Optimal combination with t2smap#
Use t2smap [DuPre et al., 2021] to combine data.
import json
import os
from glob import glob
import matplotlib.pyplot as plt
import nibabel as nb
import numpy as np
from myst_nb import glue
from nilearn import image, plotting
from tedana import workflows
data_path = os.path.abspath('../DATA')
/opt/hostedtoolcache/Python/3.12.11/x64/lib/python3.12/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, "ds006185/sub-24053/ses-1/func/")
data_files = sorted(
glob(
os.path.join(
func_dir,
"sub-24053_ses-1_task-rat_rec-nordic_dir-PA_run-01_echo-*_part-mag_desc-preproc_bold.nii.gz",
),
),
)
echo_times = []
for f in data_files:
json_file = f.replace('.nii.gz', '.json')
with open(json_file, 'r') as fo:
metadata = json.load(fo)
echo_times.append(metadata['EchoTime'] * 1000)
mask_file = os.path.join(
func_dir,
"sub-24053_ses-1_task-rat_rec-nordic_dir-PA_run-01_part-mag_desc-brain_mask.nii.gz"
)
confounds_file = os.path.join(
func_dir,
"sub-24053_ses-1_task-rat_rec-nordic_dir-PA_run-01_part-mag_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-24053_ses-1_task-rat_rec-nordic_dir-PA_run-01",
fittype="loglin",
overwrite=True,
)
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
Cell In[3], line 1
----> 1 workflows.t2smap_workflow(
2 data_files,
3 echo_times,
4 out_dir=out_dir,
5 mask=mask_file,
6 prefix="sub-24053_ses-1_task-rat_rec-nordic_dir-PA_run-01",
7 fittype="loglin",
8 overwrite=True,
9 )
File /opt/hostedtoolcache/Python/3.12.11/x64/lib/python3.12/site-packages/tedana/workflows/t2smap.py:322, in t2smap_workflow(data, tes, n_independent_echos, out_dir, mask, prefix, convention, dummy_scans, exclude, masktype, fittype, fitmode, combmode, debug, verbose, quiet, overwrite, t2smap_command)
320 out_dir = op.abspath(out_dir)
321 if not op.isdir(out_dir):
--> 322 os.mkdir(out_dir)
324 # Parse exclude parameter
325 exclude_idx = parse_volume_indices(exclude)
FileNotFoundError: [Errno 2] No such file or directory: '/home/runner/work/multi-echo-data-analysis/multi-echo-data-analysis/DATA/t2smap'
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))
fig, ax = plt.subplots(figsize=(16, 8))
plotting.plot_stat_map(
os.path.join(out_dir, "sub-24053_ses-1_task-rat_rec-nordic_dir-PA_run-01_T2starmap.nii.gz"),
vmax=0.6,
draw_cross=False,
bg_img=None,
figure=fig,
axes=ax,
)
glue("figure_t2starmap", fig, display=False)
fig, ax = plt.subplots(figsize=(16, 8))
plotting.plot_stat_map(
os.path.join(out_dir, "sub-24053_ses-1_task-rat_rec-nordic_dir-PA_run-01_S0map.nii.gz"),
vmax=8000,
draw_cross=False,
bg_img=None,
figure=fig,
axes=ax,
)
glue("figure_s0map", fig, display=False)
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-24053_ses-1_task-rat_rec-nordic_dir-PA_run-01_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)
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-24053_ses-1_task-rat_rec-nordic_dir-PA_run-01_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)
fig, ax = plt.subplots(figsize=(16, 8))
plotting.plot_carpet(
data_files[1],
figure=fig,
axes=ax,
)
glue("figure_echo2_carpet", fig, display=False)
fig, ax = plt.subplots(figsize=(16, 8))
plotting.plot_carpet(
os.path.join(out_dir, "sub-24053_ses-1_task-rat_rec-nordic_dir-PA_run-01_desc-optcom_bold.nii.gz"),
axes=ax,
)
glue("figure_optcom_carpet", fig, display=False)