Daniel Topgaard

This study uses multidimensional diffusion MRI to show increased microstructural heterogeneity in 5xFAD mouse brains compared to wild-type, associated with amyloid pathology but not widespread degeneration.

This study introduces frequency-dependent diffusion tensor distribution imaging (ωDTD) combined with machine learning to better characterize ischemic tissue damage in rats, showing improved representation of microstructural changes compared to conventional DTI.

This paper uses unsupervised clustering on diffusion-relaxation MRI data to classify tissue water populations in rat and human brains, revealing more complex tissue structures than traditional methods.

This study evaluates the reproducibility of MMD-MRI metrics in ex vivo mouse brains and identifies fixation time effects.

This study investigates whether frequency-dependent diffusion-relaxation MRI can provide meaningful information or if it leads to overfitting in different tissue types and frequency ranges.

The paper demonstrates in vivo rat-brain mapping using nonparametric D(omega)-R1-R2 distributions MRI to identify multiple water pools in gray matter with distinct diffusion and relaxation properties.

This paper introduces an efficient in vivo MD-MRI protocol for whole-brain imaging and demonstrates its feasibility and robustness using phantoms and human scans.

This paper evaluates the reliability and reproducibility of multidimensional diffusion-relaxation MRI (MD-MRI) in the human brain, showing it can reliably capture structural and chemical diversity within voxels.

This paper introduces a method to estimate orientation-specific relaxation-diffusion features using nonparametric Monte Carlo MRI signal inversion, enabling detailed analysis of white matter microstructure.