2 citations · 2 across the 5 of their papers we have counts for
6 papers
SIINR: Structurally Informed Implicit Neural Representations for super-resolution with uncertainty quantification of clinical quality diffusion MRI datasets
Tom Hendriks, William Consagra, Anna Vilanova +2
Diffusion Magnetic Resonance Imaging (dMRI) is a powerful tool for probing brain microstructure, but clinical acquisitions are often limited by low out-of-plane resolution, resulti…
Real-Time Rendering of Dynamic Line Sets using Voxel Ray Tracing
Bram Kraaijeveld, Andrei C. Jalba, Anna Vilanova +1
Real-time rendering of dynamic line sets is relevant in many visualization tasks, including unsteady flow visualization and interactive white matter reconstruction from Magnetic Re…
Implicit neural representations for accurate estimation of the standard model of white matter
Tom Hendriks, Gerrit Arends, Edwin Versteeg +3
Diffusion magnetic resonance imaging (dMRI) enables non-invasive investigation of tissue microstructure. The Standard Model (SM) of white matter aims to disentangle dMRI signal con…
MICCAI-CDMRI 2023 QuantConn Challenge Findings on Achieving Robust Quantitative Connectivity through Harmonized Preprocessing of Diffusion MRI
Nancy R. Newlin, Kurt Schilling, Serge Koudoro +33
White matter alterations are increasingly implicated in neurological diseases and their progression. International-scale studies use diffusion-weighted magnetic resonance imaging (…
Multi-dimensional Parameter Space Exploration for Streamline-specific Tractography
Ruben Vink, Anna Vilanova, Maxime Chamberland
One of the unspoken challenges of tractography is choosing the right parameters for a given dataset or bundle. In order to tackle this challenge, we explore the multi-dimensional p…
Neural Spherical Harmonics for structurally coherent continuous representation of diffusion MRI signal
Tom Hendriks, Anna Vilanova, Maxime Chamberland
We present a novel way to model diffusion magnetic resonance imaging (dMRI) datasets, that benefits from the structural coherence of the human brain while only using data from a si…