Source code for mriqc.qc.functional

# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
# Copyright 2021 The NiPreps Developers <>
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
# We support and encourage derived works from this project, please read
# about our expectations at
Measures for the spatial information

Definitions are given in the
:ref:`summary of structural IQMs <iqms_t1w>`.

.. _iqms_efc:

- **Entropy-focus criterion** (:py:func:`~mriqc.qc.anatomical.efc`).

.. _iqms_fber:

- **Foreground-Background energy ratio** (:py:func:`~mriqc.qc.anatomical.fber`,  [Shehzad2015]_).

.. _iqms_fwhm:

- **Full-width half maximum smoothness** (``fwhm_*``, see [Friedman2008]_).

.. _iqms_snr:

- **Signal-to-noise ratio** (:py:func:`~mriqc.qc.anatomical.snr`).

.. _iqms_summary:

- **Summary statistics** (:py:func:`~mriqc.qc.anatomical.summary_stats`).

Measures for the temporal information

.. _iqms_dvars :

  D referring to temporal derivative of timecourses, VARS referring to
  RMS variance over voxels ([Power2012]_ ``dvars_nstd``) indexes the rate of change of
  BOLD signal across the entire brain at each frame of data. DVARS is calculated
  `with nipype
  after motion correction:

  .. math ::

      \text{DVARS}_t = \sqrt{\frac{1}{N}\sum_i \left[x_{i,t} - x_{i,t-1}\right]^2}

  .. note ::

    Intensities are scaled to 1000 leading to the units being expressed in x10
    :math:`\%\Delta\text{BOLD}` change.

  .. note ::

    MRIQC calculates two additional standardized values of the DVARS.
    The ``dvars_std`` metric is normalized with the standard deviation of the
    temporal difference time series. The ``dvars_vstd`` is a voxel-wise
    standardization of DVARS, where the temporal difference time series is
    normalized across time by that voxel standard deviation across time, before
    computing the RMS of the temporal difference [Nichols2013]_.

.. _iqms_gcor:

Global Correlation (``gcor``)
  calculates an optimized summary of time-series
  correlation as in [Saad2013]_ using AFNI's ``@compute_gcor``:

  .. math ::

      \text{GCOR} = \frac{1}{N}\mathbf{g}_u^T\mathbf{g}_u

  where :math:`\mathbf{g}_u` is the average of all unit-variance time series in a
  :math:`T` (# timepoints) :math:`\times` :math:`N` (# voxels) matrix.

.. _iqms_tsnr:

Temporal SNR (:abbr:`tSNR (temporal SNR)`, ``tsnr``)
  is a simplified interpretation of the tSNR definition [Kruger2001]_.
  We report the median value
  of the `tSNR map
  calculated like:

  .. math ::

      \text{tSNR} = \frac{\langle S \rangle_t}{\sigma_t},

  where :math:`\langle S \rangle_t` is the average BOLD signal (across time),
  and :math:`\sigma_t` is the corresponding temporal standard-deviation map. Higher
  values are better.

Measures for artifacts and other

.. _iqms_fd:

Framewise Displacement
  expresses instantaneous head-motion [Jenkinson2002]_.
  MRIQC reports the average FD, labeled as ``fd_mean``.
  Rotational displacements are calculated as the displacement on the surface of a
  sphere of radius 50 mm [Power2012]_:

  .. math ::

      \text{FD}_t = |\Delta d_{x,t}| + |\Delta d_{y,t}| +
      |\Delta d_{z,t}| + |\Delta \alpha_t| + |\Delta \beta_t| + |\Delta \gamma_t|

  Along with the base framewise displacement, MRIQC reports the
  **number of timepoints above FD threshold** (``fd_num``), and the
  **percent of FDs above the FD threshold** w.r.t. the full timeseries (``fd_perc``).
  In both cases, the threshold is set at 0.20mm.

.. _iqms_gsr:

Ghost to Signal Ratio (:py:func:`~mriqc.qc.functional.gsr`)
  labeled in the reports as ``gsr_x`` and ``gsr_y``
  (calculated along the two possible phase-encoding axes **x**, **y**):

  .. math ::

      \text{GSR} = \frac{\mu_G - \mu_{NG}}{\mu_S}

  .. image :: ../_static/epi-gsrmask.png
    :width: 200px
    :align: center

.. _iqms_aor:

AFNI's outlier ratio (``aor``)
  Mean fraction of outliers per fMRI volume
  as given by AFNI's ``3dToutcount``.

