Image Reconstruction Options

This notebook is a test bed to run and check all implemented image reconstruction methods.

[1]:

# This cells setups the environment when executed in Google Colab.
try:
    import google.colab
    !curl -s https://raw.githubusercontent.com/ibs-lab/cedalion/dev/scripts/colab_setup.py -o colab_setup.py
    # Select branch with --branch "branch name" (default is "dev")
    %run colab_setup.py
except ImportError:
    pass

Notebook configuration

Decide for an example dataset with a sparse probe or a high-density probe for DOT. The notebook will load example data accordingly.

Also specify, if precomputed results of the photon propagation should be used and if the 3D visualizations should be interactive.

[2]:

DATASET = "fingertappingDOT" # high-density montage
HEAD_MODEL = "colin27"
FORWARD_MODEL = "MCX" # photon monte carlo
PRECOMPUTED_FLUENCE = True

# set this flag to True to enable interactive 3D plots
INTERACTIVE_PLOTS = False

[3]:

import pyvista as pv

if INTERACTIVE_PLOTS:
    pv.set_jupyter_backend("server")
else:
    pv.set_jupyter_backend("static")

import itertools
import traceback
from pathlib import Path
from tempfile import TemporaryDirectory

import xarray as xr
from IPython.display import Image

import cedalion

import cedalion.dot as dot
import cedalion.sigproc.motion as motion
import cedalion.xrutils as xrutils
from cedalion import units
from cedalion.io.forward_model import load_Adot
from cedalion.sigproc.quality import measurement_variance
from cedalion.vis.anatomy import image_recon_multi_view
from tqdm import tqdm
import pickle
import numpy as np

# fail on unit errors
xrutils.unit_stripping_is_error()

xr.set_options(display_expand_data=False);

[4]:

# helper function to display gifs in rendered notbooks
def display_image(fname : str):
    display(Image(data=open(fname,'rb').read(), format='png'))

Working Directory

In this notebook the output of the fluence and sensitivity calculations are stored in a temporary directory. This will be deleted when the notebook ends.

[5]:

#temporary_directory = TemporaryDirectory()
#tmp_dir_path = Path(temporary_directory.name)
temporary_directory = TemporaryDirectory()
tmp_dir_path = Path(temporary_directory.name)

Load a finger-tapping dataset

[6]:

rec = cedalion.data.get_fingertappingDOT()

[7]:

geo3d_meas = rec.geo3d
display(geo3d_meas)

<xarray.DataArray (label: 346, digitized: 3)> Size: 8kB
[mm] -77.82 15.68 23.17 -61.91 21.23 56.49 ... 14.23 -38.28 81.95 -0.678 -37.03
Coordinates:
    type     (label) object 3kB PointType.SOURCE ... PointType.LANDMARK
  * label    (label) <U6 8kB 'S1' 'S2' 'S3' 'S4' ... 'FFT10h' 'FT10h' 'FTT10h'
Dimensions without coordinates: digitized

The measurement list is a pandas.DataFrame that describes which source-detector pairs form channels.

[8]:

meas_list = rec._measurement_lists["amp"]
display(meas_list.head(5))
sourceIndex detectorIndex wavelengthIndex wavelengthActual wavelengthEmissionActual dataType dataUnit dataTypeLabel dataTypeIndex sourcePower detectorGain moduleIndex sourceModuleIndex detectorModuleIndex channel source detector wavelength chromo
0 1 1 1 None None 1 None raw-DC 1 None None None None None S1D1 S1 D1 760.0 None
1 1 2 1 None None 1 None raw-DC 1 None None None None None S1D2 S1 D2 760.0 None
2 1 4 1 None None 1 None raw-DC 1 None None None None None S1D4 S1 D4 760.0 None
3 1 5 1 None None 1 None raw-DC 1 None None None None None S1D5 S1 D5 760.0 None
4 1 6 1 None None 1 None raw-DC 1 None None None None None S1D6 S1 D6 760.0 None

Event/stimulus information is also stored in a pandas.DataFrame.

[9]:

rec.stim

[9]:
onset duration value trial_type
0 23.855104 10.0 1.0 1
1 54.132736 10.0 1.0 1
2 84.410368 10.0 1.0 1
3 114.688000 10.0 1.0 1
4 146.112512 10.0 1.0 1
... ... ... ... ...
125 1431.535616 10.0 1.0 5
126 1526.038528 10.0 1.0 5
127 1650.819072 10.0 1.0 5
128 1805.418496 10.0 1.0 5
129 1931.116544 10.0 1.0 5

130 rows × 4 columns

For clarity, events are given more descriptive names:

[10]:

rec.stim.cd.rename_events(
    {
        "1": "Control",
        "2": "FTapping/Left",
        "3": "FTapping/Right",
        "4": "BallSqueezing/Left",
        "5": "BallSqueezing/Right",
    }
)

# count number of trials per trial_type
display(
    rec.stim.groupby("trial_type")[["onset"]]
    .count()
    .rename({"onset": "#trials"}, axis=1)
)
#trials
trial_type
BallSqueezing/Left 17
BallSqueezing/Right 16
Control 65
FTapping/Left 16
FTapping/Right 16

Preprocessing

Perform motion correction, conversion to optical density and bandpass filtering.

