Optimal combination with t2smap

Optimal combination with t2smap#

Use t2smap [DuPre et al., 2021] to combine data.

import os
from glob import glob

import matplotlib.pyplot as plt
import numpy as np
from myst_nb import glue
from nilearn import image, plotting
from repo2data.repo2data import Repo2Data
from tedana import workflows

# Install the data if running locally, or point to cached data if running on neurolibre
DATA_REQ_FILE = os.path.join("../binder/data_requirement.json")

# Download data
repo2data = Repo2Data(DATA_REQ_FILE)
data_path = repo2data.install()
data_path = os.path.abspath(data_path[0])
---- repo2data starting ----
/opt/hostedtoolcache/Python/3.10.18/x64/lib/python3.10/site-packages/repo2data
Config from file :
../binder/data_requirement.json
Destination:
./../data/multi-echo-data-analysis

Info : ./../data/multi-echo-data-analysis already downloaded
/opt/hostedtoolcache/Python/3.10.18/x64/lib/python3.10/site-packages/pybtex/plugin/__init__.py:26: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.
  import pkg_resources
/opt/hostedtoolcache/Python/3.10.18/x64/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm
func_dir = os.path.join(data_path, "func/")
data_files = [
    os.path.join(
        func_dir,
        "sub-04570_task-rest_echo-1_space-scanner_desc-partialPreproc_bold.nii.gz",
    ),
    os.path.join(
        func_dir,
        "sub-04570_task-rest_echo-2_space-scanner_desc-partialPreproc_bold.nii.gz",
    ),
    os.path.join(
        func_dir,
        "sub-04570_task-rest_echo-3_space-scanner_desc-partialPreproc_bold.nii.gz",
    ),
    os.path.join(
        func_dir,
        "sub-04570_task-rest_echo-4_space-scanner_desc-partialPreproc_bold.nii.gz",
    ),
]
echo_times = [12.0, 28.0, 44.0, 60.0]
mask_file = os.path.join(
    func_dir, "sub-04570_task-rest_space-scanner_desc-brain_mask.nii.gz"
)
confounds_file = os.path.join(
    func_dir, "sub-04570_task-rest_desc-confounds_timeseries.tsv"
)

