Meritxell Bach Cuadra

This study applies SPANGY to fetal MRI data to quantify cortical folding patterns, revealing the temporal sequence of low-to-high frequency fold emergence during prenatal development.

This study introduces an automated 3D MRI segmentation method for the human eye and orbit, providing large-scale unbiased atlases for clinical research.

This study shows that lower-quality fetal MRI scans systematically bias normative centile estimates in brain development models, particularly affecting outer centiles.

This study uses high-resolution 3D-MRSI to assess neurometabolite differences in youth at risk for psychosis compared to controls, finding elevated tNAA and Ins in specific brain regions.

This study evaluates deep learning models for fODF estimation in infant brain MRI, finding that U-Net performs best with fewer gradient directions, while MLP and Transformer improve with more directions.

This paper develops automated 3D segmentation techniques for human eye and orbit structures using MRI and evaluates axial length estimation.

The paper uses functional MRI data to study thalamic functional organization in newborns, identifying age-related changes and specific thalamic units.

This study uses deep learning and normative modeling to analyze MRI data from MS patients and identify subcortical volume deviations that improve understanding of disease progression.

This study evaluates inter- and intra-rater variability in quality scoring of fetal brain MRI reconstructions across three pipelines and identifies systematic weaknesses in the methods.

This paper improves MS lesion segmentation and detection in MRI by using saliency maps to refine prediction scores, achieving higher F1 and PPV scores.

This study uses deep learning to estimate fiber orientation distribution functions (FODs) from limited diffusion MRI data in newborns and fetuses, achieving results comparable to standard methods with fewer measurements.

The paper introduces a synthetic fetal brain MRI dataset generated with improved realism and used to evaluate automated segmentation and AI model development.

This paper uses an autoencoder to enhance through-plane resolution in fetal brain diffusion MRI, outperforming interpolation methods on pre-term newborn data.

This paper introduces a method for fetal diffusion MRI reconstruction with intensity correction, enabling detailed analysis of brain connectivity and microstructure in-utero.