MRI diffusion data suffers from significant inter-and intra-site variability, which hinders multi-
site and/or longitudinal diffusion studies. This variability may arise from a range of factors …

Tremendous efforts have been made in the last decade to advance cutting-edge MRI
technology in pursuit of map** structural connectivity in the living human brain with …

Purpose In the present work, we describe the correction of diffusion‐weighted MRI for site
and scanner biases using a novel method based on invariant representation. Theory and …

Cross-scanner and cross-protocol variability of diffusion magnetic resonance imaging
(dMRI) data are known to be major obstacles in multi-site clinical studies since they limit the …

Purpose To develop a new 3D generative adversarial network that is designed and
optimized for the application of multimodal 3D neuroimaging synthesis. Methods We present …

There is significant interest in pooling magnetic resonance image (MRI) data from multiple
datasets to enable mega-analysis. Harmonization is typically performed to reduce …

Image synthesis is one of the key applications of deep learning in neuroimaging, which
enables shortening of the scan time and/or improve image quality; therefore, reducing the …

Multicenter diffusion magnetic resonance imaging (MRI) has drawn great attention recently
due to the expanding need for large-scale brain imaging studies, whereas the variability in …

Brain magnetic resonance imaging (MRI) is widely used in clinical practice for disease
diagnosis. However, MRI scans acquired at different sites can have different appearances …

Diffusion-weighted magnetic resonance imaging (dMRI) permits a detailed in-vivo analysis
of neuroanatomical microstructure, invaluable for clinical and population studies. However …