[11]:

rec["od"] = cedalion.nirs.cw.int2od(rec["amp"])
rec["od_tddr"] = motion.tddr(rec["od"])
rec["od_wavelet"] = motion.wavelet(rec["od_tddr"])

# bandpass filter the data
rec["od_freqfiltered"] = rec["od_wavelet"].cd.freq_filter(
    fmin=0.01, fmax=0.5, butter_order=4
)

Calculate block averages in optical density

[12]:

# segment data into epochs
epochs = rec["od_freqfiltered"].cd.to_epochs(
    rec.stim,  # stimulus dataframe
    ["FTapping/Left", "FTapping/Right"],  # select fingertapping events, discard others
    before=5 * units.s,  # seconds before stimulus
    after=30 * units.s,  # seconds after stimulus
)

# calculate baseline
baseline = epochs.sel(reltime=(epochs.reltime < 0)).mean("reltime")

# subtract baseline
epochs_blcorrected = epochs - baseline

# group trials by trial_type. For each group individually average the epoch dimension
blockaverage = epochs_blcorrected.groupby("trial_type").mean("epoch")

The TwoSurfaceHeadModel

Use the get_standard_headmodel function to construct a TSHM

[13]:

head = dot.get_standard_headmodel(HEAD_MODEL)

[14]:

head

[14]:

TwoSurfaceHeadModel(
  crs: ijk
  tissue_types: csf, gm, scalp, skull, wm
  brain faces: 49992 vertices: 25000 units: dimensionless
  scalp faces: 20096 vertices: 10050 units: dimensionless
  landmarks: 73
)

[15]:

head.landmarks

[15]:

<xarray.DataArray (label: 73, ijk: 3)> Size: 2kB
[] 90.0 7.435 48.92 3.924 106.0 24.01 ... 127.4 25.63 109.0 139.8 26.66 93.47
Coordinates:
  * label    (label) <U3 876B 'Iz' 'LPA' 'RPA' 'Nz' ... 'PO1' 'PO2' 'PO4' 'PO6'
    type     (label) object 584B PointType.LANDMARK ... PointType.LANDMARK
Dimensions without coordinates: ijk

Changing between coordinate systems:

[16]:

head_ras = head.apply_transform(head.t_ijk2ras)
head_ras

[16]:

TwoSurfaceHeadModel(
  crs: mni
  tissue_types: csf, gm, scalp, skull, wm
  brain faces: 49992 vertices: 25000 units: millimeter
  scalp faces: 20096 vertices: 10050 units: millimeter
  landmarks: 73
)

Optode Registration

The optode coordinates from the recording must be aligned with the scalp surface. Currently, cedaĺion offers a simple registration method, which finds an affine transformation (scaling, rotating, translating) that matches the landmark positions of the head model and their digitized counter parts. Afterwards, optodes are snapped to the nearest vertex on the scalp.

[17]:

geo3d_snapped_ijk = head.align_and_snap_to_scalp(geo3d_meas)
display(geo3d_snapped_ijk)

<xarray.DataArray (label: 346, ijk: 3)> Size: 8kB
[] 15.1 140.9 100.2 30.75 144.6 130.6 ... 172.5 137.6 42.28 172.7 126.0 43.07
Coordinates:
    type     (label) object 3kB PointType.SOURCE ... PointType.LANDMARK
  * label    (label) <U6 8kB 'S1' 'S2' 'S3' 'S4' ... 'FFT10h' 'FT10h' 'FTT10h'
Dimensions without coordinates: ijk

Light propagation in tissue

  • construct forward model

  • load Adot via cedalion.data

  • logic for running MCX/Nirfaster is kept in the notebook to facilitate adaption to other heads

[18]:

fwm = dot.ForwardModel(head, geo3d_snapped_ijk, meas_list)

[19]:

if PRECOMPUTED_FLUENCE:
    if FORWARD_MODEL == "MCX":
        fluence_fname = cedalion.data.get_precomputed_fluence(DATASET, HEAD_MODEL)
    elif FORWARD_MODEL == "NIRFASTER":
        raise NotImplementedError(
            "Currently there are no precomputed NIRFASTER results available"
        )
else:
    fluence_fname = tmp_dir_path / "fluence.h5"

    if FORWARD_MODEL == "MCX":
        fwm.compute_fluence_mcx(fluence_fname)
    elif FORWARD_MODEL == "NIRFASTER":
        fwm.compute_fluence_nirfaster(fluence_fname)

Calculate the sensitivity matrices

The forward model’s function compute_sensitivity calculates the sensitivity matrix from the fluence file and saves the result in a new file.

[20]:

if PRECOMPUTED_FLUENCE:
    Adot = cedalion.data.get_precomputed_sensitivity(DATASET, HEAD_MODEL)
else:
    sensitivity_fname = tmp_dir_path / "sensitivity.h5"
    fwm.compute_sensitivity(fluence_fname, sensitivity_fname)
    Adot = load_Adot(sensitivity_fname)

The sensitivity matrix describes how an absorption change at a given surface vertex changes the optical density in a given channel and wavelength.

The coordinate is_brain holds a mask to distinguish brain and scalp voxels.

[21]:

# load and display sensitivity matrix
display(Adot)

<xarray.DataArray (channel: 100, vertex: 35050, wavelength: 2)> Size: 28MB
3.656e-17 3.656e-17 1.758e-18 1.758e-18 ... 8.413e-17 2.986e-15 2.986e-15
Coordinates:
    parcel      (vertex) object 280kB 'VisCent_ExStr_8_LH' ... 'scalp'
    is_brain    (vertex) bool 35kB True True True True ... False False False
  * channel     (channel) object 800B 'S1D1' 'S1D2' 'S1D4' ... 'S14D31' 'S14D32'
    source      (channel) object 800B 'S1' 'S1' 'S1' 'S1' ... 'S14' 'S14' 'S14'
    detector    (channel) object 800B 'D1' 'D2' 'D4' 'D5' ... 'D29' 'D31' 'D32'
  * wavelength  (wavelength) float64 16B 760.0 850.0
Dimensions without coordinates: vertex
Attributes:
    units:    mm

Reconstruct the Image

Gaussian Spatial Basis Functions

After constructing the SBFs once, the prepared GaussianSpatialBasisFunctions object can be stored in an hdf5 file. If the file already exists, load it.

