# Assessing the Efficacy of Artefact Synthesis and Transfer Learning for Quality Control in Clinical 3D FLAIR Brain MRI

Manon Heffernan, Sophie Loizillon, Sebastian Ströer, Lydia Chougar, Didier Dormont, Olivier Colliot, Ninon Burgos

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Manon Heffernan, Sophie Loizillon, Sebastian Ströer, Lydia Chougar, Didier Dormont, et al. Assessing the Efficacy of Artefact Synthesis and Transfer Learning for Quality Control in Clinical 3D FLAIR Brain MRI. SPIE Medical Imaging 2026, Feb 2026, Vancouver, Canada. hal-05444176v2

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Assessing the Efficacy of Artefact Synthesis and Transfer
Learning for Quality Control in Clinical 3D FLAIR Brain
MRI

Manon Heffernan<sup>a</sup>, Sophie Loizillon<sup>a</sup>, Sebastian Ströer<sup>b</sup>, Lydia Chougar<sup>a</sup>, Didier Dormont<sup>a,b</sup>,
Olivier Colliot<sup>a</sup>, and Ninon Burgos<sup>a</sup>

<sup>a</sup>Sorbonne Université, Institut du Cerveau – Paris Brain Institute, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France

<sup>b</sup>AP-HP, Hôpital Pitié-Salpêtrière, DMU DIAMENT, Dep. of Neuroradiology, Paris, France

ABSTRACT

The recent advent of clinical data warehouses (CDWs) has greatly simplified the sharing of large medical datasets, including imaging, for research purposes. MRI scans can be affected by various artefacts such as motion, noise and low grey/white matter contrast, which can degrade image quality. Given the abundance of MRI scans available in CDWs, it is imperative to develop tools to automatically detect images corrupted by these artefacts, in order to guarantee the reliability of the data used for research. We have previously developed a method for detecting moderate artefacts in 3D T1-weighted brain MRI. We propose to test and extend our approach to the FLAIR sequence. Our approach involves pretraining models on research data augmented with synthetic artefacts, followed by a fine-tuning phase designed to adapt these models to routine clinical data, either keeping the clinical data unchanged, or balancing the classes by simulating artefacts on some of the artefact-free MRIs. This adaptation relies on the manual annotation of 637 FLAIR images. We evaluate whether this strategy can generalise to new MRI sequences and clinical settings where manual labels are limited. While leveraging synthetic artefacts and research datasets shows some benefit, the performance gains remain modest compared to models trained from scratch on clinical data, and the results fall short of human-level accuracy. A key limitation remains the scarcity of representative, annotated clinical images, which constrains overall performance.

**Keywords:** Magnetic Resonance Imaging, Quality Control, Clinical Data Warehouse, Noise, Contrast, Motion

# 1. INTRODUCTION

Clinical data warehouses (CDWs) gather medical images from thousands to millions of patients, offering an exceptional opportunity to develop computational tools.¹ Unlike research datasets that follow standardised acquisition protocols, images from CDWs are highly heterogeneous, originating from different hospitals, spanning multiple decades, and acquired using various machines without any homogenisation.

Magnetic resonance images (MRIs) can present different types of artefacts, such as noise, poor contrast, motion or ghosting artefacts² that can greatly impact the overall quality. Quality control (QC) tools are therefore fundamental to identify images of sufficient quality, but also to improve the analysis of performance of downstream tasks that can be impacted by image quality. Significant effort has been invested in creating QC tools for T1-weighted (T1w) images,³⁻⁵ and as part of this effort, we previously developed a transfer learning approach for automatic detection of contrast, noise and motion artefacts on 3D-T1w brain MRIs.⁶,⁷ However, fewer work has focused on the Fluid Attenuated Inversion Recovery (FLAIR) sequence,⁸⁻¹⁰ despite its relevance for the detection of white matter lesions in diverse disorders.¹¹,¹² In this paper, we propose to test and extend our previous transfer learning approach on artefact detection to a new MRI sequence, the 3D-FLAIR, focusing on noise and motion artefacts as well as poor contrast. Our method relies on pretraining three artefact-specific convolutional neural network (CNN) classifiers using research data corrupted with synthetic artefacts, followed by a fine-tuning step with 300 manually annotated MRIs from a CDW. We evaluated different intensities of synthetic artefacts during pretraining and simulated synthetic artefacts on the clinical data to address class imbalance issues.

