Niall Bourke

This study develops a deep learning model to enhance ultra-low-field MRI scans to near high-field resolution without paired high-field data, improving image quality and accessibility.

The paper introduces dGAMLSS, a distributed algorithm for fitting GAMLSS models to construct population reference charts without sharing patient data.

This study identifies two ASD subgroups with distinct brain morphology using cross-cultural datasets, highlighting key brain regions associated with subgroup characteristics and autistic traits.

This study evaluates the reliability and correspondence of ultra-low-field MRI morphometry to high-field MRI, showing high reliability and correspondence across scan resolutions and contrasts.

This paper develops a deep learning model to enhance ultra-low-field MRI scans for pediatric neurodevelopmental research, achieving better reconstruction of brain structures than existing methods.

The paper introduces a longitudinal dataset of school-age children and adolescents in China, detailing its design, sampling, and neuroimaging measurements.

The paper introduces brain charts as a standardized resource for benchmarking brain morphology across the human lifespan using MRI data from a large and diverse cohort.