[22]:

# previous SFB implementation. uncomment for testing
#fname = tmp_dir_path / "sbf.h5"
#
#if fname.exists():
#    sbf = dot.OriginalGaussianSpatialBasisFunctions.from_file(fname)
#else:
#    sbf = dot.OriginalGaussianSpatialBasisFunctions(
#        head_ras,
#        Adot,
#        mask_threshold=-2,
#        threshold_brain=1 * units.mm,
#        threshold_scalp=5 * units.mm,
#        sigma_brain=1 * units.mm,
#        sigma_scalp=5 * units.mm,
#    )
#    sbf.to_file(fname)

# explicit parameters
sbf = dot.GaussianSpatialBasisFunctions(
    head_ras,
    Adot,
    mask_threshold=-2,
    threshold_brain=1 * units.mm,
    threshold_scalp=5 * units.mm,
    sigma_brain=1 * units.mm,
    sigma_scalp=5 * units.mm,
)
# alternatively: using predefined parameter sets
sbf = dot.GaussianSpatialBasisFunctions(head_ras, Adot, **dot.SBF_GAUSSIANS_DENSE)

100%|██████████| 4161/4161 [00:00<00:00, 5184.21it/s]
100%|██████████| 1973/1973 [00:00<00:00, 25185.61it/s]
100%|██████████| 3952/3952 [00:05<00:00, 674.19it/s]
100%|██████████| 576/576 [00:00<00:00, 1583.08it/s]
100%|██████████| 4161/4161 [00:00<00:00, 5192.90it/s]
100%|██████████| 1973/1973 [00:00<00:00, 26152.51it/s]
100%|██████████| 3952/3952 [00:06<00:00, 599.34it/s]
100%|██████████| 576/576 [00:00<00:00, 1598.95it/s]

Test default parameter sets

[23]:

c_meas = measurement_variance(rec["od"].pint.dequantify(), calc_covariance=False)

recon = dot.ImageRecon(
    Adot,
    recon_mode="mua2conc",
    spatial_basis_functions=sbf,
    **dot.REG_TIKHONOV_ONLY,
)

recon_result = recon.reconstruct(blockaverage, c_meas)
recon.get_image_noise_posterior()

recon = dot.ImageRecon(
    Adot,
    recon_mode="mua2conc",
    spatial_basis_functions=sbf,
    **dot.REG_PAPER_MUA_SBF
)

recon_result = recon.reconstruct(blockaverage, c_meas)

Create combinations of ImageRecon parameters

[24]:

timeseries = [
    # ('channel', 'wavelength', 'time')
    blockaverage.isel(reltime=[40], trial_type=0).rename({"reltime" : "time"}),
    # ('channel', 'wavelength')
    blockaverage.isel(reltime=40, trial_type=0),
    # ('trial_type', 'channel', 'wavelength', 'reltime')
    blockaverage.isel(reltime=[40], trial_type=[0]),
]

c_meas = measurement_variance(rec["od"].pint.dequantify(), calc_covariance=False)

c_meas_with_time = xr.concat([c_meas, c_meas, c_meas], dim="time").assign_coords(
    {"time": [0.0, 1.0, 2.0], "sample": ("time", [0, 1, 2])}
)

sbfs = [sbf, None]
recon_modes = ["mua", "conc", "mua2conc"]
brainonly_modes = [False, True]
alpha_meas_params = [0.01]
alpha_spatial_params = [None, 0.01]
c_meas_params = [None, c_meas, c_meas_with_time]
lambda_R_conc_params = [None, 1e-6]

combinations = list(
    itertools.product(
        timeseries,
        sbfs,
        recon_modes,
        brainonly_modes,
        alpha_meas_params,
        alpha_spatial_params,
        c_meas_params,
        lambda_R_conc_params,
    )
)
print(f"generated {len(combinations)} combinations")

generated 432 combinations

[25]:

def describe_combination(i):
    ts, sbf_, recon_mode, brainonly_mode, alpha_meas, alpha_spatial, c_meas_, lambda_R_conc = combinations[i]

    print(
        f"{i}/{len(combinations)}: {' x '.join(ts.dims)} sbf_={sbf_ is not None} "
        f"{recon_mode=} {brainonly_mode=} {alpha_meas=} {alpha_spatial=} "
        f"c_meas_={c_meas_ is not None}"
    )


def run_comb(i, catch, silent=False):
    ts, sbf_, recon_mode, brainonly_mode, alpha_meas, alpha_spatial, c_meas_, lambda_R_conc = combinations[i]

    def msg(m):
        if not silent:
            print(m)

    msg(
        f"{i}/{len(combinations)}: {' x '.join(ts.dims)} sbf_={sbf_ is not None} "
        f"{recon_mode=} {brainonly_mode=} {alpha_meas=} {alpha_spatial=} "
        f"c_meas_={c_meas_ is not None}"
    )

    # construct ImageRecon object
    recon = dot.ImageRecon(
        Adot,
        recon_mode=recon_mode,
        brain_only=brainonly_mode,
        alpha_meas=alpha_meas,
        alpha_spatial=alpha_spatial,
        apply_c_meas=(c_meas_ is not None),
        spatial_basis_functions=sbf_,
        lambda_R_conc=lambda_R_conc,

    )

    recon_result = None
    noise = None

    if catch:
        # run reconstruction, catch any exception
        try:
            recon_result = recon.reconstruct(ts, c_meas=c_meas_)

            if c_meas_ is not None:
                noise = recon.get_image_noise(c_meas_)

            msg(f"recon_result dims:{recon_result.dims} shape: {recon_result.shape}")
        except Exception:
            msg("Error")
            msg(traceback.format_exc())
            return None
        else:
            msg("OK")
    else:
        # run reconstruction, don't catch any exception. jump into debugger
        recon_result = recon.reconstruct(ts, c_meas=c_meas_)
        if c_meas_ is not None:
            noise = recon.get_image_noise(c_meas_)

        msg(f"recon_result dims:{recon_result.dims} shape: {recon_result.shape}")

    return (recon_result, noise)

Run a single combination of recon settings. Entry point for debugging

[ ]:

Run all combinations. Catch and count combinations that throw errors.