Send correspondence to Manon Heffernan (manon.heffernan@gmail.com) and Ninon Burgos (ninon.burgos@cnrs.fr).
<figure><img src="image_2.png" /><figcaption>Figure 1. Examples of scans from the CDW presenting different moderate artefacts. For poor contrast, the contrast in the parietal lobes is low, while the contrast in the temporal lobes still allows for good distinction of the structures. Motion on the central image induces blurriness, but the structures are still visible and distinguishable. For the last image, noise is present especially at the centre of the brain, however its impact on structures boundaries is limited.</figcaption></figure>

## 2. MATERIALS AND METHODS

### 2.1 Dataset description

Six research-oriented datasets were used to pretrain our models, and one routine clinical dataset was used for the fine-tuning and validation of the method.

**Clinical dataset** The routine clinical data comes from a large CDW containing all the 3D-FLAIR brain MRIs acquired in hospitals of the Greater Paris area (Assistance Publique-Hôpitaux de Paris). We used the same dataset as in 10, extracted from a pool of images acquired on various scanners (Siemens, GE and Philips). 926 3D-FLAIR were manually graded by two annotators on a three-point scale for motion, noise and poor contrast. Noise was scored as (0) for no noise, (1) noise present but not hindering structure identification, and (2) severe noise preventing structure identification. Contrast was graded as (0) for good contrast, (1) when grey and white matter were challenging to distinguish in certain regions, and (2) when the distinction between grey and white matter was difficult throughout the entire brain. Motion was scored as (0) for no motion, (1) for distinguishable structures despite motion, and (2) for structures difficult to distinguish. In case of disagreement on the labels, both annotators re-evaluated the images together to reach a consensus. Some images did not correspond to 3D-FLAIR brain MRIs and were left unlabelled (*straight reject*);⁴ they are not considered in this study. Our 3D-FLAIR clinical dataset surpasses the quality of our previous T1w MRI dataset,¹⁰ likely due to the more recent adoption of 3D scans for FLAIR sequences than T1w in Greater Paris hospitals. We focused on data of good and moderate quality (classes 0 and 1), accounting for 637 MRIs. Figure 1 presents examples of manually annotated 3D-FLAIR MRI from the CDW, presenting moderate artefacts (poor contrast, motion or noise).

**Research-oriented datasets** We aimed to include datasets of different pathologies to increase the diversity of the research cohort. We used data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study,¹³ which involves elderly individuals with normal cognition, mild cognitive impairment, or Alzheimer's disease. The ADNI3 and 4 phases include more than 1500 3D-FLAIR MRI scans acquired on 3T scanners from different manufacturers.¹⁴ The MSSEG¹⁵ and MSSEG2¹⁶ MICCAI challenges provide datasets of 3D-FLAIR scans with segmentations of multiple sclerosis lesions. They include respectively, 53 patients acquired at four different sites and 100 patients acquired on 15 different scanners. The MPI-Leipzig Mind-Brain-Body dataset contains MRI and behavioural data from 318 participants.¹⁷ The data set from the study "Utilizing Amide Proton Transfer Technique to Characterize Diffuse Gliomas Based on WHO 2021 Classification of CNS Tumors" includes 42 adult participants with diagnosed diffuse brain gliomas.¹⁸ The UK BioBank (UKB) cohort includes over 100,000 participants with a brain MRI.¹⁹ The MRIs were acquired on different sites on a Siemens Skyra 3T scanner, with a standardised acquisition protocol. We extracted a random subset of 3000 images from the UKB, stratified by sex and age without considering pathology. The following automated QC preselection was performed to select images with good contrast. First, the normalised difference between white and grey matter (ND-WGM = |*I*<sub>GM</sub> − *I*<sub>WM*</sub>|/*|*I*<sub>GM</sub> + *I*<sub>WM*</sub>|*) was computed for each scan from the CDW relying on segmentation maps obtained using Synthseg.²⁰ Then, the ND-WGM threshold that allows for a good separation of images of
<figure><img src="image_3.png" /><figcaption>Figure 2. Overview of the transfer learning pipeline for automatic quality control of clinical routine 3D FLAIR brain MRI. The approach involves pretraining models on artefact-free research data augmented with synthetic artefacts, followed by a fine-tuning phase using routine clinical data manually annotated.</figcaption></figure>

good and moderate contrast quality on the CDW was used to select scans from the UKB with sufficiently good contrast. For all other research datasets, additional manual QC was performed to ensure the absence of head motion and noise artefacts.