.. _iqms_aqi:

AFNI's quality index (``aqi``)
  Mean quality index as computed by AFNI's ``3dTqual``; for each volume,
  it is one minus the Spearman's (rank) correlation of that volume with the
  median volume. Lower values are better.

.. _iqms_dummy:

Number of *dummy* scans** (``dummy``)
  A number of volumes in the beginning of the
  fMRI timeseries identified as non-steady state.

.. topic:: References

  .. [Atkinson1997] Atkinson et al., *Automatic correction of motion artifacts
    in magnetic resonance images using an entropy
    focus criterion*, IEEE Trans Med Imag 16(6):903-910, 1997.
    doi:`10.1109/42.650886 <>`_.

  .. [Friedman2008] Friedman, L et al., *Test--retest and between‐site reliability in a multicenter
    fMRI study*. Hum Brain Mapp, 29(8):958--972, 2008. doi:`10.1002/hbm.20440

  .. [Giannelli2010] Giannelli et al., *Characterization of Nyquist ghost in
    EPI-fMRI acquisition sequences implemented on two clinical 1.5 T MR scanner
    systems: effect of readout bandwidth and echo spacing*. J App Clin Med Phy,
    11(4). 2010.
    doi:`10.1120/jacmp.v11i4.3237 <>`_.

  .. [Jenkinson2002] Jenkinson et al., *Improved Optimisation for the Robust and
    Accurate Linear Registration and Motion Correction of Brain Images*.
    NeuroImage, 17(2), 825-841, 2002.
    doi:`10.1006/nimg.2002.1132 <>`_.

  .. [Kruger2001] Krüger et al., *Physiological noise in oxygenation-sensitive
    magnetic resonance imaging*, Magn. Reson. Med. 46(4):631-637, 2001.
    doi:`10.1002/mrm.1240 <>`_.

  .. [Nichols2013] Nichols, `Notes on Creating a Standardized Version of DVARS

  .. [Power2012] Power et al., *Spurious but systematic correlations in
    functional connectivity MRI networks arise from subject motion*,
    NeuroImage 59(3):2142-2154,
    2012, doi:`10.1016/j.neuroimage.2011.10.018

  .. [Saad2013] Saad et al. *Correcting Brain-Wide Correlation Differences
    in Resting-State FMRI*, Brain Conn 3(4):339-352,
    2013, doi:`10.1089/brain.2013.0156

import os.path as op

import numpy as np

RAS_AXIS_ORDER = {'x': 0, 'y': 1, 'z': 2}

[docs]def gsr(epi_data, mask, direction='y', ref_file=None, out_file=None): """ Compute the :abbr:`GSR (ghost to signal ratio)` [Giannelli2010]_. The procedure is as follows: #. Create a Nyquist ghost mask by circle-shifting the original mask by :math:`N/2`. #. Rotate by :math:`N/2` #. Remove the intersection with the original mask #. Generate a non-ghost background #. Calculate the :abbr:`GSR (ghost to signal ratio)` .. warning :: This should be used with EPI images for which the phase encoding direction is known. :param str epi_file: path to epi file :param str mask_file: path to brain mask :param str direction: the direction of phase encoding (x, y, all) :return: the computed gsr """ direction = direction.lower() if direction[-1] not in ('x', 'y', 'all'): raise Exception(f'Unknown direction {direction}, should be one of x, -x, y, -y, all') if direction == 'all': result = [] for newdir in ('x', 'y'): ofile = None if out_file is not None: fname, ext = op.splitext(ofile) if ext == '.gz': fname, ext2 = op.splitext(fname) ext = ext2 + ext ofile = f'{fname}_{newdir}{ext}' result += [gsr(epi_data, mask, newdir, ref_file=ref_file, out_file=ofile)] return result # Roll data of mask through the appropriate axis axis = RAS_AXIS_ORDER[direction] n2_mask = np.roll(mask, mask.shape[axis] // 2, axis=axis) # Step 3: remove from n2_mask pixels inside the brain n2_mask = n2_mask * (1 - mask) # Step 4: non-ghost background region is labeled as 2 n2_mask = n2_mask + 2 * (1 - n2_mask - mask) # Step 5: signal is the entire foreground image ghost = np.mean(epi_data[n2_mask == 1]) - np.mean(epi_data[n2_mask == 2]) signal = np.median(epi_data[n2_mask == 0]) return float(ghost / signal)