[26]:

def load_previous(comb):
    result_path: Path = tmp_dir_path / f"run_com_{comb:06d}.pickle"
    with open(result_path, "rb") as fin:
        return pickle.load(fin)



def cmp(a, b):
    if (a is None) and (b is None):
        return True
    if (a is not None) and (b is not None) and (np.abs(a - b).pint.dequantify() < 1e-4).all():
        return True
    else:
        return False


[27]:

error_free = 0
img_unchanged = 0
noise_unchanged = 0
errors = []
differences = []

for i in (pbar := tqdm(range(len(combinations)))):
    result_path: Path = tmp_dir_path / f"run_com_{i:06d}.pickle"
    if (result := run_comb(i, catch=True, silent=True)) is not None:
        error_free += 1

        if result_path.exists():
            with open(result_path, "rb") as fin:
                previous_img, previous_noise = pickle.load(fin)

                if cmp(result[0], previous_img):
                    img_unchanged += 1
                else:
                    differences.append(i)
                if cmp(result[1], previous_noise):
                    noise_unchanged += 1
                else:
                    differences.append(i)

        else:
            with open(result_path, "wb") as fout:
                pickle.dump(result, fout)

    else:
        errors.append(i)
    pbar.set_description(f"OK={error_free}/{i + 1} IUC={img_unchanged} NUC={noise_unchanged}")
    # print(80*"-")

print("=" * 80)
print(f"OK={error_free} / {len(combinations)}")
print("=" * 80)

OK=378/432 IUC=0 NUC=0: 100%|██████████| 432/432 [02:55<00:00,  2.46it/s]

================================================================================
OK=378 / 432
================================================================================



[28]:

sorted(set(differences))

[28]:

[]

[29]:

for i in differences:
    describe_combination(i)

for i in [j for j in np.arange(len(combinations)) if j not in differences]:
    describe_combination(i)

