15 citations · 40 across the 17 of their papers we have counts for
18 papers
PRIME-SVR: Physics-infoRmed Implicit Multi-Echo Slice-to-Volume Reconstruction for Fetal T2 mapping
Busra Bulut, Maik Dannecker, Thomas Sanchez +14
Slice-to-volume reconstruction (SVR) is the standard method for obtaining high-resolution (HR) 3D fetal brain volumes from motion-corrupted 2D MRI slice stacks acquired in multiple…
Causal Attribution of Model Performance Gaps in Medical Imaging Under Distribution Shifts
Pedro M. Gordaliza, Nataliia Molchanova, Jaume Banus +2
Deep learning models for medical image segmentation suffer significant performance drops due to distribution shifts, but the causal mechanisms behind these drops remain poorly unde…
Fetpype: An Open-Source Pipeline for Reproducible Fetal Brain MRI Analysis
Thomas Sanchez, Gerard Martí-Juan, David Meunier +6
Fetal brain magnetic resonance imaging (MRI) is crucial for assessing neurodevelopment in utero. However, fetal MRI analysis remains technically challenging due to fetal motion, lo…
Accounting for Underspecification in Statistical Claims of Model Superiority
Thomas Sanchez, Pedro M. Gordaliza, Meritxell Bach Cuadra
Machine learning methods are increasingly applied in medical imaging, yet many reported improvements lack statistical robustness: recent works have highlighted that small but signi…
T2 mapping at 0.55 T using Ultra-Fast Spin Echo MRI
Margaux Roulet, Hamza Kebiri, Busra Bulut +9
Low-field T2 mapping MRI can democratize neuropediatric imaging by improving accessibility and providing quantitative biomarkers of brain development. \textbf{Purpose:} To evaluate…
Segmenting infant brains across magnetic fields: Domain randomization and annotation curation in ultra-low field MRI
Vladyslav Zalevskyi, Dondu-Busra Bulut, Thomas Sanchez +1
Early identification of neurodevelopmental disorders relies on accurate segmentation of brain structures in infancy, a task complicated by rapid brain growth, poor tissue contrast,…
Enhancing Corpus Callosum Segmentation in Fetal MRI via Pathology-Informed Domain Randomization
Marina Grifell i Plana, Vladyslav Zalevskyi, Léa Schmidt +6
Accurate fetal brain segmentation is crucial for extracting biomarkers and assessing neurodevelopment, especially in conditions such as corpus callosum dysgenesis (CCD), which can…
Physics-Informed Joint Multi-TE Super-Resolution with Implicit Neural Representation for Robust Fetal T2 Mapping
Busra Bulut, Maik Dannecker, Thomas Sanchez +9
T2 mapping in fetal brain MRI has the potential to improve characterization of the developing brain, especially at mid-field (0.55T), where T2 decay is slower. However, this is cha…
Meta-learning Slice-to-Volume Reconstruction in Fetal Brain MRI using Implicit Neural Representations
Maik Dannecker, Thomas Sanchez, Meritxell Bach Cuadra +6
High-resolution slice-to-volume reconstruction (SVR) from multiple motion-corrupted low-resolution 2D slices constitutes a critical step in image-based diagnostics of moving subjec…
Advances in Automated Fetal Brain MRI Segmentation and Biometry: Insights from the FeTA 2024 Challenge
Vladyslav Zalevskyi, Thomas Sanchez, Misha Kaandorp +67
Accurate fetal brain tissue segmentation and biometric analysis are essential for studying brain development in utero. The FeTA Challenge 2024 advanced automated fetal brain MRI an…
Automatic quality control in multi-centric fetal brain MRI super-resolution reconstruction
Thomas Sanchez, Vladyslav Zalevskyi, Angeline Mihailov +7
Quality control (QC) has long been considered essential to guarantee the reliability of neuroimaging studies. It is particularly important for fetal brain MRI, where acquisitions a…
Evaluating Synthetic Data Generation for Domain Generalization in Fetal Brain MRI Segmentation
Vladyslav Zalevskyi, Thomas Sanchez, Margaux Roulet +10
Fetal brain tissue segmentation from magnetic resonance imaging (MRI) is crucial for studying neurodevelopment, but remains challenging due to data heterogeneity and limited annota…
Improving cross-domain brain tissue segmentation in fetal MRI with synthetic data
Vladyslav Zalevskyi, Thomas Sanchez, Margaux Roulet +4
Segmentation of fetal brain tissue from magnetic resonance imaging (MRI) plays a crucial role in the study of in utero neurodevelopment. However, automated tools face substantial d…
Learning to sample in Cartesian MRI
Thomas Sanchez
Despite its exceptional soft tissue contrast, Magnetic Resonance Imaging (MRI) faces the challenge of long scanning times compared to other modalities like X-ray radiography. Short…
FetMRQC: a robust quality control system for multi-centric fetal brain MRI
Thomas Sanchez, Oscar Esteban, Yvan Gomez +8
Fetal brain MRI is becoming an increasingly relevant complement to neurosonography for perinatal diagnosis, allowing fundamental insights into fetal brain development throughout ge…
FetMRQC: Automated Quality Control for fetal brain MRI
Thomas Sanchez, Oscar Esteban, Yvan Gomez +2
Quality control (QC) has long been considered essential to guarantee the reliability of neuroimaging studies. It is particularly important for fetal brain MRI, where large and unpr…
Simulation-based parameter optimization for fetal brain MRI super-resolution reconstruction
Priscille de Dumast, Thomas Sanchez, Hélène Lajous +1
Tuning the regularization hyperparameter in inverse problems has been a longstanding problem. This is particularly true in the case of fetal brain magnetic resonance imaging, w…
Scalable Learning-Based Sampling Optimization for Compressive Dynamic MRI
Thomas Sanchez, Baran Gözcü, Ruud B. van Heeswijk +4
Compressed sensing applied to magnetic resonance imaging (MRI) allows to reduce the scanning time by enabling images to be reconstructed from highly undersampled data. In this pape…