Christophe Lenglet

This paper presents high-resolution diffusion MRI techniques for in vivo and ex vivo macaque brain imaging at 10.5 Tesla, enabling detailed connectivity studies.

The paper shows that structural connectivity in the human visual cortex preserves retinotopic organization, revealing a retinotopic wiring principle and systematic perceptual asymmetries in early visual cortex connectivity.

The paper introduces a numerical method to quantify SAR prediction errors for MRI safety validation, avoiding the need for MR thermometry.

This paper evaluates the diffusion-weighted steady-state free precession (DW-SSFP) sequence for ex vivo macaque brain imaging at 10.5T, showing improved SNR efficiency and geometric distortion reduction.

This study shows body size parameters significantly correlate with brain and spinal cord MRI metrics and should be included in neuroimaging studies.

This paper optimizes diffusion MRI protocols using a Cramer-Rao lower bound framework to improve biophysical modeling accuracy of brain microstructure.

The paper compares microstructure and fiber orientation measurements from polarization-sensitive OCT and ultra-high-field dMRI in macaque brains and demonstrates a GAN for improving MRI resolution using OCT data.

This paper introduces NORDIC, a denoising framework for dMRI that improves SNR and fiber resolution, outperforming existing methods in simulations and human connectome data.

The paper applies network curvature to brain structural networks to identify robust/fragile regions, track development/aging changes, and detect connectivity differences in Autism Spectrum Disorders.