## 2.2 Image preprocessing

All 3D-FLAIR MRIs were pre-processed using the **flair-linear** pipeline from the Clinica software.²¹ First, bias field correction was performed using the N4ITK method,²² followed by an affine registration to the MNI space.²³ Then, the background was removed by cropping the images to size 169×208×179, with 1 mm isotropic voxels.²⁴ Finally, we performed a percentile normalisation using the 1st and 99th percentile on the voxel intensities.

## 2.3 Proposed approach

Using the same transfer learning framework as in our previous studies on T1w MRIs,⁶,⁷ we evaluate the method's ability to detect moderate levels of noise, motion, and poor contrast in clinical 3D-FLAIR images. We pretrained three CNNs on diverse research-oriented datasets corrupted with synthetic artefacts, either by reusing the parameters from our T1w-based artefact simulation approach⁶ or by simulating artefacts matching the severity observed in CDW data. We then fine-tuned the models on a set of 328 manually labelled clinical scans, either using the original scans or by simulating artefacts on class 0 images from the fine-tuning set, for class balancing. The fine-tuned models were finally applied to an independent test set of 309 3D-FLAIR MRIs from the CDW. The proposed approach is summarised in Figure 2.

### 2.3.1 Artefact simulation

We simulated three common artefacts on research 3D-FLAIR scans: noise, head motion, and poor contrast (Figure 3). All artefacts were generated using the Python library TorchIO.²⁵ To simulate noise, we relied on the `RandomNoise` function to approximate intrinsic MRI noise by a Gaussian distribution. The standard deviation was sampled from a uniform distribution each time. Head motion is well approximated by a rigid body motion with six degrees of freedom.²⁶ Thus, head motion artefacts were simulated using the `RandomMotion` function,
<figure><img src="image_4.png" /><figcaption>Figure 3. Comparison of artefact intensities for the two explored approaches. Each row compares the ground truth (GT) with the lowest (low) and highest (high) simulated artefact intensities. Top row shows intensity ranges for the realistic approach, where simulated intensity ranges visually match the intensity observed on the CDW. Bottom row shows artefacts intensity ranges based off the parameters from our T1w-based artefact simulation approach.⁶</figcaption></figure>

enabling translations and rotations within a defined range. Finally, we used the `RandomGamma` function to simulate poor contrast between grey and white matter. It applies a non-linear gamma correction to each image *I*, producing $I_c(\beta) = I^{1/e^\beta}$. The parameter *β*, sampled from a uniform distribution, controls the correction intensity.

## 2.3.2 Artefact quantification and selection of generation parameters

Given the high quality of our clinical dataset, we focused solely on detecting moderate artefacts. However, only a small proportion of our routine scans exhibit artefacts, mostly labelled as having moderate noise or poor contrast. This limitation led us to generate highly realistic synthetic artefacts to enhance our fine-tuning capabilities.

For T1-weighted images, we selected ranges for the noise parameter *σ* and the contrast parameter *β* based on distribution matching. We compared the SNR and ND-WGM distributions from the CDW with those from images corrupted with varying levels of artefact severity.⁶ On our 3D-FLAIR scans, while the ND-WGM distributions sufficiently differentiated between good and moderate poor contrast, the SNR distributions were similar despite visible noise differences. Various computation methods, including focusing on grey and white matter SNR, did not resolve this issue. Inconsistencies in the correlation between SNR and visual image quality assessments across different scanner models further complicated matters (see Figure 4). These inconsistencies may stem from different scanner architectures and methods, such as parallel imaging or reconstruction filters, which affect noise distribution and SNR measurement.²⁷ Non-linear transformations at the scanner level or during preprocessing, like bias field correction, could also play a role.²⁸

To address this, we manually defined the parameters by preselecting a broad range for *σ* covering low to severe noise artefacts. We corrupted a subset of our pretraining data with these noise levels and had annotators grade them, allowing us to define a range for *σ* that matches our grading criteria and provides diversity in noise intensity.

