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# -*- coding: utf-8 -*-
"""
.. _tut-auto-coreg:
=============================================
Using an automated approach to coregistration
=============================================
This example shows how to use the coregistration functions to perform an
automated MEG-MRI coregistration via scripting. Generally the results of
this approach are consistent with those obtained from manual
coregistration :footcite:`HouckClaus2020`.
.. warning:: The quality of the coregistration depends heavily upon the
quality of the head shape points (HSP) collected during subject
prepration and the quality of your T1-weighted MRI. Use with
caution and check the coregistration error.
"""
# Author: Jon Houck <jon.houck@gmail.com>
# Guillaume Favelier <guillaume.favelier@gmail.com>
#
# License: BSD-3-Clause
import numpy as np
import mne
from mne.coreg import Coregistration
from mne.io import read_info
data_path = mne.datasets.sample.data_path()
# data_path and all paths built from it are pathlib.Path objects
subjects_dir = data_path / 'subjects'
subject = 'sample'
fname_raw = data_path / 'MEG' / subject / f'{subject}_audvis_raw.fif'
info = read_info(fname_raw)
plot_kwargs = dict(subject=subject, subjects_dir=subjects_dir,
surfaces="head-dense", dig=True, eeg=[],
meg='sensors', show_axes=True,
coord_frame='meg')
view_kwargs = dict(azimuth=45, elevation=90, distance=0.6,
focalpoint=(0., 0., 0.))
# %%
# Set up the coregistration model
# -------------------------------
fiducials = "estimated" # get fiducials from fsaverage
coreg = Coregistration(info, subject, subjects_dir, fiducials=fiducials)
fig = mne.viz.plot_alignment(info, trans=coreg.trans, **plot_kwargs)
# %%
# Initial fit with fiducials
# --------------------------
# Do first a coregistration fit using only 3 fiducial points. This allows
# to find a good initial solution before further optimization using
# head shape points. This can also be useful to detect outlier head shape
# points which are too far from the skin surface. One can see for example
# that on this dataset there is one such point and we will omit it from
# the subsequent fit.
coreg.fit_fiducials(verbose=True)
fig = mne.viz.plot_alignment(info, trans=coreg.trans, **plot_kwargs)
# %%
# Refining with ICP
# -----------------
# Next we refine the transformation using a few iteration of the
# Iterative Closest Point (ICP) algorithm. As the initial fiducials
# are obtained from fsaverage and not from precise manual picking in the
# GUI we do a fit with reduced weight for the nasion.
coreg.fit_icp(n_iterations=6, nasion_weight=2., verbose=True)
fig = mne.viz.plot_alignment(info, trans=coreg.trans, **plot_kwargs)
# %%
# Omitting bad points
# -------------------
# It is now very clear that we have one point that is an outlier
# and that should be removed.
coreg.omit_head_shape_points(distance=5. / 1000) # distance is in meters
# %%
# Final coregistration fit
# ------------------------
# sphinx_gallery_thumbnail_number = 4
coreg.fit_icp(n_iterations=20, nasion_weight=10., verbose=True)
fig = mne.viz.plot_alignment(info, trans=coreg.trans, **plot_kwargs)
mne.viz.set_3d_view(fig, **view_kwargs)
dists = coreg.compute_dig_mri_distances() * 1e3 # in mm
print(
f"Distance between HSP and MRI (mean/min/max):\n{np.mean(dists):.2f} mm "
f"/ {np.min(dists):.2f} mm / {np.max(dists):.2f} mm"
)
# %%
# .. warning::
# Don't forget to save the resulting ``trans`` matrix!
#
# .. code-block:: python
#
# mne.write_trans('/path/to/filename-trans.fif', coreg.trans)
#
# .. note:: The :class:`mne.coreg.Coregistration` class has the ability to
# compute MRI scale factors using
# :meth:`~mne.coreg.Coregistration.set_scale_mode` that is useful
# for creating surrogate MRI subjects, i.e., using a template MRI
# (such as one from :func:`mne.datasets.fetch_infant_template`)
# matched to a subject's head digitization. When scaling is desired,
# a scaled surrogate MRI should be created using
# :func:`mne.scale_mri`.
# %%
# References
# ----------
# .. footbibliography::
|
