Extensive information can be found in the Quality Assessment Protocol webpage.
Various objective measures for MRI data quality have been proposed over the years. However, until now no software has allowed researchers to obtain all these measures in the same place with relative ease. The Quality Assessment Protocol package allows you to obtain spatial and anatomical data quality measures for your own data. Reports can optionally be generated that provide a variety of data visualizations to aid with quality assessment.
This container calculates dataset level quality assessment metrics of structural and functional MRI data when run in "participant" mode and compiles the outputs of many different participants outputs into single CSV files when run in "group" mode.
This App has the following command line arguments:
usage: run.py [-h]
[--participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]]
[--pipeline_file PIPELINE_FILE] [--n_cpus N_CPUS] [--mem MEM]
[--save_working_dir] [--report]
bids_dir output_dir {participant,group}
PCP-QAP Pipeline Runner
positional arguments:
bids_dir The directory with the input dataset formatted
according to the BIDS standard.
output_dir The directory where the output CSV files should be
stored.
{participant,group} Level of the analysis that will be performed. Multiple
participant level analyses can be run independently
(in parallel) using the same output_dir. Group level
analysis compiles multiple participant level quality
metrics intogroup-level csv files.
optional arguments:
-h, --help show this help message and exit
--participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]
The label of the participant that should be analyzed.
The label corresponds to sub-<participant_label> from
the BIDS spec (so it does not include "sub-"). If this
parameter is not provided all subjects should be
analyzed. Multiple participants can be specified with
a space separated list.
--pipeline_file PIPELINE_FILE
Name for the pipeline configuration file to use, uses
a default configuration if not specified
--n_cpus N_CPUS Number of execution resources available for the
pipeline, default=1
--mem MEM Amount of RAM available to the pipeline in GB, default
= 6
--save_working_dir Save the contents of the working directory.
--report Generates pdf for graphically assessing data quality.
To run it in participant level mode (for one participant):
docker run -i --rm \
-v /Users/filo/data/ds005:/bids_dataset \
-v /Users/filo/outputs:/outputs \
bids-apps/qap:v1.0.0 \
/bids_dataset /outputs --participant_label 01 participant
To run all subjects and generate the group level analysis report:
docker run -i --rm \
-v /Users/filo/data/ds005:/bids_dataset \
-v /Users/filo/outputs:/outputs \
bids-apps/qap:v1.0.0 \
/bids_dataset /outputs group
Please report errors on the QAP github page issue tracker. Please use the PCP google group for help using C-PAC and this application.
We currently have a publication in preparation, in the meantime please cite our poster from INCF:
Shehzad Z, Giavasis S, Li Q, Benhajali Y, Yan C, Yang Z, Milham M, Bellec P
and Craddock C (2015). The Preprocessed Connectomes Project Quality
Assessment Protocol - a resource for measuring the quality of MRI data..
Front. Neurosci. Conference Abstract: Neuroinformatics 2015. doi:
10.3389/conf.fnins.2015.91.00047
@ARTICLE{shehzad2015,
AUTHOR={Shehzad, Zarrar and Giavasis, Steven and Li, Qingyang and
Benhajali, Yassine and Yan, Chaogan and Yang, Zhen and Milham,
Michael and Bellec, Pierre and Craddock, Cameron},
TITLE={The Preprocessed Connectomes Project Quality Assessment Protocol
- a resource for measuring the quality of MRI data.},
JOURNAL={Frontiers in Neuroscience},
VOLUME={},
YEAR={},
NUMBER={47},
URL={http://www.frontiersin.org/neuroscience/10.3389/conf.fnins.2015.91.00047/full},
DOI={10.3389/conf.fnins.2015.91.00047},
ISSN={1662-453X} ,
ABSTRACT={}
}