For motion artefacts, there is a lack of robust motion quantification metric in MRIs.²⁹ We investigated the Average Edge Strength and Tenengrad measure but they did not significantly correlate with our manual annotations, likely due to their sensitivity to contrast and preprocessing methods.³⁰ Parameters from our previous study on T1w scans produced motion artefacts too strong compared to the clinical FLAIR MRIs. Thus, we opted for generating transformations with very small ranges to mimic subtle movements during the exam. The translation and rotation ranges were determined through visual assessment of images corrupted with varied head motion severity.

## 2.3.3 Pretraining on research data with synthetic artefacts

To detect the three artefacts described above, we first pretrained three 3D CNNs, comprising five convolutional blocks and three fully connected layers,⁶ using the research-oriented datasets. We corrupted 1636 artefact-
<figure><img src="image_5.png" /><figcaption>Figure 4. Boxplot of the SNR distributions for the main scanner models and noise classes on the FLAIR cohort on the CDW. *noise_0*: no artefact, *noise_1*: moderate noise.</figcaption></figure>

free 3D-FLAIR MRI scans with synthetic artefacts. Gaussian noise was generated with standard deviation $\sigma$ in [0.02, 0.05]. Head motion was simulated using eight transforms, rotation in [0.5°, 1°] and translation in [0.5 mm, 1 mm]. Poor contrast was induced with a $\beta$ in [-0.3, -0.1]. To ensure robustness to artefact combinations, we generated all possible corrupted versions per image, including one, two, or all three artefacts. Each model was trained with negatives for a given artefact (e.g. noise) possibly containing other artefacts (e.g. motion, poor contrast, or both). This resulted in a training dataset of 13,088 images. To increase training data variability, we applied TorchIO's *RandomHorizontalFlip*. We used 5-fold cross-validation at the subject level for each research dataset to prevent data leakage, selecting the best model based on the balanced accuracy on the validation set.

### 2.3.4 Fine-tuning on routine clinical data

After training the three artefact-specific networks on the synthetically corrupted research database, we fine-tuned our models by training only the fully connected layers, keeping all other weights frozen. As the classes are imbalanced and the artefacts under-represented in the CDW, see the label distribution of the 637 3D-FLAIR images manually annotated in Table 1, we explored two strategies. We either kept the clinical data unchanged, or we balanced the classes by simulating artefacts on some of the artefact-free MRIs from the fine-tuning set. The methods and parameter values used for generating these artefacts are as described in Section 2.3.3.

<table><caption>Table 1. Noise, head motion and poor contrast label distribution for the manually annotated 3D-FLAIR of the CDW.</caption><thead><tr><th rowspan="2"></th><th colspan="2">Noise</th><th colspan="2">Motion</th><th colspan="2">Contrast</th></tr><tr><th>noise_0</th><th>noise_1</th><th>mov_0</th><th>mov_1</th><th>cont_0</th><th>cont_1</th></tr></thead><tbody><tr><th>Moderate artefact detection</th><td>519</td><td>118</td><td>608</td><td>29</td><td>450</td><td>187</td></tr><tr><th>N° of MRIs</th><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

### 2.3.5 Network implementation and optimisation details

The 3D CNNs, referred to as Conv5FC3, are composed of five convolutional blocks and three fully connected layers. Each convolutional block is made of a convolutional layer, a batch normalisation layer, a ReLU activation function and a max pooling layer. Each Conv5FC3 is designed to detect one type of artefact among noise, head motion and poor contrast. We used the Adam optimiser, the weighted binary cross-entropy loss and a batch size of 8. Models were pretrained for 100 epochs with a weight decay and learning rate of 1e-5. The fine-tuning was done on 50 epochs with the same parameters as for the pretraining. Our implementation relies on the Pytorch library and the ClinicaDL software.31,32

