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Description:

fsqc 2.0.2

fsqc toolbox
Description
This package provides quality assurance / quality control scripts for FastSurfer- or
FreeSurfer-processed structural MRI data.
It is a revision, extension, and translation to the Python language of the
Freesurfer QA Tools. It has
been augmented by additional functions from the MRIQC toolbox,
and with code derived from the LaPy and
BrainPrint toolboxes.
The core functionality of this toolbox is to compute the following features:

variable
description

subject
subject ID

wm_snr_orig
signal-to-noise ratio for white matter in orig.mgz

gm_snr_orig
signal-to-noise ratio for gray matter in orig.mgz

wm_snr_norm
signal-to-noise ratio for white matter in norm.mgz

gm_snr_norm
signal-to-noise ratio for gray matter in norm.mgz

cc_size
relative size of the corpus callosum

lh_holes
number of holes in the left hemisphere

rh_holes
number of holes in the right hemisphere

lh_defects
number of defects in the left hemisphere

rh_defects
number of defects in the right hemisphere

topo_lh
topological fixing time for the left hemisphere

topo_rh
topological fixing time for the right hemisphere

con_lh_snr
wm/gm contrast signal-to-noise ratio in the left hemisphere

con_rh_snr
wm/gm contrast signal-to-noise ratio in the right hemisphere

rot_tal_x
rotation component of the Talairach transform around the x axis

rot_tal_y
rotation component of the Talairach transform around the y axis

rot_tal_z
rotation component of the Talairach transform around the z axis

The program will use an existing output directory (or try to create it) and
write a csv table into that location. The csv table will contain the above
metrics plus a subject identifier.
The program can also be run on images that were processed with FastSurfer
(v1.1 or later) instead of FreeSurfer. In that case, simply add a --fastsurfer
switch to your shell command. Note that FastSurfer's full processing stream must
have been run, including surface reconstruction (i.e. brain segmentation alone
is not sufficient).
In addition to the core functionality of the toolbox there are several optional
modules that can be run according to need:

screenshots module

This module allows for the automated generation of cross-sections of the brain
that are overlaid with the anatomical segmentations (asegs) and the white and
pial surfaces. These images will be saved to the 'screenshots' subdirectory
that will be created within the output directory. These images can be used for
quickly glimpsing through the processing results. Note that no display manager
is required for this module, i.e. it can be run on a remote server, for example.

surfaces module

This module allows for the automated generation of surface renderings of the
left and right pial and inflated surfaces, overlaid with the aparc annotation.
These images will be saved to the 'surfaces' subdirectory that will be created
within the output directory. These images can be used for quickly glimpsing
through the processing results. Note that no display manager is required for
this module, i.e. it can be run on a remote server, for example.

skullstrip module

This module allows for the automated generation cross-sections of the brain
that are overlaid with the colored and semi-transparent brainmask. This allows
to check the quality of the skullstripping in FreeSurfer. The resulting images
will be saved to the 'skullstrip' subdirectory that will be created within the
output directory.

fornix module

This is a module to assess potential issues with the segmentation of the
corpus callosum, which may incorrectly include parts of the fornix. To assess
segmentation quality, a screenshot of the contours of the corpus callosum
segmentation overlaid on the norm.mgz will be saved as 'cc.png' for each
subject within the 'fornix' subdirectory of the output directory.

modules for the amygdala, hippocampus, and hypothalamus

These modules evaluate potential missegmentations of the amygdala, hippocampus,
and hypothalamus. To assess segmentation quality, screenshots will be created
These modules require prior processing of the MR images with FreeSurfer's
dedicated toolboxes for the segmentation of the amygdala and hippocampus, and
the hypothalamus, respectively.

shape module

The shape module will run a shapeDNA / brainprint analysis to compute distances
of shape descriptors between lateralized brain structures. This can be used
to identify discrepancies and irregularities between pairs of corresponding
structures. The results will be included in the main csv table, and the output
directory will also contain a 'brainprint' subdirectory.

