50 citations · 135 across the 6 of their papers we have counts for
6 papers
Denoising Improves Cross-Scanner and Cross-Protocol Test-Retest Reproducibility of Higher-Order Diffusion Metrics
Benjamin Ades-Aron, Santiago Coelho, Gregory Lemberskiy +5
The clinical translation of diffusion MRI (dMRI)-derived quantitative contrasts hinges on robust reproducibility, minimizing both same-scanner and cross-scanner variability. This s…
Volume electron microscopy in injured rat brain validates white matter microstructure metrics from diffusion MRI
Ricardo Coronado-Leija, Ali Abdollahzadeh, Hong-Hsi Lee +8
Biophysical modeling of diffusion MRI (dMRI) offers the exciting potential of bridging the gap between the macroscopic MRI resolution and microscopic cellular features, effectively…
Mapping tissue microstructure of brain white matter in vivo in health and disease using diffusion MRI
Ying Liao, Santiago Coelho, Jenny Chen +7
Diffusion magnetic resonance imaging offers unique in vivo sensitivity to tissue microstructure in brain white matter, which undergoes significant changes during development and is…
Optimization and Validation of the DESIGNER dMRI preprocessing pipeline in white matter aging
Jenny Chen, Benjamin Ades-Aron, Hong-Hsi Lee +5
Various diffusion MRI (dMRI) preprocessing pipelines are currently available to yield more accurate diffusion parameters. Here, we evaluated accuracy and robustness of the optimize…
Reproducibility of the Standard Model of diffusion in white matter on clinical MRI systems
Santiago Coelho, Steven H. Baete, Gregory Lemberskiy +5
Estimating intra- and extra-axonal microstructure parameters, such as volume fractions and diffusivities, has been one of the major efforts in brain microstructure imaging with MRI…
Training a Neural Network for Gibbs and Noise Removal in Diffusion MRI
Matthew J. Muckley, Benjamin Ades-Aron, Antonios Papaioannou +7
We develop and evaluate a neural network-based method for Gibbs artifact and noise removal. A convolutional neural network (CNN) was designed for artifact removal in diffusion-weig…