## 3. RESULTS

Table 2 presents results obtained with our different approaches on our independent clinical test set of 309 3D-FLAIR MRIs. There is no significant performance gain for detecting moderate noise, however all models reach levels close to that of manual annotators (85.7%), with balanced accuracies over 81%. Contrast detection achieves on average a balanced accuracy of 70%, well below the annotator accuracy of 84.6%. Regarding motion, fine-tuning with the T1w parameters reaches 67%, improving balanced accuracy by 5.7 percentage points over the
<table><caption>Table 2. Detection of noise, motion and poor contrast artefacts on the CDW. We report the agreement between human raters and the consensus (manual annotators), the baseline (training from scratch on CDW), fine-tuning with pretraining simulation parameters from our T1w study⁶ (Fine-tuning (T1 params)), fine-tuning with more realistic pretraining synthetic artefacts (Fine-tuning (realistic)), and artificial class balancing on the CDW. The table shows the average ± standard deviation over splits, with 95% confidence intervals (via bootstrapping on the test set) shown in brackets.</caption><thead><tr><th></th><th></th><th>Balanced Accuracy</th><th>Specificity</th><th>Sensitivity</th></tr></thead><tbody><tr><td rowspan="5">Noise</td><td>Manual annotators</td><td>85.73</td><td>-</td><td>-</td></tr><tr><td>Training from scratch</td><td>82.77 ± 0.89 [81.97, 83.47]</td><td><strong>86.67</strong> ± 10.54 [77.33, 95.67]</td><td>78.88 ± 9.17 [70.84, 86.91]</td></tr><tr><td>Fine-tuning (T1 params)</td><td>79.51 ± 2.86 [77.01, 82.15]</td><td>75.71 ± 6.14 [70.71, 81.43]</td><td>83.31 ± 3.79 [80.72, 87.19]</td></tr><tr><td>Fine-tuning (realistic)</td><td><strong>83.46</strong> ± 1.12 [82.53, 84.49]</td><td>82.33 ± 4.03 [78.67, 85.67]</td><td>84.58 ± 2.39 [82.49, 86.67]</td></tr><tr><td>Artificial class balancing</td><td>82.41 ± 2.26 [80.49, 84.33]</td><td>75.67 ± 7.93 [68.67, 82.33]</td><td><strong>89.16</strong> ± 3.6 [85.86, 92.13]</td></tr><tr><td rowspan="5">Contrast</td><td>Manual annotators</td><td>84.65</td><td>-</td><td>-</td></tr><tr><td>Training from scratch</td><td>70.44 ± 2.08 [68.71, 72.16]</td><td>64.74 ± 6.2 [59.38, 70.52]</td><td>76.13 ± 4.93 [71.51, 79.91]</td></tr><tr><td>Fine-tuning (T1 params)</td><td>69.09 ± 2.47 [66.88, 71.29]</td><td>61.43 ± 11.5 [51.07, 71.07]</td><td><strong>76.76</strong> ± 7.48 [69.73, 82.7]</td></tr><tr><td>Fine-tuning (realistic)</td><td><strong>71.13</strong> ± 1.9 [69.2, 72.46]</td><td>67.84 ± 8.02 [61.03, 74.64]</td><td>74.43 ± 5.64 [69.53, 79.25]</td></tr><tr><td>Artificial class balancing</td><td>70.04 ± 1.61 [68.86, 71.71]</td><td><strong>69.9</strong> ± 6.6 [64.33, 75.67]</td><td>70.19 ± 4.99 [65.66, 74.34]</td></tr><tr><td rowspan="5">Motion</td><td>Manual annotators</td><td>90</td><td>-</td><td>-</td></tr><tr><td>Training from scratch</td><td>61.6 ± 7.97 [55.01, 68.18]</td><td><strong>55.0</strong> ± 32.69 [26.25, 83.75]</td><td>68.19 ± 37.17 [31.26, 95.9]</td></tr><tr><td>Fine-tuning (T1 params)</td><td><strong>66.92</strong> ± 4.96 [62.37, 70.88]</td><td>40.0 ± 12.33 [29.09, 49.09]</td><td>93.85 ± 4.54 [89.49, 97.31]</td></tr><tr><td>Fine-tuning (realistic)</td><td>59.99 ± 5.84 [55.51, 65.74]</td><td>26.25 ± 16.96 [13.75, 41.25]</td><td>93.72 ± 5.58 [88.53, 98.36]</td></tr><tr><td>Artificial class balancing</td><td>52.16 ± 2.25 [50.35, 54.07]</td><td>5.0 ± 4.68 [1.25, 8.75]</td><td><strong>99.32</strong> ± 0.37 [98.98, 99.59]</td></tr></tbody></table>