0/432: channel x wavelength x time sbf_=True recon_mode='mua' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=False
1/432: channel x wavelength x time sbf_=True recon_mode='mua' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=False
2/432: channel x wavelength x time sbf_=True recon_mode='mua' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=True
3/432: channel x wavelength x time sbf_=True recon_mode='mua' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=True
4/432: channel x wavelength x time sbf_=True recon_mode='mua' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=True
5/432: channel x wavelength x time sbf_=True recon_mode='mua' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=True
6/432: channel x wavelength x time sbf_=True recon_mode='mua' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
7/432: channel x wavelength x time sbf_=True recon_mode='mua' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
8/432: channel x wavelength x time sbf_=True recon_mode='mua' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
9/432: channel x wavelength x time sbf_=True recon_mode='mua' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
10/432: channel x wavelength x time sbf_=True recon_mode='mua' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
11/432: channel x wavelength x time sbf_=True recon_mode='mua' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
12/432: channel x wavelength x time sbf_=True recon_mode='mua' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=False
13/432: channel x wavelength x time sbf_=True recon_mode='mua' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=False
14/432: channel x wavelength x time sbf_=True recon_mode='mua' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=True
15/432: channel x wavelength x time sbf_=True recon_mode='mua' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=True
16/432: channel x wavelength x time sbf_=True recon_mode='mua' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=True
17/432: channel x wavelength x time sbf_=True recon_mode='mua' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=True
18/432: channel x wavelength x time sbf_=True recon_mode='mua' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
19/432: channel x wavelength x time sbf_=True recon_mode='mua' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
20/432: channel x wavelength x time sbf_=True recon_mode='mua' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
21/432: channel x wavelength x time sbf_=True recon_mode='mua' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
22/432: channel x wavelength x time sbf_=True recon_mode='mua' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
23/432: channel x wavelength x time sbf_=True recon_mode='mua' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
24/432: channel x wavelength x time sbf_=True recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=False
25/432: channel x wavelength x time sbf_=True recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=False
26/432: channel x wavelength x time sbf_=True recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=True
27/432: channel x wavelength x time sbf_=True recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=True
28/432: channel x wavelength x time sbf_=True recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=True
29/432: channel x wavelength x time sbf_=True recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=True
30/432: channel x wavelength x time sbf_=True recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
31/432: channel x wavelength x time sbf_=True recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
32/432: channel x wavelength x time sbf_=True recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
33/432: channel x wavelength x time sbf_=True recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
34/432: channel x wavelength x time sbf_=True recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
35/432: channel x wavelength x time sbf_=True recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
36/432: channel x wavelength x time sbf_=True recon_mode='conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=False
37/432: channel x wavelength x time sbf_=True recon_mode='conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=False
38/432: channel x wavelength x time sbf_=True recon_mode='conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=True
39/432: channel x wavelength x time sbf_=True recon_mode='conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=True
40/432: channel x wavelength x time sbf_=True recon_mode='conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=True
41/432: channel x wavelength x time sbf_=True recon_mode='conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=True
42/432: channel x wavelength x time sbf_=True recon_mode='conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
43/432: channel x wavelength x time sbf_=True recon_mode='conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
44/432: channel x wavelength x time sbf_=True recon_mode='conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
45/432: channel x wavelength x time sbf_=True recon_mode='conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
46/432: channel x wavelength x time sbf_=True recon_mode='conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
47/432: channel x wavelength x time sbf_=True recon_mode='conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
48/432: channel x wavelength x time sbf_=True recon_mode='mua2conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=False
49/432: channel x wavelength x time sbf_=True recon_mode='mua2conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=False
50/432: channel x wavelength x time sbf_=True recon_mode='mua2conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=True
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54/432: channel x wavelength x time sbf_=True recon_mode='mua2conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
55/432: channel x wavelength x time sbf_=True recon_mode='mua2conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
56/432: channel x wavelength x time sbf_=True recon_mode='mua2conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
57/432: channel x wavelength x time sbf_=True recon_mode='mua2conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
58/432: channel x wavelength x time sbf_=True recon_mode='mua2conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
59/432: channel x wavelength x time sbf_=True recon_mode='mua2conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
60/432: channel x wavelength x time sbf_=True recon_mode='mua2conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=False
61/432: channel x wavelength x time sbf_=True recon_mode='mua2conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=False
62/432: channel x wavelength x time sbf_=True recon_mode='mua2conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=True
63/432: channel x wavelength x time sbf_=True recon_mode='mua2conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=True
64/432: channel x wavelength x time sbf_=True recon_mode='mua2conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=True
65/432: channel x wavelength x time sbf_=True recon_mode='mua2conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=True
66/432: channel x wavelength x time sbf_=True recon_mode='mua2conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
67/432: channel x wavelength x time sbf_=True recon_mode='mua2conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
68/432: channel x wavelength x time sbf_=True recon_mode='mua2conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
69/432: channel x wavelength x time sbf_=True recon_mode='mua2conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
70/432: channel x wavelength x time sbf_=True recon_mode='mua2conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
71/432: channel x wavelength x time sbf_=True recon_mode='mua2conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
72/432: channel x wavelength x time sbf_=False recon_mode='mua' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=False
73/432: channel x wavelength x time sbf_=False recon_mode='mua' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=False
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78/432: channel x wavelength x time sbf_=False recon_mode='mua' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
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80/432: channel x wavelength x time sbf_=False recon_mode='mua' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
81/432: channel x wavelength x time sbf_=False recon_mode='mua' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
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90/432: channel x wavelength x time sbf_=False recon_mode='mua' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
91/432: channel x wavelength x time sbf_=False recon_mode='mua' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
92/432: channel x wavelength x time sbf_=False recon_mode='mua' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
93/432: channel x wavelength x time sbf_=False recon_mode='mua' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
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96/432: channel x wavelength x time sbf_=False recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=False
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102/432: channel x wavelength x time sbf_=False recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
103/432: channel x wavelength x time sbf_=False recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
104/432: channel x wavelength x time sbf_=False recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
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114/432: channel x wavelength x time sbf_=False recon_mode='conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
115/432: channel x wavelength x time sbf_=False recon_mode='conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
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120/432: channel x wavelength x time sbf_=False recon_mode='mua2conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=False
121/432: channel x wavelength x time sbf_=False recon_mode='mua2conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=False
122/432: channel x wavelength x time sbf_=False recon_mode='mua2conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=True
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126/432: channel x wavelength x time sbf_=False recon_mode='mua2conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
127/432: channel x wavelength x time sbf_=False recon_mode='mua2conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
128/432: channel x wavelength x time sbf_=False recon_mode='mua2conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
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130/432: channel x wavelength x time sbf_=False recon_mode='mua2conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
131/432: channel x wavelength x time sbf_=False recon_mode='mua2conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
132/432: channel x wavelength x time sbf_=False recon_mode='mua2conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=False
133/432: channel x wavelength x time sbf_=False recon_mode='mua2conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=False
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138/432: channel x wavelength x time sbf_=False recon_mode='mua2conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
139/432: channel x wavelength x time sbf_=False recon_mode='mua2conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
140/432: channel x wavelength x time sbf_=False recon_mode='mua2conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
141/432: channel x wavelength x time sbf_=False recon_mode='mua2conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
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143/432: channel x wavelength x time sbf_=False recon_mode='mua2conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
144/432: channel x wavelength sbf_=True recon_mode='mua' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=False
145/432: channel x wavelength sbf_=True recon_mode='mua' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=False
146/432: channel x wavelength sbf_=True recon_mode='mua' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=True
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150/432: channel x wavelength sbf_=True recon_mode='mua' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
151/432: channel x wavelength sbf_=True recon_mode='mua' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
152/432: channel x wavelength sbf_=True recon_mode='mua' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
153/432: channel x wavelength sbf_=True recon_mode='mua' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
154/432: channel x wavelength sbf_=True recon_mode='mua' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
155/432: channel x wavelength sbf_=True recon_mode='mua' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
156/432: channel x wavelength sbf_=True recon_mode='mua' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=False
157/432: channel x wavelength sbf_=True recon_mode='mua' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=False
158/432: channel x wavelength sbf_=True recon_mode='mua' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=True
159/432: channel x wavelength sbf_=True recon_mode='mua' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=True
160/432: channel x wavelength sbf_=True recon_mode='mua' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=True
161/432: channel x wavelength sbf_=True recon_mode='mua' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=True
162/432: channel x wavelength sbf_=True recon_mode='mua' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
163/432: channel x wavelength sbf_=True recon_mode='mua' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
164/432: channel x wavelength sbf_=True recon_mode='mua' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
165/432: channel x wavelength sbf_=True recon_mode='mua' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
166/432: channel x wavelength sbf_=True recon_mode='mua' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
167/432: channel x wavelength sbf_=True recon_mode='mua' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
168/432: channel x wavelength sbf_=True recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=False
169/432: channel x wavelength sbf_=True recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=False
170/432: channel x wavelength sbf_=True recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=True
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180/432: channel x wavelength sbf_=True recon_mode='conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=False
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192/432: channel x wavelength sbf_=True recon_mode='mua2conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=False
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205/432: channel x wavelength sbf_=True recon_mode='mua2conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=False
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207/432: channel x wavelength sbf_=True recon_mode='mua2conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=True
208/432: channel x wavelength sbf_=True recon_mode='mua2conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=True
209/432: channel x wavelength sbf_=True recon_mode='mua2conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=True
210/432: channel x wavelength sbf_=True recon_mode='mua2conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
211/432: channel x wavelength sbf_=True recon_mode='mua2conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
212/432: channel x wavelength sbf_=True recon_mode='mua2conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
213/432: channel x wavelength sbf_=True recon_mode='mua2conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
214/432: channel x wavelength sbf_=True recon_mode='mua2conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
