QAnT : image Quality Assessment aNd dicom Tags extraction

QAnT extracts no-reference IQMs (image quality metrics) representing noise/information measurements as well as DICOM metadata (Tags).

The initial goal of this project was to extract IQMs and dicom tags from structural MRI images in order to automatically determine a site/scanners effect of the images.

Installation

1. Create a conda environment (recommended)

ENVNAME="QAnT"
conda create -n $ENVNAME python==3.7.7 -y
conda activate $ENVNAME

2. Install repository

Method 1: Github Master Branch

pip install git+https://github.com/Alxaline/QAnT.git

Method 2: Development Installation

git clone https://github.com/Alxaline/QAnT.git
cd QAnT
pip install -e .

Documentation

https://comscan.readthedocs.io/en/latest/

Usage

Extraction

This tool takes datasets in the file formats (.dcm, .nii, .nii.gz) as the input. To parse DICOM files, the script need to have dicom series in an independent folder, i.e. a unique folder for a volume with all .dcm slices inside.

You need to provide a parameter file for extraction. An example is available in QAnT/example_parameters/default_parameters.yaml

The tool is multi-process in order to speed up the extraction process.

You can directly use the cli:

qant-extractor [-h] -i INPUT_DIR [INPUT_DIR ...] -o OUTPUT_FILEPATH [-p PARAM] [-j N] [-v]

or in python mode:

usage: python -m QAnT.extractor [-h] -i INPUT_DIR [INPUT_DIR ...] -o OUTPUT_FILEPATH
                    [-p PARAM] [-j N] [-v]

QAnT: image Quality Assessment and dicom Tags extraction

optional arguments:
  -h, --help            show this help message and exit

Required:
  -i INPUT_DIR [INPUT_DIR ...], --input_dir INPUT_DIR [INPUT_DIR ...]
                        Input directories path with DICOM files to be parsed.
                        Can be a list of directory
  -o OUTPUT_FILEPATH, --output_filepath OUTPUT_FILEPATH
                        Output filepath for saving the content in csv files.
                        Need to have the .csv extensions
  -p PARAM, --param PARAM
                        Parameter file containing the settings to be used in
                        extraction. If not provided use default setting.
  --inclusion_keywords INCLUSION_KEYWORDS [INCLUSION_KEYWORDS ...]
                        Inclusion keywords to parse files. fnmatch style, i.e
                        ['a*', 'b*']
  --exclusion_keywords EXCLUSION_KEYWORDS [EXCLUSION_KEYWORDS ...]
                        Exclusion keywords to parse files. fnmatch style, i.e
                        ['a*', 'b*']

Options:
  -j N, --n_jobs N      Specifies the number of threads to use for parallel
                        processing (default: all)
  -v, --verbosity       increase output verbosity (e.g., -vv is more than -v)

Visualize

You can visualize results csv file in the application interface.

You can directly use the cli:

qant-interface

or in python mode:

usage: python -m QAnT.interface

How to cite ?

If you find this repository useful for your research, please cite our work:

Carré, A., Battistella, E., Niyoteka, S. et al. AutoComBat: a generic method for harmonizing MRI-based radiomic features. Sci Rep 12, 12762 (2022). https://doi.org/10.1038/s41598-022-16609-1

BibTeX:

@article{carreAutoComBatGenericMethod2022,
    title = {AutoComBat: a generic method for harmonizing MRI-based radiomic features},
    volume = {12},
    issn = {2045-2322},
    url = {https://www.nature.com/articles/s41598-022-16609-1},
    doi = {10.1038/s41598-022-16609-1},
    language = {en},
    number = {1},
    urldate = {2022-07-27},
    journal = {Scientific Reports},
    author = {Carré, Alexandre and Battistella, Enzo and Niyoteka, Stephane and Sun, Roger and Deutsch, Eric and Robert, Charlotte},
    year = {2022},
    keywords = {Cancer imaging, Computational science, Tumour biomarkers},
    pages = {12762},
}

Disclaimer

Based on: MRQy

Sadri AR, Janowczyk A, Zhou R, Verma R, Beig N, Antunes J, Madabhushi A, Tiwari P, Viswanath SE. Technical Note: MRQy - An open-source tool for quality control of MR imaging data. Med Phys. 2020 Dec;47(12):6029-6038. doi: 10.1002/mp.14593.