baseline, while the two other models performed worse. Interestingly, T1w motion parameters induced visually stronger motion than our “realistic” ones, suggesting pretraining may help the network learn a rough sense of motion, while fine-tuning refined the criteria. Simulating subtle artefacts might make the task more complex and require more training time or a slower learning curve for the model to reach similar understanding. However, the limited number of mov_1 MRIs (see Table 1) limits statistical power.

## 4. DISCUSSION AND CONCLUSION

In this work, we tested our transfer learning approach for automatic artefact detection on 3D-FLAIR brain MRIs from a CDW. We pretrained three CNNs to detect synthetic head motion, poor contrast and noise artefacts on research datasets, and extended our models to routine clinical images by fine-tuning them on 328 CDW scans. Despite employing various approaches and augmentation techniques, we observed little improvement over training from scratch on the clinical FLAIR MRIs. This highlights the persistent gap between research and clinical data, even when the former is altered to resemble the latter. Making synthetic artefacts more realistic by learning from clinical data could help reduce the gap between research and clinical datasets, while still taking advantage of the larger number of research images. The substantial imbalance within our clinical dataset and its limited size may also account for some of the challenges encountered, increasing the pressure on the realism of the generated artefacts. Image quality metrics, such as that provided by MRIQC³ or SynthSeg,²⁰ could help assess dataset quality, enabling the preselection of images likely to contain specific artefacts, thus leading to a more balanced dataset and a more efficient annotation process. Alternatively, semi-supervised methods offer a promising way to leverage existing annotations, particularly given the wider availability of sequences like T1w MRIs.¹⁰ Unsupervised and self-supervised learning approaches could further reduce reliance on manual annotations.

## Acknowledgments

The research leading to these results has received funding from the French government under management of Agence Nationale de la Recherche as part of the ‘France 2030’ program (reference ANR-23-IACL-0008, PRAIRIE-PSAI) and as part of the ‘Investissements d’avenir’ program (reference ANR-19-P3IA-0001, PRAIRIE 3IA Institute and reference ANR-10-IAIHU-06, Agence Nationale de la Recherche-10-IA Institut Hospitalo-Universitaire-6), from the European Union’s Horizon Europe Framework Program (grant number 101136607, project CLARA), and was supported by a grant from Inserm and the French Ministry of Health in the context of the MESSIDORE 2023 call operated by IReSP (reference AAP-2023-MSDR-341011).
REFERENCES

[1] Jannot, A.-S., Zapletal, E., Avillach, P., Mamzer, M.-F., Burgun, A., and Degoulet, P., "The georges pompidou university hospital clinical data warehouse: a 8-years follow-up experience," *International journal of medical informatics* **102**, 21–28 (2017).

[2] Krupa, K. and Bekiesińska-Figatowska, M., "Artifacts in magnetic resonance imaging," *Polish journal of radiology / Polish Medical Society of Radiology* **80**, 93–106 (02 2015).

[3] Esteban, O., Birman, D., Schaer, M., Koyejo, S., Poldrack, R., and Gorgolewski, K., "Mriqc: Advancing the automatic prediction of image quality in mri from unseen sites," *PLoS ONE* **12**, e0184661 (09 2017).

[4] Bottani, S., Burgos, N., Maire, A., Wild, A., Strer, S., Dormont, D., and Colliot, O., "Automatic quality control of brain T1-weighted magnetic resonance images for a clinical data warehouse," *Medical Image Analysis* **75**, 102219 (2021).

[5] Hendriks, J., Mutsaerts, H.-J., Joules, R., Peña-Nogales, Ó., Rodrigues, P. R., Wolz, R., Burchell, G. L., Barkhof, F., and Schrantee, A., "A systematic review of (semi-)automatic quality control of T1-weighted MRI scans," *Neuroradiology* **66**, 31–42 (Jan. 2024).

