Alberto De Luca

Cross-site harmonization of diffusion MRI data without matched training subjects

This paper introduces RISH-GLM, a method to harmonize diffusion MRI data across sites without requiring matched training subjects, showing effectiveness with unmatched groups and multiple sites.

Anatomically informed multi-level fiber tractography

This paper introduces a novel fiber tractography method that improves the reconstruction of the corticospinal tract by incorporating anatomical priors and multi-level fiber orientation analysis.

Insights from the IronTract challenge: optimal methods for mapping brain pathways from multi-shell diffusion MRI

The paper evaluates tractography methods using a macaque dataset to determine optimal approaches for mapping brain pathways from multi-shell diffusion MRI.

Tractography dissection variability: what happens when 42 groups dissect 14 white matter bundles on the same dataset?

This study evaluates variability in white matter bundle segmentation across 42 groups using the same dataset, finding protocol variability exceeds other sources of inconsistency.

On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: chronicles of the MEMENTO challenge.

Effects of Inaccurate Response Function Calibration on Characteristics of the Fiber Orientation Distribution in Diffusion MRI

This study investigates how inaccuracies in response function calibration affect fiber orientation distribution (FOD) characteristics and fiber tracking in diffusion MRI.

Spherical deconvolution with tissue-specific response functions and multi-shell diffusion MRI to estimate multiple fiber orientation distributions (mFODs)

This paper introduces a method to estimate multiple fiber orientation distributions in both white and grey matter using tissue-specific response functions and multi-shell diffusion MRI, improving cortical fiber tractography.

Automated characterization of noise distributions in diffusion MRI data

This paper introduces automated methods to estimate noise distributions in diffusion MRI data using Gamma distribution moments and maximum likelihood, showing improved accuracy over Rician assumptions.

Variability in the analysis of a single neuroimaging dataset by many teams

The paper shows that different analysis workflows by 70 teams on the same fMRI dataset led to varied results, but meta-analysis found consensus in activated regions.