Helge J. Zöllner

The paper estimates age-related changes in metabolite relaxation times in pediatric thalamus using MRS and provides linear equations for age-specific correction.

The paper introduces Osprey-MRSI, an open-source software package for fully automated MRSI data analysis and visualization, validated across multiple vendors and sites.

The paper analyzes age-related changes in neurometabolite concentrations in infants using 1H-MRS data from the HBCD study, finding significant increases in some metabolites and decreases in others.

This study benchmarks how different anatomical segmentation tools affect MRS-derived metabolite concentration estimates in brain imaging.

The paper introduces a method to identify out-of-voxel signals in MRS by analyzing phase cycles, leading to improved suppression of artifacts through optimized gradients.

This paper optimizes a gradient scheme for PRESS-localized edited MRS to suppress out-of-voxel artifacts using a volume-based likelihood model and genetic algorithm.

This paper introduces a data-driven algorithm using AIC to determine the optimal basis set composition for 1H-MRS, showing improved accuracy in identifying metabolites.

The paper measures metabolite T1 relaxation times in healthy adults across age groups and finds age-related decreases in several metabolites in specific brain regions.

The paper investigates age-related changes in metabolite T2 relaxation times across the adult lifespan in a multi-site cohort, finding significant declines in several metabolites with aging.

This paper develops a water relaxation atlas to correct metabolite concentrations in MRS by integrating age and region-specific relaxation values into the analysis workflow.

This study compares the test-retest reproducibility of DESPOT and QALAS for T1 and T2 mapping in healthy volunteers, finding QALAS provides better reproducibility and reduced acquisition time.

This study compares 1D and 2D linear-combination modeling of MRS data, finding similar accuracy and uncertainty estimation between the two approaches, with 2D showing stability under correlated noise.

This paper introduces a model-based frequency-and-phase correction method for 1H MRS data using 2D linear-combination modeling, showing improved performance over traditional approaches at low SNR.

The paper evaluates the ISTHMUS MRS sequence for reproducibility and data quality in the HBCD study, finding improved T2 correction and reduced operator workload.

This study found no significant differences in cerebral glutathione or GABA levels between autistic children and typically developing children, but found a positive correlation between GABA levels and ADHD inattention scores.

This study evaluates how acquisition and modeling parameters affect test-retest reproducibility of GABA+ measurements using edited MRS, finding that model selection significantly impacts reproducibility.

This paper introduces AGNOSTIC, a synthetic 1H MRS dataset for training deep learning models to detect and predict out-of-voxel echoes in brain MRS data.

This paper introduces an automated MRS analysis workflow that reduces manual steps and integrates with neuroimaging standards, improving efficiency and scalability.

This study compares PRESS and sLASER for detecting metabolite-age correlations in brain regions, finding similar results despite technical differences.

The paper examines age-related changes in metabolite T1 relaxation times in brain regions using a cross-sectional dataset and finds significant decreases in white-matter-rich areas with age.

The paper investigates neurometabolic changes with age using MR spectroscopy in a cross-sectional cohort, finding age correlations for several metabolites but not others.

The paper shows that over half of the variance in 1H-MRS metabolite estimates is shared across all metabolites, with age and sex affecting this common variance.

This study compares metabolite estimation using different macromolecular modeling strategies and finds that cohort-mean measured MM basis functions yield more robust results than subject-specific or parameterized ones.

This study investigates the association between genetic variation in glutamate and GABA genes and brain structure/chemistry in autism, finding differential involvement in specific brain regions.

The paper compares seven algorithms for modelling GABA-edited 1H-MRS data from 222 subjects across 20 sites, finding moderate agreement and highlighting the need for standardized parameters.