[6] Loizillon, S., Bottani, S., Mabille, S., Jacob, Y., Maire, A., Ströer, S., Dormont, D., Colliot, O., and Burgos, N., "Automated MRI Quality Assessment of Brain T1-weighted MRI in Clinical Data Warehouses: A Transfer Learning Approach Relying on Artefact Simulation," *Journal of Machine Learning for Biomedical Imaging* **2**, 888–915 (June 2024).

[7] Loizillon, S., Bottani, S., Maire, A., Ströer, S., Dormont, D., Colliot, O., and Burgos, N., "Automatic motion artefact detection in brain T1-weighted magnetic resonance images from a clinical data warehouse using synthetic data," *Medical Image Analysis* **93**, 103073 (2024).

[8] Peltonen, J. I., Mäkelä, T., and Salli, E., "MRI quality assurance based on 3D FLAIR brain images," *Magnetic Resonance Materials in Physics, Biology and Medicine* **31**, 689–699 (Dec. 2018).

[9] Scott, C. J., Arnott, S. R., Chemparathy, A., Dong, F., Solovey, I., Gee, T., Schmah, T., Chavez, S., Lobaugh, N., Nanayakkara, N., Liang, S., Zamyadi, M., Ozzoude, M., Holmes, M. F., Szilagyi, G. M., Ramirez, J., Symons, S., Black, S. E., Bartha, R., Strother, S., and investigators, t. O., "An overview of the quality assurance and quality control of magnetic resonance imaging data for the Ontario Neurodegenerative Disease Research Initiative (ONDRI): Pipeline development and neuroinformatics," *bioRxiv : the preprint server for biology* (2020).

[10] Loizillon, S., Bottani, S., Maire, A., Ströer, S., Chougar, L., Dormont, D., Colliot, O., and Burgos, N., "Automatic quality control of brain 3d flair mris for a clinical data warehouse," *Medical Image Analysis* **103**, 103617 (2025).

[11] Caligiuri, M. E., Perrotta, P., Augimeri, A., Rocca, F., Quattrone, A., and Cherubini, A., "Automatic Detection of White Matter Hyperintensities in Healthy Aging and Pathology Using Magnetic Resonance Imaging: A Review," *Neuroinformatics* **13**, 261–276 (July 2015).

[12] Karadeli, H. H., Giurgiutiu, D.-V., Cloonan, L., Fitzpatrick, K., Kanakis, A., Ozcan, M. E., Schwamm, L. H., and Rost, N. S., "FLAIR Vascular Hyperintensity is a Surrogate of Collateral Flow and Leukoaraiosis in Patients With Acute Stroke Due to Proximal Artery Occlusion," *Journal of Neuroimaging* **26**, 219–223 (Mar. 2016).

[13] Petersen, R. C., Aisen, P. S., Beckett, L. A., Donohue, M. C., Gamst, A. C., Harvey, D. J., Jack, C. R., Jagust, W. J., Shaw, L. M., Toga, A. W., Trojanowski, J. Q., and Weiner, M. W., "Alzheimer's Disease Neuroimaging Initiative (ADNI)," *Neurology* **74**(3), 201–209 (2010).

[14] Alzheimer's Disease Neuroimaging Initiative, "Image Acquisition." https://adni.loni.usc.edu/data-samples/adni-data/neuroimaging/mri/. (Accessed: 18 July 2025).

[15] [MSSEG Challenge Proceedings: Multiple Sclerosis Lesions Segmentation Challenge Using a Data Management and Processing Infrastructure] (2016).

[16] [MSSEG-2 challenge proceedings: Multiple sclerosis new lesions segmentation challenge using a data management and processing infrastructure] (Sept. 2021).
[17] Babayan, A., Baczkowski, B., Cozatl, R., Dreyer, M., Engen, H., Erbey, M., Falkiewicz, M., Farrugia, N., Gaebler, M., Golchert, J., Golz, L., Gorgolewski, K., Haueis, P., Huntenburg, J., Jost, R., Kramarenko, Y., Krause, S., Kumral, D., Lauckner, M., Margulies, D. S., Mendes, N., Ohrnberger, K., Oligschläger, S., Osoianu, A., Pool, J., Reichelt, J., Reiter, A., Röbbig, J., Schaare, L., Smallwood, J., and Villringer, A., "ds000221_r1.0.0," (2018).

