mriqc.qc package¶
Module contents¶
This module contains the actual computation of measures included within MRIQC.
Note
Most of the measures computed in this module are adapted, derived or reproduced from the QAP project [QAP].
Submodules¶
mriqc.qc.anatomical module¶
Computation of the quality assessment measures on structural MRI
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mriqc.qc.anatomical.
artifacts
(img, seg, calculate_qi2=False, bglabel=0)[source]¶ Detect artifacts in the image using the method described in [Mortamet2009]. Calculates QI1, the fraction of total voxels that within artifacts.
Optionally, it also calculates QI2, the distance between the distribution of noise voxel (non-artifact background voxels) intensities, and a Rician distribution.
Parameters: - img (numpy.ndarray) – input data
- seg (numpy.ndarray) – input segmentation
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mriqc.qc.anatomical.
cnr
(img, seg, lbl=None)[source]¶ Calculate the CNR
\[\text{CNR} = \frac{|\mu_\text{GM} - \mu_\text{WM} |}{\sigma_B}\]Parameters: - img (numpy.ndarray) – input data
- seg (numpy.ndarray) – input segmentation
Returns: the computed CNR
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mriqc.qc.anatomical.
efc
(img)[source]¶ Calculate the EFC [Atkinson1997]
The original equation is normalized by the maximum entropy, so that the EFC can be compared across images with different dimensions.
Parameters: img (numpy.ndarray) – input data
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mriqc.qc.anatomical.
fber
(img, seg, fglabel=None, bglabel=0)[source]¶ Calculate the FBER
\[\text{FBER} = \frac{E[|F|^2]}{E[|B|^2]}\]Parameters: - img (numpy.ndarray) – input data
- seg (numpy.ndarray) – input segmentation
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mriqc.qc.anatomical.
snr
(img, seg, fglabel, bglabel='bg')[source]¶ Calculate the SNR
\[\text{SNR} = \frac{\mu_F}{\sigma_B}\]where \(\mu_F\) is the mean intensity of the foreground and \(\sigma_B\) is the standard deviation of the background, where the noise is computed.
Parameters: Returns: the computed SNR for the foreground segmentation
mriqc.qc.functional module¶
Computation of the quality assessment measures on functional MRI
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mriqc.qc.functional.
dvars
(func, mask, output_all=False, out_file=None)[source]¶ Compute the mean DVARS [Power2012].
Particularly, the standardized DVARS [Nichols2013] are computed.
Note
Implementation details
Uses the implementation of the Yule-Walker equations from nitime for the AR filtering of the fMRI signal.
Parameters: Returns: the standardized DVARS
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mriqc.qc.functional.
fd_jenkinson
(in_file, rmax=80.0, out_file=None)[source]¶ Compute the FD [Jenkinson2002] on a 4D dataset, after
3dvolreg
has been executed (generally a file named*.affmat12.1D
).Parameters: Returns: the output file with the FD, and the average FD along the time series
Return type: tuple(str, float)
Note
infile
should have one 3dvolreg affine matrix in one row - NOT the motion parameters
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mriqc.qc.functional.
gcor
(func, mask)[source]¶ Compute the GCOR.
Parameters: - func (numpy.ndarray) – input fMRI dataset, after motion correction
- mask (numpy.ndarray) – 3D brain mask
Returns: the computed GCOR value
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mriqc.qc.functional.
gsr
(epi_data, mask, direction='y', ref_file=None, out_file=None)[source]¶ Computes the GSR [Giannelli2010]. The procedure is as follows:
- Create a Nyquist ghost mask by circle-shifting the original mask by \(N/2\).
- Rotate by \(N/2\)
- Remove the intersection with the original mask
- Generate a non-ghost background
- Calculate the GSR
Warning
This should be used with EPI images for which the phase encoding direction is known.
Parameters: Returns: the computed gsr
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mriqc.qc.functional.
zero_variance
(func, mask)[source]¶ Mask out voxels with zero variance across t-axis
Parameters: - func (numpy.ndarray) – input fMRI dataset, after motion correction
- mask (numpy.ndarray) – 3D brain mask
Returns: the 3D mask of voxels with nonzero variance across \(t\).
Return type: numpy.ndarray
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.
[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.
[Mortamet2009] Mortamet B et al., Automatic quality assessment in structural brain magnetic resonance imaging, Mag Res Med 62(2):365-372, 2009. doi:10.1002/mrm.21992.
[Nichols2013] Nichols, Notes on Creating a Standardized Version of DVARS, 2013.
[Power2012] Poweret 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.
[QAP] The QAP project.