Carl‐Fredrik Westin

ROVER-dMRI improves high-SNR whole-brain mesoscale diffusion MRI with reduced scan time and preserved fiber details.

This paper introduces FCG, a technique that corrects spatiotemporal-varying phase errors in EPI using an MLP, improving image quality on high-performance systems.

MaxEnt-DTD estimates diffusion tensor distributions from dMRI data, providing reliable joint fiber-orientation and microstructure analysis.

This study identifies sex differences in white matter tracts using diffusion MRI tractography and finds associations with motor function in a large cohort.

This study compares multi-echo and time-division multiplexing sequences for relaxation-diffusion MRI, finding TDM provides more accurate T2 estimates and microstructure measures with faster acquisition.

This paper introduces a method to model combined relaxation diffusion MRI data by characterizing b-value dependent T2 relaxation rates to improve neurite microstructure characterization.

The paper introduces a cortical surface-informed volumetric spatial smoothing method for fMRI data using graph signal processing to adapt smoothing to brain morphology.

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 introduces an anisotropic spatial filtering method for smoothing fMRI data in white matter, showing improved detection of anisotropic activations compared to isotropic Gaussian filters.

This study uses diffusion-informed white matter harmonics to analyze spatial dynamics of fMRI data, revealing subtle spatial modulations under functional load.

This paper develops a deep learning method to segment brain tissue from diffusion MRI without anatomical registration, achieving high accuracy on both HCP and clinical data.

This paper introduces REDIM, a method to compute joint moments of relaxation-diffusion to characterize neural microstructure, showing it can distinguish fiber bundles with similar orientations but different relaxation rates.

This paper introduces a Finsler geometry-based method for analyzing brain structural connectivity, showing improved accuracy compared to previous geodesic methods using HCP and autism data.

This study evaluates modern diffusion tractography methods using a standardized challenge, finding no significant improvement in anatomical accuracy despite methodological advances.

The paper evaluates tractography methods by analyzing a simulated dataset, finding that most algorithms generate many false-positive connections.