FetMRQC SR

FetMRQC SR is the super-resolution extension of FetMRQC [paper1,paper2] is a tool for quality assessment (QA) and quality control (QC) of T2-weighted (T2w) fetal brain MR images.

It builds on top of the utilities developed in the FetMRQC repository, a tool for the QC of low-resolution T2w scans.

It contains the tools needed

It consists of two parts.

1. A rating interface (visual report) to standardize and facilitate quality annotations of T2w fetal brain MRI images, by creating interactive HTML-based visual reports from fetal brain scans. It uses a pair of low-resolution (LR) T2w images with corresponding brain masks to provide snapshots of the brain in the three orientations of the acquisition in the subject-space.
2. A QA/QC model that can predict the quality of given super-resolution reconstructed volumes.

Given a list of SRR images listed using qc_list_bids, it then uses srqc_segmentation to compute the segmentations using BOUNTI [1] and extracts image quality metrics (IQMs) using srqc_compute_iqms. These IQMs can then be transformed in FetMRQC SR predictions using srqc_inference.

If you have found this useful in your research, please cite

Thomas Sanchez, Vladyslav Zalevskyi, Angeline Mihailov, Gerard Martí-Juan, Elisenda Eixarch, Andras Jakab, Vincent Dunet, Mériam Koob, Guillaume Auzias, Meritxell Bach Cuadra. (2025) Automatic quality control in multi-centric fetal brain MRI super-resolution reconstruction. arXiv preprint arXiv:2503.10156

Installing FetMRQC_SR

To install FetMRQC SR, just create a new conda environment with python 3.9.0

conda create --name fetmrqc_sr python=3.9.0

Then, simply activate the environment and install fetmrqc_sr and its dependencies by running pip install -e .

Generating reports for manual QC

After installing fetmrqc_sr, you will need to follow these steps to generate manual QC reports.

1. Given a BIDS-formatted dataset, get a CSV list of the data with qc_list_bids (use --help to see the detail). You will need to use the option --skip_masks.
2. Once you have your csv file, you can generate the visual reports for manual annotations using

qc_generate_reports --bids_csv <csv_path> --out_dir <output_directory> --sr

3. You can then run qc_generate_index to generate an index file to easily navigate the reports. After intalling fetmrqc_sr, you will need to generate a csv file with a name and an im column listing the path to the SRR volumes for which you want to generate the reports. If you do not have such a CSV, you can generate it using qc_list_bids (use --help to see the detail). If you do, please use the --skip_masks argument.

Once you have your csv file, you can generate the reports using qc_generate_reports --bids_csv <csv_path> --out_dir <output_directory> --sr

Finally, you can run qc_generate_index to generate an index file to easily navigate the reports.

Custom model training using FetMRQC SR

Once your manual ratings are done, you then train a custom QC model as follows.

1. Get back a CSV file using qc_ratings_to_csv in the folder where your ratings are stored.
2. Compute brain segmentations using srqc_segmentation and IQMs using srqc_compute_iqms.
3. Train your custom models using the manual ratings with automatically extracted IQMs using srqc_train_model.

References

[1] Uus, Alena U., et al. "BOUNTI: Brain vOlumetry and aUtomated parcellatioN for 3D feTal MRI." bioRxiv (2023).

License

Copyright 2025 Medical Image Analysis Laboratory.

Acknowledgements

This project was supported by the ERA-net Neuron MULTIFACT – SNSF grant 31NE30_203977.