215/432: channel x wavelength sbf_=True recon_mode='mua2conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
216/432: channel x wavelength sbf_=False recon_mode='mua' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=False
217/432: channel x wavelength sbf_=False recon_mode='mua' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=False
218/432: channel x wavelength sbf_=False recon_mode='mua' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=True
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220/432: channel x wavelength sbf_=False recon_mode='mua' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=True
221/432: channel x wavelength sbf_=False recon_mode='mua' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=True
222/432: channel x wavelength sbf_=False recon_mode='mua' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
223/432: channel x wavelength sbf_=False recon_mode='mua' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
224/432: channel x wavelength sbf_=False recon_mode='mua' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
225/432: channel x wavelength sbf_=False recon_mode='mua' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
226/432: channel x wavelength sbf_=False recon_mode='mua' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
227/432: channel x wavelength sbf_=False recon_mode='mua' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
228/432: channel x wavelength sbf_=False recon_mode='mua' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=False
229/432: channel x wavelength sbf_=False recon_mode='mua' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=False
230/432: channel x wavelength sbf_=False recon_mode='mua' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=True
231/432: channel x wavelength sbf_=False recon_mode='mua' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=True
232/432: channel x wavelength sbf_=False recon_mode='mua' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=True
233/432: channel x wavelength sbf_=False recon_mode='mua' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=True
234/432: channel x wavelength sbf_=False recon_mode='mua' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
235/432: channel x wavelength sbf_=False recon_mode='mua' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
236/432: channel x wavelength sbf_=False recon_mode='mua' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
237/432: channel x wavelength sbf_=False recon_mode='mua' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
238/432: channel x wavelength sbf_=False recon_mode='mua' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
239/432: channel x wavelength sbf_=False recon_mode='mua' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
240/432: channel x wavelength sbf_=False recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=False
241/432: channel x wavelength sbf_=False recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=False
242/432: channel x wavelength sbf_=False recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=True
243/432: channel x wavelength sbf_=False recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=True
244/432: channel x wavelength sbf_=False recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=True
245/432: channel x wavelength sbf_=False recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=True
246/432: channel x wavelength sbf_=False recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
247/432: channel x wavelength sbf_=False recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
248/432: channel x wavelength sbf_=False recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
249/432: channel x wavelength sbf_=False recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
250/432: channel x wavelength sbf_=False recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
251/432: channel x wavelength sbf_=False recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
252/432: channel x wavelength sbf_=False recon_mode='conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=False
253/432: channel x wavelength sbf_=False recon_mode='conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=False
254/432: channel x wavelength sbf_=False recon_mode='conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=True
255/432: channel x wavelength sbf_=False recon_mode='conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=True
256/432: channel x wavelength sbf_=False recon_mode='conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=True
257/432: channel x wavelength sbf_=False recon_mode='conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=True
258/432: channel x wavelength sbf_=False recon_mode='conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
259/432: channel x wavelength sbf_=False recon_mode='conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
260/432: channel x wavelength sbf_=False recon_mode='conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
261/432: channel x wavelength sbf_=False recon_mode='conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
262/432: channel x wavelength sbf_=False recon_mode='conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
263/432: channel x wavelength sbf_=False recon_mode='conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
264/432: channel x wavelength sbf_=False recon_mode='mua2conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=False
265/432: channel x wavelength sbf_=False recon_mode='mua2conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=False
266/432: channel x wavelength sbf_=False recon_mode='mua2conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=True
267/432: channel x wavelength sbf_=False recon_mode='mua2conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=True
268/432: channel x wavelength sbf_=False recon_mode='mua2conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=True
269/432: channel x wavelength sbf_=False recon_mode='mua2conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=True
270/432: channel x wavelength sbf_=False recon_mode='mua2conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
271/432: channel x wavelength sbf_=False recon_mode='mua2conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
272/432: channel x wavelength sbf_=False recon_mode='mua2conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
273/432: channel x wavelength sbf_=False recon_mode='mua2conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
274/432: channel x wavelength sbf_=False recon_mode='mua2conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
275/432: channel x wavelength sbf_=False recon_mode='mua2conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
276/432: channel x wavelength sbf_=False recon_mode='mua2conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=False
277/432: channel x wavelength sbf_=False recon_mode='mua2conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=False
278/432: channel x wavelength sbf_=False recon_mode='mua2conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=True
279/432: channel x wavelength sbf_=False recon_mode='mua2conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=True
280/432: channel x wavelength sbf_=False recon_mode='mua2conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=True
281/432: channel x wavelength sbf_=False recon_mode='mua2conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=True
282/432: channel x wavelength sbf_=False recon_mode='mua2conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
283/432: channel x wavelength sbf_=False recon_mode='mua2conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
284/432: channel x wavelength sbf_=False recon_mode='mua2conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
285/432: channel x wavelength sbf_=False recon_mode='mua2conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
286/432: channel x wavelength sbf_=False recon_mode='mua2conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
287/432: channel x wavelength sbf_=False recon_mode='mua2conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
288/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='mua' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=False
289/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='mua' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=False
290/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='mua' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=True
291/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='mua' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=True
292/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='mua' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=True
293/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='mua' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=True
294/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='mua' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
295/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='mua' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
296/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='mua' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
297/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='mua' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
298/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='mua' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
299/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='mua' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
300/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='mua' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=False
301/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='mua' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=False
302/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='mua' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=True
303/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='mua' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=True
304/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='mua' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=True
305/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='mua' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=True
306/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='mua' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
307/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='mua' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
308/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='mua' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
309/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='mua' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
310/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='mua' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
311/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='mua' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
312/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=False
313/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=False
314/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=True
315/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=True
316/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=True
317/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=True
318/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
319/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
320/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
321/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
322/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
323/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
324/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=False
325/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=False
326/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=True
327/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=True
328/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=True
329/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=True
330/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
331/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
332/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
333/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
334/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
335/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
336/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='mua2conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=False
337/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='mua2conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=False
338/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='mua2conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=True
339/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='mua2conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=True
340/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='mua2conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=True
341/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='mua2conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=True
342/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='mua2conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
343/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='mua2conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
344/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='mua2conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
345/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='mua2conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
346/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='mua2conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
347/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='mua2conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
348/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='mua2conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=False
349/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='mua2conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=False
350/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='mua2conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=True
351/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='mua2conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=True
352/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='mua2conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=True
353/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='mua2conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=None c_meas_=True
354/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='mua2conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
355/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='mua2conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
356/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='mua2conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
357/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='mua2conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
358/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='mua2conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
359/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='mua2conc' brainonly_mode=True alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
360/432: trial_type x channel x wavelength x reltime sbf_=False recon_mode='mua' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=False
361/432: trial_type x channel x wavelength x reltime sbf_=False recon_mode='mua' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=False
362/432: trial_type x channel x wavelength x reltime sbf_=False recon_mode='mua' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=True
363/432: trial_type x channel x wavelength x reltime sbf_=False recon_mode='mua' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=True
364/432: trial_type x channel x wavelength x reltime sbf_=False recon_mode='mua' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=True
365/432: trial_type x channel x wavelength x reltime sbf_=False recon_mode='mua' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=True
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Inspect Reconstruction results

