Publications associées à Multi-modal Latent-Space Self-alignment for Super-Resolution Cardiac MR Segmentation

Axial and radial axonal diffusivities and radii from single encoding strongly diffusion-weighted MRI

Erick Jorge Canales Rodriguez, Marco Pizzolato, Tim Bjørn Dyrby

We enable the estimation of the per-axon axial diffusivity from single encoding, strongly diffusion-weighted, pulsed gradient spin echo data. Additionally, we improve the estimation of the per-axon radial diffusivity compared to estimates based on spherica ...

ELSEVIER2023

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

Prospective motion correction in kidney MRI using FID navigators

Tobias Kober

Purpose Abdominal MRI scans may require breath-holding to prevent image quality degradation, which can be challenging for patients, especially children. In this study, we evaluate whether FID navigators can be used to measure and correct for motion prospec ...

WILEY2022

3-Dimensional Fluid and White Matter Suppression Magnetic Resonance Imaging Sequence Accelerated With Compressed Sensing Improves Multiple Sclerosis Cervical Spinal Cord Lesion Detection Compared With Standard 2-Dimensional Imaging

Tobias Kober

Objectives Fluid and white matter suppression (FLAWS) is a recently proposed magnetic resonance sequence derived from magnetization-prepared 2 rapid acquisition gradient-echo providing 2 coregistered datasets with white matter- and cerebrospinal fluid-supp ...

LIPPINCOTT WILLIAMS & WILKINS2022

Combining Model Driven and Data Driven Approaches for Inverse Problems in Parameter Estimation and Image Reconstruction: From Modelling to Validation

Thomas Yu

Machine learning has become the state of the art for the solution of the diverse inverse problems arising from computer vision and medical imaging, e.g. denoising, super-resolution, de-blurring, reconstruction from scanner data, quantitative magnetic reson ...

EPFL2022

Medipix4, a high granularity four sides buttable pixel readout chip for high resolution spectroscopic X-ray imaging at rates compatible with medical CT scans

Viros Sriskaran

Medipix4 is the latest member in the Medipix/Timepix family of pixel detector chips aimed at high rate spectroscopic X-ray imaging using high-Z materials. The chip address the limitations of conventional hybrid pixel detectors for X-ray imaging. Its predec ...

EPFL2022

Diamond-Based Nanoscale Quantum Relaxometry for Sensing Free Radical Production in Cells

Mayeul Sylvain Chipaux, Hoda Shirzad

Diamond magnetometry makes use of fluorescent defects in diamonds to convert magnetic resonance signals into fluorescence. Because optical photons can be detected much more sensitively, this technique currently holds several sensitivity world records for r ...

WILEY-V C H VERLAG GMBH2022

Simulated Half-Fourier Acquisitions Single-shot Turbo Spin Echo (HASTE) of the Fetal Brain: Application to Super-Resolution Reconstruction

Meritxell Bach Cuadra, Tobias Kober, Tom Hilbert, Thomas Yu, Reto Meuli, Sébastien Tourbier, Patric Hagmann, Hamza Kebiri

Accurate characterization of in utero human brain maturation is critical as it involves complex interconnected structural and functional processes that may influence health later in life. Magnetic resonance imaging is a powerful tool complementary to the u ...

SPRINGER INTERNATIONAL PUBLISHING AG2021

Open-access quantitative MRI data of the spinal cord and reproducibility across participants, sites and manufacturers

Dimitri Nestor Alice Van De Ville, Nawal Noëlle Kinany, Maxime Descoteaux, Christian Büchel, Maryam Seif

In a companion paper by Cohen-Adad et al. we introduce the spine generic quantitative MRI protocol that provides valuable metrics for assessing spinal cord macrostructural and microstructural integrity. This protocol was used to acquire a single subject da ...

NATURE PORTFOLIO2021

Self-Attentive Spatial Adaptive Normalization for Cross-Modality Domain Adaptation

Jean-Philippe Thiran, Guillaume Marc Georges Vray, Devavrat Tomar, Seyedbehzad Bozorgtabar, Manana Lortkipanidze

Despite the successes of deep neural networks on many challenging vision tasks, they often fail to generalize to new test domains that are not distributed identically to the training data. The domain adaptation becomes more challenging for cross-modality m ...

Institute of Electrical and Electronics Engineers2021