out_dir = os.path.join(data_path, "t2smap")
workflows.t2smap_workflow(
    data_files,
    echo_times,
    out_dir=out_dir,
    mask=mask_file,
    prefix="sub-04570_task-rest_space-scanner",
    fittype="curvefit",
)
INFO     t2smap:t2smap_workflow:300 Using output directory: /home/runner/work/multi-echo-data-analysis/multi-echo-data-analysis/data/multi-echo-data-analysis/t2smap
INFO     t2smap:t2smap_workflow:326 Loading input data: ['/home/runner/work/multi-echo-data-analysis/multi-echo-data-analysis/data/multi-echo-data-analysis/func/sub-04570_task-rest_echo-1_space-scanner_desc-partialPreproc_bold.nii.gz', '/home/runner/work/multi-echo-data-analysis/multi-echo-data-analysis/data/multi-echo-data-analysis/func/sub-04570_task-rest_echo-2_space-scanner_desc-partialPreproc_bold.nii.gz', '/home/runner/work/multi-echo-data-analysis/multi-echo-data-analysis/data/multi-echo-data-analysis/func/sub-04570_task-rest_echo-3_space-scanner_desc-partialPreproc_bold.nii.gz', '/home/runner/work/multi-echo-data-analysis/multi-echo-data-analysis/data/multi-echo-data-analysis/func/sub-04570_task-rest_echo-4_space-scanner_desc-partialPreproc_bold.nii.gz']
INFO     t2smap:t2smap_workflow:348 Using user-defined mask
INFO     utils:make_adaptive_mask:202 Echo-wise intensity thresholds for adaptive mask: [258.33994278 180.98638476 134.6796175   91.51006253]
WARNING  utils:make_adaptive_mask:231 734 voxels in user-defined mask do not have good signal. Removing voxels from mask.
INFO     t2smap:t2smap_workflow:357 Computing adaptive T2* map
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4-echo monoexponential:  99%|█████████▉| 20044/20176 [00:32<00:00, 597.61it/s]
4-echo monoexponential: 100%|█████████▉| 20104/20176 [00:32<00:00, 594.84it/s]
4-echo monoexponential: 100%|█████████▉| 20164/20176 [00:32<00:00, 593.53it/s]
4-echo monoexponential: 100%|██████████| 20176/20176 [00:32<00:00, 623.07it/s]
INFO     t2smap:t2smap_workflow:370 Calculating model fit quality metrics
/opt/hostedtoolcache/Python/3.10.18/x64/lib/python3.10/site-packages/tedana/decay.py:541: RuntimeWarning: Mean of empty slice
  rmse_map = np.nanmean(rmse, axis=1)
INFO     t2smap:t2smap_workflow:382 Computing optimal combination
INFO     combine:make_optcom:192 Optimally combining data with voxel-wise T2* estimates
INFO     t2smap:t2smap_workflow:440 Workflow completed
INFO     utils:log_newsletter_info:705 Don't forget to subscribe to the tedana newsletter for updates! This is a very low volume email list.
INFO     utils:log_newsletter_info:709 https://groups.google.com/g/tedana-newsletter
out_files = sorted(glob(os.path.join(out_dir, "*")))
out_files = [os.path.basename(f) for f in out_files]
print("\n".join(out_files))
sub-04570_task-rest_space-scanner_S0map.nii.gz
sub-04570_task-rest_space-scanner_T2starmap.nii.gz
sub-04570_task-rest_space-scanner_dataset_description.json
sub-04570_task-rest_space-scanner_desc-confounds_timeseries.tsv
sub-04570_task-rest_space-scanner_desc-limited_S0map.nii.gz
sub-04570_task-rest_space-scanner_desc-limited_T2starmap.nii.gz
sub-04570_task-rest_space-scanner_desc-optcom_bold.nii.gz
sub-04570_task-rest_space-scanner_desc-rmse_statmap.nii.gz
sub-04570_task-rest_space-scanner_desc-tedana_registry.json
fig, ax = plt.subplots(figsize=(16, 8))
plotting.plot_stat_map(
    os.path.join(out_dir, "sub-04570_task-rest_space-scanner_T2starmap.nii.gz"),
    vmax=0.6,
    draw_cross=False,
    bg_img=None,
    figure=fig,
    axes=ax,
)
glue("figure_t2starmap", fig, display=False)
<Figure size 1600x800 with 5 Axes>

Fig. 20 T2* map estimated from multi-echo data using tedana’s t2smap_workflow().#

fig, ax = plt.subplots(figsize=(16, 8))
plotting.plot_stat_map(
    os.path.join(out_dir, "sub-04570_task-rest_space-scanner_S0map.nii.gz"),
    vmax=8000,
    draw_cross=False,
    bg_img=None,
    figure=fig,
    axes=ax,
)
glue("figure_s0map", fig, display=False)
<Figure size 1600x800 with 5 Axes>

