'extinct_coef')
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#
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# Returns data as [[HbO2(freq1), Hb(freq1)],
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# [HbO2(freq2), Hb(freq2)]]
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extinction_fname = op.join(
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op.dirname(__file__), "..", "..", "data", "extinction_coef.mat"
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)
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a = loadmat(extinction_fname)["extinct_coef"]
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interp_hbo = interp1d(a[:, 0], a[:, 1], kind="linear")
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interp_hb = interp1d(a[:, 0], a[:, 2], kind="linear")
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ext_coef = np.array(
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[
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[interp_hbo(freqs[0]), interp_hb(freqs[0])],
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[interp_hbo(freqs[1]), interp_hb(freqs[1])],
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]
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)
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abs_coef = ext_coef * 0.2303
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return abs_coef
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