# Automatic quality control of brain T1-weighted magnetic resonance images for a clinical data warehouse

arXiv: https://arxiv.org/abs/2104.08131 · HTML: https://mlsanity.com/paper/2104.08131/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 62 references that are in the map.
- First posted on arXiv 16 Apr 2021.

## Builds on (most cited first, 9 of 9)

- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation](https://mlsanity.com/paper/1904.07773/) (2019)
- [Real-time Prediction of Segmentation Quality](https://mlsanity.com/paper/1806.06244/) (2018)
- [Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning](https://mlsanity.com/paper/1810.12185/) (2018)
- [Reproducible evaluation of classification methods in Alzheimer's disease: framework and application to MRI and PET data](https://mlsanity.com/paper/1808.06452/) (2018)
- [MRQy: An Open-Source Tool for Quality Control of MR Imaging Data](https://mlsanity.com/paper/2004.04871/) (2020)
- [A Machine-learning framework for automatic reference-free quality assessment in MRI](https://mlsanity.com/paper/1806.09602/) (2018)
- [Neuroimaging Modality Fusion in Alzheimer's Classification Using Convolutional Neural Networks](https://mlsanity.com/paper/1811.05105/) (2018)
- [Accuracy of MRI Classification Algorithms in a Tertiary Memory Center Clinical Routine Cohort](https://mlsanity.com/paper/2003.09260/) (2020)

## Built on by (most cited first, 8 of 8)

- [Reproducibility in machine learning for medical imaging](https://mlsanity.com/paper/2209.05097/) (2022)
- [Improving Uncertainty-based Out-of-Distribution Detection for Medical Image Segmentation](https://mlsanity.com/paper/2211.05421/) (2022)
- [Multi-layer Aggregation as a key to feature-based OOD detection](https://mlsanity.com/paper/2307.15647/) (2023)
- [An efficient semi-supervised quality control system trained using physics-based MRI-artefact generators and adversarial training](https://mlsanity.com/paper/2206.03359/) (2022)
- [HAMIL-QA: Hierarchical Approach to Multiple Instance Learning for Atrial LGE MRI Quality Assessment](https://mlsanity.com/paper/2407.07254/) (2024)
- [Large-Scale Label Quality Assessment for Medical Segmentation via a Vision-Language Judge and Synthetic Data](https://mlsanity.com/paper/2601.14406/) (2026)
- [Automated MRI Quality Assessment of Brain T1-weighted MRI in Clinical Data Warehouses: A Transfer Learning Approach Relying on Artefact Simulation](https://mlsanity.com/paper/2406.12448/) (2024)
- [Improving Quality Control Of MRI Images Using Synthetic Motion Data](https://mlsanity.com/paper/2502.00160/) (2025)

## Abstract (arXiv)

Many studies on machine learning (ML) for computer-aided diagnosis have so far been mostly restricted to high-quality research data. Clinical data warehouses, gathering routine examinations from hospitals, offer great promises for training and validation of ML models in a realistic setting. However, the use of such clinical data warehouses requires quality control (QC) tools. Visual QC by experts is time-consuming and does not scale to large datasets. In this paper, we propose a convolutional neural network (CNN) for the automatic QC of 3D T1-weighted brain MRI for a large heterogeneous clinical data warehouse. To that purpose, we used the data warehouse of the hospitals of the Greater Paris area (Assistance Publique-Hopitaux de Paris [AP-HP]). Specifically, the objectives were: 1) to identify images which are not proper T1-weighted brain MRIs; 2) to identify acquisitions for which gadolinium was injected; 3) to rate the overall image quality. We used 5000 images for training and validation and a separate set of 500 images for testing. In order to train/validate the CNN, the data were annotated by two trained raters according to a visual QC protocol that we specifically designed for application in the setting of a data warehouse. For objectives 1 and 2, our approach achieved excellent accuracy (balanced accuracy and F1-score textgreater 90%), similar to the human raters. For objective 3, the performance was good but substantially lower than that of human raters. Nevertheless, the automatic approach accurately identified (balanced accuracy and F1-score textgreater 80%) low quality images, which would typically need to be excluded. Overall, our approach shall be useful for exploiting hospital data warehouses in medical image computing.