[30]:

plot_combinations = [
    i
    for i, (
        ts,
        sbf_,
        recon_mode,
        brainonly_mode,
        alpha_meas,
        alpha_spatial,
        c_meas_,
        lambda_R_conc
    ) in enumerate(combinations)
    if (recon_mode == "conc")
    and (brainonly_mode == False)
    and (ts.dims == ("trial_type", "channel", "wavelength", "reltime"))
]
display(plot_combinations)

[312,
 313,
 314,
 315,
 316,
 317,
 318,
 319,
 320,
 321,
 322,
 323,
 384,
 385,
 386,
 387,
 388,
 389,
 390,
 391,
 392,
 393,
 394,
 395]

[31]:

for i_comb in plot_combinations:

    recon_result, _ = run_comb(i_comb, catch=False)

    #display(recon_result)

    # reduce recon_result to vertex x chromo
    X_ts = recon_result.sel(vertex=recon_result.is_brain).isel(trial_type=0, reltime=0)

    image_recon_multi_view(
        X_ts,  # time series data; can be 2D (static) or 3D (dynamic)
        head,
        cmap="seismic",
        #clim=(-1e-6, 1e-6),
        view_type="hbr_brain",
        title_str="HbO / uM",
        # filename=filename_multiview,
        SAVE=False,
        # time_range=(-5,30,0.5)*units.s,
        # fps=6,
        geo3d_plot=None,  #  geo3d_plot
        wdw_size=(1024, 768),
    )

312/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=False
recon_result dims:('trial_type', 'reltime', 'chromo', 'vertex') shape: (1, 1, 2, 35050)
../../_images/examples_head_models_47_image_reconstruction_regularizations_53_1.png 
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recon_result dims:('trial_type', 'reltime', 'chromo', 'vertex') shape: (1, 1, 2, 35050)
../../_images/examples_head_models_47_image_reconstruction_regularizations_53_3.png 
314/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=True
recon_result dims:('trial_type', 'reltime', 'chromo', 'vertex') shape: (1, 1, 2, 35050)
../../_images/examples_head_models_47_image_reconstruction_regularizations_53_5.png 
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recon_result dims:('trial_type', 'reltime', 'chromo', 'vertex') shape: (1, 1, 2, 35050)
../../_images/examples_head_models_47_image_reconstruction_regularizations_53_7.png 
316/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=True
recon_result dims:('trial_type', 'reltime', 'chromo', 'vertex') shape: (1, 1, 2, 35050)
../../_images/examples_head_models_47_image_reconstruction_regularizations_53_9.png 
317/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=True
recon_result dims:('trial_type', 'reltime', 'chromo', 'vertex') shape: (1, 1, 2, 35050)
../../_images/examples_head_models_47_image_reconstruction_regularizations_53_11.png 
318/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
recon_result dims:('trial_type', 'reltime', 'chromo', 'vertex') shape: (1, 1, 2, 35050)
../../_images/examples_head_models_47_image_reconstruction_regularizations_53_13.png 
319/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
recon_result dims:('trial_type', 'reltime', 'chromo', 'vertex') shape: (1, 1, 2, 35050)
../../_images/examples_head_models_47_image_reconstruction_regularizations_53_15.png 
320/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
recon_result dims:('trial_type', 'reltime', 'chromo', 'vertex') shape: (1, 1, 2, 35050)
../../_images/examples_head_models_47_image_reconstruction_regularizations_53_17.png 
321/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
recon_result dims:('trial_type', 'reltime', 'chromo', 'vertex') shape: (1, 1, 2, 35050)
../../_images/examples_head_models_47_image_reconstruction_regularizations_53_19.png 
322/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
recon_result dims:('trial_type', 'reltime', 'chromo', 'vertex') shape: (1, 1, 2, 35050)
../../_images/examples_head_models_47_image_reconstruction_regularizations_53_21.png 
323/432: trial_type x channel x wavelength x reltime sbf_=True recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
recon_result dims:('trial_type', 'reltime', 'chromo', 'vertex') shape: (1, 1, 2, 35050)
../../_images/examples_head_models_47_image_reconstruction_regularizations_53_23.png 
384/432: trial_type x channel x wavelength x reltime sbf_=False recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=False
recon_result dims:('trial_type', 'reltime', 'chromo', 'vertex') shape: (1, 1, 2, 35050)
../../_images/examples_head_models_47_image_reconstruction_regularizations_53_25.png 
385/432: trial_type x channel x wavelength x reltime sbf_=False recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=False
recon_result dims:('trial_type', 'reltime', 'chromo', 'vertex') shape: (1, 1, 2, 35050)
../../_images/examples_head_models_47_image_reconstruction_regularizations_53_27.png 
386/432: trial_type x channel x wavelength x reltime sbf_=False recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=True
recon_result dims:('trial_type', 'reltime', 'chromo', 'vertex') shape: (1, 1, 2, 35050)
../../_images/examples_head_models_47_image_reconstruction_regularizations_53_29.png 
387/432: trial_type x channel x wavelength x reltime sbf_=False recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=True
recon_result dims:('trial_type', 'reltime', 'chromo', 'vertex') shape: (1, 1, 2, 35050)
../../_images/examples_head_models_47_image_reconstruction_regularizations_53_31.png 
388/432: trial_type x channel x wavelength x reltime sbf_=False recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=True
recon_result dims:('trial_type', 'reltime', 'chromo', 'vertex') shape: (1, 1, 2, 35050)
../../_images/examples_head_models_47_image_reconstruction_regularizations_53_33.png 
389/432: trial_type x channel x wavelength x reltime sbf_=False recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=None c_meas_=True
recon_result dims:('trial_type', 'reltime', 'chromo', 'vertex') shape: (1, 1, 2, 35050)
../../_images/examples_head_models_47_image_reconstruction_regularizations_53_35.png 
390/432: trial_type x channel x wavelength x reltime sbf_=False recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
recon_result dims:('trial_type', 'reltime', 'chromo', 'vertex') shape: (1, 1, 2, 35050)
../../_images/examples_head_models_47_image_reconstruction_regularizations_53_37.png 
391/432: trial_type x channel x wavelength x reltime sbf_=False recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=False
recon_result dims:('trial_type', 'reltime', 'chromo', 'vertex') shape: (1, 1, 2, 35050)
../../_images/examples_head_models_47_image_reconstruction_regularizations_53_39.png 
392/432: trial_type x channel x wavelength x reltime sbf_=False recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
recon_result dims:('trial_type', 'reltime', 'chromo', 'vertex') shape: (1, 1, 2, 35050)
../../_images/examples_head_models_47_image_reconstruction_regularizations_53_41.png 
393/432: trial_type x channel x wavelength x reltime sbf_=False recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
recon_result dims:('trial_type', 'reltime', 'chromo', 'vertex') shape: (1, 1, 2, 35050)
../../_images/examples_head_models_47_image_reconstruction_regularizations_53_43.png 
394/432: trial_type x channel x wavelength x reltime sbf_=False recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
recon_result dims:('trial_type', 'reltime', 'chromo', 'vertex') shape: (1, 1, 2, 35050)
../../_images/examples_head_models_47_image_reconstruction_regularizations_53_45.png 
395/432: trial_type x channel x wavelength x reltime sbf_=False recon_mode='conc' brainonly_mode=False alpha_meas=0.01 alpha_spatial=0.01 c_meas_=True
recon_result dims:('trial_type', 'reltime', 'chromo', 'vertex') shape: (1, 1, 2, 35050)
../../_images/examples_head_models_47_image_reconstruction_regularizations_53_47.png

