RadQy is a quality assurance and evaluation platform for MRI and CT imaging datasets. It extracts metadata, computes image quality metrics, generates scan thumbnails, supports foreground/background mask generation, and provides an interactive front-end interface for visual quality review, cohort exploration, and LLM-assisted reporting and dataset interpretation.

Full demo video: https://youtu.be/pzJUZovFlT0?si=PcMnU4Dhdkh_qTX8

• MRI and CT quality assessment
• DICOM metadata/tag extraction
• Image quality metric computation
• Thumbnail generation for participant-level review
• Optional foreground/background mask export
• Interactive front-end visualization
• Parallel coordinate plots for quality metric exploration
• Subject/table filtering and cohort review
• Support for multiple segmentation options including otsuhull, adaptiveborder, and unet

RadQy/
├── backend/          # Processing, IQM, segmentation, and CLI logic
├── frontend/         # Web-based RadQy interface
├── docs/             # Documentation and figures
├── tests/            # Test files
├── tools/            # Utility scripts
├── README.md
├── requirements.txt
└── pyproject.toml

Clone the repository:

git clone https://github.com/viswanath-lab/RadQy.git
cd RadQy

Create an environment:

conda create -n radqy python=3.10
conda activate radqy

Install requirements:

pip install -r requirements.txt

Basic command:

python backend/main.py --inputdir "PATH_TO_DICOM_DATASET" --scantype mri

For CT data:

python backend/main.py --inputdir "PATH_TO_DICOM_DATASET" --scantype ct

Example with options:

python backend/main.py ^
  --inputdir "E:\Data\Rectal\input_data_folder" ^
  --scantype mri ^
  --middle-percent 50 ^
  --num-samples 2 ^
  --save-fgbg ^
  --segmenter otsuhull

--inputdir          Root folder containing participant DICOM files
--scantype          Scan type: mri or ct
--middle-percent    Percent of middle slices to process
--num-samples       Slice sampling stride
--save-fgbg         Save foreground/background masks
--segmenter         Segmentation method: adaptiveborder, otsuhull, regiongrowing, unet, fcn, mobilenet
--verbose           Print detailed per-file processing progress

RadQy creates an output folder under:

frontend/Data/<dataset_name>/

The main output file is:

results.tsv

The output includes:

• participant-level metadata
• number of slices
• extracted DICOM tags
• image quality metrics
• thumbnail image filenames
• optional foreground/background mask outputs

After generating results.tsv, open the RadQy front-end from the frontend folder and load the generated dataset output.

Please report and issues, bugfixes, ideas for enhancements via the "Issues" tab.

Detailed usage instructions and an example of using MRQy to analyze TCIA datasets are in the Wiki.

You can cite this in any associated publication as:
Sadri, AR, Janowczyk, A, Zou, 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, 47: 6029-6038. https://doi.org/10.1002/mp.14593

ArXiv: https://arxiv.org/abs/2004.04871

If you do use the tool in your own work, please drop us a line to let us know.