outlier module

This is a module to detect extreme values among the subcortical ('aseg')
segmentations as well as the cortical parcellations. If present, hypothalamic
and hippocampal subsegmentations will also be included.
The outlier detection is based on comparisons with the
distributions of the sample as well as normative values taken from the
literature (see References).
For comparisons with the sample distributions, extreme values are defined in
two ways: nonparametrically, i.e. values that are 1.5 times the interquartile
range below or above the 25th or 75th percentile of the sample, respectively,
and parametrically, i.e. values that are more than 2 standard deviations above
or below the sample mean. Note that a minimum of 10 supplied subjects is
required for running these analyses, otherwise NaNs will be returned.
For comparisons with the normative values, lower and upper bounds are computed
from the 95% prediction intervals of the regression models given in Potvin et
al., 1996, and values exceeding these bounds will be flagged. As an
alternative, users may specify their own normative values by using the
'--outlier-table' argument. This requires a custom csv table with headers
label, upper, and lower, where label indicates a column of anatomical
names. It can be a subset and the order is arbitrary, but naming must exactly
match the nomenclature of the 'aseg.stats' and/or '[lr]h.aparc.stats' file.
If cortical parcellations are included in the outlier table for a comparison
with aparc.stats values, the labels must have a 'lh.' or 'rh.' prefix. file.
upper and lower are user-specified upper and lower bounds.
The main csv table will be appended with the following summary variables, and
more detailed output about will be saved as csv tables in the 'outliers'
subdirectory of the main output directory.

variable
description

n_outliers_sample_nonpar
number of structures that are 1.5 times the IQR above/below the 75th/25th percentile

n_outliers_sample_param
number of structures that are 2 SD above/below the mean

n_outliers_norms
number of structures exceeding the upper and lower bounds of the normative values

Usage
As a command line tool
run_fsqc --subjects_dir <directory> --output_dir <directory>
[--subjects SubjectID [SubjectID ...]]
[--subjects-file <file>] [--screenshots]
[--screenshots-html] [--surfaces] [--surfaces-html]
[--skullstrip] [--skullstrip-html]
[--fornix] [--fornix-html] [--hippocampus]
[--hippocampus-html] [--hippocampus-label ... ]
[--hypothalamus] [--hypothalamus-html] [--shape]
[--outlier] [--fastsurfer] [-h] [--more-help]
[...]

required arguments:
--subjects_dir <directory>
subjects directory with a set of Freesurfer- or
Fastsurfer-processed individual datasets.
--output_dir <directory>
output directory

optional arguments:
--subjects SubjectID [SubjectID ...]
list of subject IDs
--subjects-file <file> filename of a file with subject IDs (one per line)
--screenshots create screenshots of individual brains
--screenshots-html create screenshots of individual brains incl.
html summary page
--surfaces create screenshots of individual brain surfaces
--surfaces-html create screenshots of individual brain surfaces
and html summary page
--skullstrip create screenshots of individual brainmasks
--skullstrip-html create screenshots of individual brainmasks and
html summary page
--fornix check fornix segmentation
--fornix-html check fornix segmentation and create html summary
page of fornix evaluation
--hypothalamus check hypothalamic segmentation
--hypothalamus-html check hypothalamic segmentation and create html
summary page
--hippocampus check segmentation of hippocampus and amygdala
--hippocampus-html check segmentation of hippocampus and amygdala
and create html summary page
--hippocampus-label specify label for hippocampus segmentation files
(default: T1.v21). The full filename is then
[lr]h.hippoAmygLabels-<LABEL>.FSvoxelSpace.mgz
--shape run shape analysis
--outlier run outlier detection
--outlier-table specify normative values (only in conjunction with
--outlier)
--fastsurfer use FastSurfer instead of FreeSurfer output
--exit-on-error terminate the program when encountering an error;
otherwise, try to continue with the next module or
case

getting help:
-h, --help display this help message and exit
--more-help display extensive help message and exit

expert options:
--screenshots_base <image>
filename of an image that should be used instead of
norm.mgz as the base image for the screenshots. Can be
an individual file (which would not be appropriate for
multi-subject analysis) or can be a file without
pathname and with the same filename across subjects
within the 'mri' subdirectory of an individual
FreeSurfer results directory (which would be appropriate
for multi-subject analysis).
--screenshots_overlay <image>
path to an image that should be used instead of aseg.mgz
as the overlay image for the screenshots; can also be
none. Can be an individual file (which would not be
appropriate for multi-subject analysis) or can be a file
without pathname and with the same filename across
subjects within the 'mri' subdirectory of an individual
FreeSurfer results directory (which would be appropriate
for multi-subject analysis).
--screenshots_surf <surf> [<surf> ...]
one or more surface files that should be used instead
of [lr]h.white and [lr]h.pial; can also be none. Can be
one or more individual file(s) (which would not be
appropriate for multi-subject analysis) or can be a
(list of) file(s) without pathname and with the same
filename across subjects within the 'surf' subdirectory
of an individual FreeSurfer results directory (which
would be appropriate for multi-subject analysis).
--screenshots_views <view> [<view> ...]
one or more views to use for the screenshots in the form
of x=<numeric> y=<numeric> and/or z=<numeric>. Order
does not matter. Default views are x=-10 x=10 y=0 z=0.
--screenshots_layout <rows> <columns>
layout matrix for screenshot images.

