Publications de Thomas Sanchez

Biometry and volumetry in multi-centric fetal brain magnetic resonance imaging: assessing the bias of super-resolution reconstruction

Background: Fetal brain MRI is increasingly used to complement ultrasound imaging. Images are processed using complex super-resolution reconstruction pipelines, which may bias biometric and volumetric measurements. Objective: To assess the consistency of 2 ...

2025

Assessing Data Quality on Fetal Brain MRI Reconstruction: A Multi-site and Multi-rater Study

Meritxell Bach Cuadra, Thomas Sanchez

Quality assessment (QA) has long been considered essential to guarantee the reliability of neuroimaging studies. It is particularly important for fetal brain MRI, where unpredictable fetal motion can lead to substantial artifacts in the acquired images. Mu ...

Springer Science and Business Media Deutschland GmbH2025

Removing Facial Features From Structural Mri Images Biases Visual Quality Assessment

Jonas Richiardi, Patric Hagmann, Elda Fischi Gomez, Thomas Sanchez, Oscar Esteban Rucavado Leandro

A critical step before data-sharing of human neuroimaging is removing facial features to protect individuals' privacy. However, not only does this process redact identifiable information about individuals, but it also removes non-identifiable information. ...

PUBLIC LIBRARY SCIENCE2025

Learning to sample in Cartesian MRI

Thomas Sanchez

Magnetic Resonance Imaging (MRI) is a non-invasive, non-ionizing imaging modality with unmatched soft tissue contrast. However, compared to imaging methods like X-ray radiography, MRI suffers from long scanning times, due to its inherently sequential acqui ...

EPFL2022

A Plug-and-Play Deep Image Prior

Volkan Cevher, Thomas Sanchez, Fabian Ricardo Latorre Gomez, Zhaodong Sun

Deep image priors (DIP) offer a novel approach for the regularization that leverages the inductive bias of a deep convolutional architecture in inverse problems. However, the quality of DIP approaches often degrades when the number of iterations exceeds a ...

IEEE2021

Scalable Learning-Based Sampling Optimization For Compressive Dynamic MRI

Ruud van Heeswijk, Armin Eftekhari, Volkan Cevher, Baran Gözcü, Thomas Sanchez

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 paper, we tackle the problem of designing a sampling mask for an arbitrary recon ...

IEEE2020

Rethinking Sampling in Parallel MRI: A Data-Driven Approach

Volkan Cevher, Baran Gözcü, Thomas Sanchez

In the last decade, Compressive Sensing (CS) has emerged as the most promising, model-driven approach to accelerate MRI scans. CS relies on the key sparsity assumption and proposes random sampling for data acquisition. The practical CS approaches in MRI em ...

IEEE2019