[18] Filimonova, E., Pashkov, A., Borisov, N., Kalinovsky, A., and Rzaev, J., "Utilizing amide proton transfer technique to characterise diffuse gliomas based on who 2021 classification of cns tumors," (2023).

[19] Sudlow, C., Gallacher, J., Allen, N., Beral, V., Burton, P., Danesh, J., Downey, P., Elliott, P., Green, J., Landray, M., et al., "Uk biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age," *PLoS medicine* **12**(3), e1001779 (2015).

[20] Billot, B., Greve, D. N., Puonti, O., ThIELSCHER, A., Van Leemput, K., Fischl, B., Dalca, A. V., and Iglesias, J. E., "Synthseg: Segmentation of brain MRI scans of any contrast and resolution without retraining," *Medical Image Analysis* **86**, 102789 (2023).

[21] Routier, A., Burgos, N., Díaz, M., Bacci, M., Bottani, S., El-Rifai, O., Fontanella, S., Gori, P., Guillon, J., Guyot, A., et al., "Clinica: An open-source software platform for reproducible clinical neuroscience studies," *Frontiers in neuroinformatics* **15**, 689675 (2021).

[22] Tustison, N. J., Avants, B. B., Cook, P. A., Zheng, Y., Egan, A., Yushkevich, P. A., and Gee, J. C., "N4ITK: Improved N3 Bias Correction," *IEEE Transactions on Medical Imaging* **29**(6), 1310-1320 (2010).

[23] Avants, B. B., Epstein, C. L., Grossman, M., and Gee, J. C., "Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain," *Medical Image Analysis* **12**(1), 26-41 (2008).

[24] Wen, J., Thibeau-Sutre, E., Diaz-Melo, M., Samper-González, J., Routier, A., Bottani, S., Dormont, D., Durrleman, S., Burgos, N., and Colliot, O., "Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation," *Medical Image Analysis* **63**, 101694 (2020).

[25] Pérez-García, F., Sparks, R., and Ourselin, S., "TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning," *Computer Methods and Programs in Biomedicine* **208**, 106236 (2021).

[26] Duffy, B. A., Zhang, W., Tang, H., and Zhao, L., "Retrospective correction of motion artifact affected structural MRI images using deep learning of simulated motion," in [*Medical Imaging with Deep Learning - MIDL 2018*], (2018).

[27] Dietrich, O., Raya, J. G., Reeder, S. B., Reiser, M. F., and Schoenberg, S. O., "Measurement of signal-to-noise ratios in mr images: Influence of multichannel coils, parallel imaging, and reconstruction filters," *Journal of Magnetic Resonance Imaging* **26**(2), 375-385 (2007).

[28] Erdogmus, D., Larsson, E., Yan, R., Principe, J., and Fitzsimmons, J., "Measuring the signal-to-noise ratio in magnetic resonance imaging: A caveat," *Signal Processing* **84**, 1035-1040 (06 2004).

[29] Eichhorn, H., Chemnitz-Thomsen, S., Vouros, E., Shekhrajka, N., Frost, R., van der Kouwe, A., and Ganz, M., "Evaluating the match of image quality metrics with radiological assessment in a dataset with and without motion artifacts," in [*Proc. ISMRM Annu. Meeting*], 2 (2022).

[30] Marchetto, E., Eichhorn, H., Gallichan, D., Schnabel, J. A., and Ganz, M., "Agreement of image quality metrics with radiological evaluation in the presence of motion artifacts," *Magnetic Resonance Materials in Physics, Biology and Medicine*, 1-12 (2025).

[31] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S., [*PyTorch: an imperative style, high-performance deep learning library*], Curran Associates Inc., Red Hook, NY, USA (2019).

[32] Thibeau-Sutre, E., Díaz, M., Hassanaly, R., Routier, A., Dormont, D., Colliot, O., and Burgos, N., "ClinicaDL: An open-source deep learning software for reproducible neuroimaging processing," *Computer Methods and Programs in Biomedicine* **220**, 106818 (2022).