2 citations · 3 across the 2 of their papers we have counts for
5 papers
What if each voxel were measured with a different diffusion protocol?
Santiago Coelho, Gregory Lemberskiy, Ante Zhu +5
Expansion of diffusion MRI (dMRI) both into the realm of strong gradients, and into accessible imaging with portable low-field devices, brings about the challenge of gradient nonli…
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…
Universal Sampling Denoising (USD) for noise mapping and noise removal of non-Cartesian MRI
Hong-Hsi Lee, Mahesh Bharath Keerthivasan, Gregory Lemberskiy +3
Random matrix theory (RMT) combined with principal component analysis has resulted in a widely used MPPCA noise mapping and denoising algorithm, that utilizes the redundancy in mul…
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…