References

[32]:

cedalion.bib.dump_to_notebook()

Methods used

[1]Tucker2022cedalion.io.snirf.read_snirfStephen Tucker, Jay Dubb, Sreekanth Kura, Alexander von Lühmann, Robert Franke, Jörn M. Horschig, Samuel Powell, Robert Oostenveld, Michael Lührs, Édouard Delaire, Zahra M. Aghajan, Hanseok Yun, Meryem A. Yücel, Qianqian Fang, Theodore J. Huppert, Blaise deB. Frederick, Luca Pollonini, David A. Boas, and Robert Luke. Introduction to the shared near infrared spectroscopy format. Neurophotonics, 10(1):013507, 2022. doi:10.1117/1.NPh.10.1.013507.
[2]Delpy1988cedalion.nirs.cw.int2odD. T. Delpy, M. Cope, P. van der Zee, S. Arridge, S. Wray, and J. Wyatt. Estimation of optical pathlength through tissue from direct time of flight measurement. Physics in Medicine and Biology, 33(12):1433–1442, 1988. doi:10.1088/0031-9155/33/12/008.
[3]Villringer1997cedalion.nirs.cw.int2odArno Villringer and Britton Chance. Non-invasive optical spectroscopy and imaging of human brain function. Trends in Neurosciences, 20(10):435–442, 1997. doi:10.1016/S0166-2236(97)01132-6.
[4]Fishburn2019cedalion.sigproc.motion.tddrFrank A. Fishburn, Ruth S. Ludlum, Chandan J. Vaidya, and Andrei V. Medvedev. Temporal derivative distribution repair (tddr): a motion correction method for fnirs. NeuroImage, 184:171–179, 2019. doi:https://doi.org/10.1016/j.neuroimage.2018.09.025.
[5]Fishburn2018cedalion.sigproc.motion.tddrFrank Fishburn. Tddr. 2018. URL: https://github.com/frankfishburn/TDDR/.
[6]Molavi2012cedalion.sigproc.motion.waveletBehnam Molavi and Guy A Dumont. Wavelet-based motion artifact removal for functional near-infrared spectroscopy. Physiological Measurement, 33(2):259, 2012. doi:10.1088/0967-3334/33/2/259.
[7]Huppert2009cedalion.sigproc.motion.waveletTheodore J. Huppert, Solomon G. Diamond, Maria A. Franceschini, and David A. Boas. Homer: a review of time-series analysis methods for near-infrared spectroscopy of the brain. Appl. Opt., 48(10):D280–D298, Apr 2009. doi:https://doi.org/10.1364/AO.48.00D280.
[8]Holmes1998cedalion.data.get_colin27_headmodel_filesColin J. Holmes, Rick Hoge, Louis Collins, Roger Woods, Arthur W. Toga, and Alan C. Evans. Enhancement of mr images using registration for signal averaging. Journal of Computer Assisted Tomography, 22(2):324–333, March 1998. doi:10.1097/00004728-199803000-00032.
[9]Fischl2012cedalion.data.get_colin27_headmodel_filesBruce Fischl. FreeSurfer. NeuroImage, 62(2):774–781, 2012. doi:10.1016/j.neuroimage.2012.01.021.
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