Mr. Aurélien Maire Profile

Aurélien Maire

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Publications (3)

Proceedings Article | 2 April 2024 Presentation + Paper

Leveraging noise and contrast simulation for the automatic quality control of routine clinical T1-weighted brain MRI

Sophie Loizillon, Stéphane Mabille, Simona Bottani, Yannick Jacob, Aurélien Maire, Sebastian Ströer, Didier Dormont, Olivier Colliot, Ninon Burgos

Proceedings Volume 12926, 129261A (2024) https://doi.org/10.1117/12.3005781

KEYWORDS: Magnetic resonance imaging, Brain, Education and training, Data modeling, Quality control, Image quality, Motion detection, Neuroimaging, Cross validation

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Proceedings Article | 3 April 2023 Poster + Paper

Transfer learning from synthetic to routine clinical data for motion artefact detection in brain T1-weighted MRI

Sophie Loizillon, Simona Bottani, Aurélien Maire, Sebastian Ströer, Didier Dormont, Olivier Colliot, Ninon Burgos

Proceedings Volume 12464, 124641H (2023) https://doi.org/10.1117/12.2648201

KEYWORDS: Magnetic resonance imaging, Motion detection, Education and training, Motion models, Machine learning, Brain, Radiology, Data modeling, Image quality, Neuroimaging

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Proceedings Article | 4 April 2022 Poster + Paper

Homogenization of brain MRI from a clinical data warehouse using contrast-enhanced to non-contrast-enhanced image translation with U-Net derived models

Simona Bottani, Elina Thibeau-Sutre, Aurélien Maire, Sebastian Ströer, Didier Dormont, Olivier Colliot, Ninon Burgos

Proceedings Volume 12032, 1203226 (2022) https://doi.org/10.1117/12.2608565

KEYWORDS: Image quality, Image segmentation, Brain, Magnetic resonance imaging, Neuroimaging, Data modeling, 3D modeling, Tissues, Gadolinium, 3D image enhancement

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Clinical data warehouses provide access to massive amounts of medical images and thus offer unprecedented opportunities for research. However, they also pose important challenges, a major challenge being their heterogeneity. In particular, they contain patients with numerous different diseases. The exploration of some neurological diseases with magnetic resonance imaging (MRI) requires injecting a gadolinium-based contrast agent (for instance to detect tumors or other contrast-enhancing lesions) while other diseases do not require such injection. Image harmonization is a key factor to enable unbiased differential diagnosis in such context. Additionally, classical neuroimaging software tools that extract features used as inputs of classification algorithms are typically applied only to images without gadolinium. The objective of this work is to homogenize images from a clinical data warehouse and enable the extraction of consistent features from brain MR images, no matter the initial presence or absence of gadolinium. We propose a deep learning approach based on a 3D U-Net to translate contrast-enhanced into non-contrast-enhanced T1-weighted brain MRI. The approach was trained/validated using 230 image pairs and tested on 26 image pairs of good quality and 51 image pairs of low quality from the data warehouse of the hospitals of the Greater Paris area (Assistance Publique-Hˆopitaux de Paris [AP-HP]). We tested two different 3D U-Net architectures and we chose the one reaching the best image similarity metrics for a further validation for a segmentation task. We tested two 3D U-Net architectures with the addition either of residual connections or of attention mechanisms. The U-Net with attention mechanisms reached the best image similarity metrics and was further validated on a segmentation task. We showed that features extracted from the synthetic images (gray matter, white matter and cerebrospinal fluid volumes) were closer to those obtained from the non-contrast-enhanced T1-weighted brain MRI (considered as reference) than the original, contrast-enhanced, images.

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