Markus Nilsson

This paper investigates how histological features like cell density and structure anisotropy explain variations in diffusion MRI metrics MD and FAIP in meningioma tumors, finding that cell density explains variability across but not within tumors.

This paper introduces a super-resolution reconstruction framework for diffusion MRI, showing it improves signal accuracy and precision in low-SNR conditions, especially with high-resolution and spherical b-tensor encoding.

This study uses deep neural networks to accelerate parameter estimation in a high-dimensional diffusion-relaxation MRI model but finds that machine learning cannot replace careful acquisition protocol design.

This paper identifies challenges in estimating spherical pore size in the human brain using diffusion MRI, highlighting noise biases, signal fraction limits, and sensitivity to cylindrical structures.

The paper investigates the dot-compartment in diffusion MRI using ultra-strong gradients and spherical tensor encoding, finding residual signals in cerebellar grey matter and white matter.