Examples:

Run the QC pipeline for all subjects found in /my/subjects/directory:

run_fsqc --subjects_dir /my/subjects/directory --output_dir /my/output/directory

Run the QC pipeline for two specific subjects that need to be present in /my/subjects/directory:

run_fsqc --subjects_dir /my/subjects/directory --output_dir /my/output/directory --subjects mySubjectID1 mySubjectID2

Run the QC pipeline for all subjects found in /my/subjects/directory after full FastSurfer processing:

run_fsqc --subjects_dir /my/subjects/directory --output_dir /my/output/directory --fastsurfer

Run the QC pipeline plus the screenshots module for all subjects found in /my/subjects/directory:

run_fsqc --subjects_dir /my/subjects/directory --output_dir /my/output/directory --screenshots

Run the QC pipeline plus the fornix pipeline for all subjects found in /my/subjects/directory:

run_fsqc --subjects_dir /my/subjects/directory --output_dir /my/output/directory --fornix

Run the QC pipeline plus the shape analysis pipeline for all subjects found in /my/subjects/directory:

run_fsqc --subjects_dir /my/subjects/directory --output_dir /my/output/directory --shape

Run the QC pipeline plus the outlier detection module for all subjects found in /my/subjects/directory:

run_fsqc --subjects_dir /my/subjects/directory --output_dir /my/output/directory --outlier

Run the QC pipeline plus the outlier detection module with a user-specific table of normative values for all subjects found in /my/subjects/directory:

run_fsqc --subjects_dir /my/subjects/directory --output_dir /my/output/directory --outlier --outlier-table /my/table/with/normative/values.csv

Note that the --screenshots, --fornix, --shape, and --outlier (and other) arguments can also be used in conjunction.

As a Python package
As an alternative to their command-line usage, the fsqc scripts can also be run
within a pure Python environment, i.e. installed and imported as a Python package.
Use import fsqc (or sth. equivalent) to import the package within a
Python environment, and use the run_fsqc function from the fsqc module to
run an analysis.
In its most basic form:
import fsqc
fsqc.run_fsqc(subjects_dir='/my/subjects/dir', output_dir='/my/output/dir')

Specify subjects as a list:
import fsqc
fsqc.run_fsqc(subjects_dir='/my/subjects/dir', output_dir='/my/output/dir', subjects=['subject1', 'subject2', 'subject3'])

And as a more elaborate example:
import fsqc
fsqc.run_fsqc(subjects_dir='/my/subjects/dir', output_dir='/my/output/dir', subject_file='/my/subjects/file.txt', screenshots_html=True, surfaces_html=True, skullstrip_html=True, fornix_html=True, hypothalamus_html=True, hippocampus_html=True, hippocampus_label="T1.v21", shape=True, outlier=True)

Call help(fsqc.run_fsqc) for further usage info and additional options.

Citations

Esteban O, Birman D, Schaer M, Koyejo OO, Poldrack RA, Gorgolewski KJ; 2017;
MRIQC: Advancing the Automatic Prediction of Image Quality in MRI from Unseen
Sites; PLOS ONE 12(9):e0184661; doi:10.1371/journal.pone.0184661.

Wachinger C, Golland P, Kremen W, Fischl B, Reuter M; 2015; BrainPrint: a
Discriminative Characterization of Brain Morphology; Neuroimage: 109, 232-248;
doi:10.1016/j.neuroimage.2015.01.032.

Reuter M, Wolter FE, Shenton M, Niethammer M; 2009; Laplace-Beltrami
Eigenvalues and Topological Features of Eigenfunctions for Statistical Shape
Analysis; Computer-Aided Design: 41, 739-755; doi:10.1016/j.cad.2009.02.007.

Potvin O, Mouiha A, Dieumegarde L, Duchesne S, & Alzheimer's Disease
Neuroimaging Initiative; 2016; Normative data for subcortical regional volumes
over the lifetime of the adult human brain; Neuroimage: 137, 9-20; doi.org/10.1016/j.neuroimage.2016.05.016