Fig. 21 S0 map estimated from multi-echo data using tedana’s t2smap_workflow().#

fig, axes = plt.subplots(figsize=(16, 15), nrows=5)
plotting.plot_epi(
    image.mean_img(data_files[0]),
    draw_cross=False,
    bg_img=None,
    cut_coords=[-10, 0, 10, 20, 30, 40, 50, 60, 70],
    display_mode="z",
    figure=fig,
    axes=axes[0],
)
plotting.plot_epi(
    image.mean_img(data_files[1]),
    draw_cross=False,
    bg_img=None,
    cut_coords=[-10, 0, 10, 20, 30, 40, 50, 60, 70],
    display_mode="z",
    figure=fig,
    axes=axes[1],
)
plotting.plot_epi(
    image.mean_img(data_files[2]),
    draw_cross=False,
    bg_img=None,
    cut_coords=[-10, 0, 10, 20, 30, 40, 50, 60, 70],
    display_mode="z",
    figure=fig,
    axes=axes[2],
)
plotting.plot_epi(
    image.mean_img(data_files[3]),
    draw_cross=False,
    bg_img=None,
    cut_coords=[-10, 0, 10, 20, 30, 40, 50, 60, 70],
    display_mode="z",
    figure=fig,
    axes=axes[3],
)
plotting.plot_epi(
    image.mean_img(
        os.path.join(
            out_dir, "sub-04570_task-rest_space-scanner_desc-optcom_bold.nii.gz"
        )
    ),
    draw_cross=False,
    bg_img=None,
    cut_coords=[-10, 0, 10, 20, 30, 40, 50, 60, 70],
    display_mode="z",
    figure=fig,
    axes=axes[4],
)
glue("figure_t2smap_epi_plots", fig, display=False)
<Figure size 1600x1500 with 50 Axes>

Fig. 22 Mean map of each of the echoes in the original data, along with the mean map of the optimally combined data.#

te30_tsnr = image.math_img(
    "(np.nanmean(img, axis=3) / np.nanstd(img, axis=3)) * mask",
    img=data_files[1],
    mask=mask_file,
)
oc_tsnr = image.math_img(
    "(np.nanmean(img, axis=3) / np.nanstd(img, axis=3)) * mask",
    img=os.path.join(
        out_dir, "sub-04570_task-rest_space-scanner_desc-optcom_bold.nii.gz"
    ),
    mask=mask_file,
)
vmax = np.nanmax(np.abs(oc_tsnr.get_fdata()))

fig, axes = plt.subplots(figsize=(10, 8), nrows=2)
plotting.plot_stat_map(
    te30_tsnr,
    draw_cross=False,
    bg_img=None,
    threshold=0.1,
    cut_coords=[0, 10, 10],
    vmax=vmax,
    symmetric_cbar=False,
    figure=fig,
    axes=axes[0],
)
axes[0].set_title("TE30 TSNR", fontsize=16)
plotting.plot_stat_map(
    oc_tsnr,
    draw_cross=False,
    bg_img=None,
    threshold=0.1,
    cut_coords=[0, 10, 10],
    vmax=vmax,
    symmetric_cbar=False,
    figure=fig,
    axes=axes[1],
)
axes[1].set_title("Optimal Combination TSNR", fontsize=16)
glue("figure_t2smap_t2snr", fig, display=False)
<string>:1: RuntimeWarning: invalid value encountered in divide
/opt/hostedtoolcache/Python/3.10.18/x64/lib/python3.10/site-packages/nilearn/plotting/img_plotting.py:1317: UserWarning: Non-finite values detected. These values will be replaced with zeros.
  safe_get_data(stat_map_img, ensure_finite=True),
<Figure size 1000x800 with 10 Axes>

Fig. 23 TSNR map of each of the echoes in the original data, along with the TSNR map of the optimally combined data.#

fig, ax = plt.subplots(figsize=(16, 8))
plotting.plot_carpet(
    data_files[1],
    figure=fig,
    axes=ax,
)
glue("figure_echo2_carpet", fig, display=False)
<Figure size 1600x800 with 1 Axes>

Fig. 24 Carpet plot of the second echo’s data.#

fig, ax = plt.subplots(figsize=(16, 8))
plotting.plot_carpet(
    os.path.join(out_dir, "sub-04570_task-rest_space-scanner_desc-optcom_bold.nii.gz"),
    axes=ax,
)
glue("figure_optcom_carpet", fig, display=False)
<Figure size 1600x800 with 1 Axes>

Fig. 25 Carpet plot of the optimally combined data.#