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Resumen

Quantitative susceptibility mapping (QSM) is an MRI-based, computational method for anatomically localizing and measuring concentrations of specific biomarkers in tissue such as iron. Growing research suggests QSM is a viable method for evaluating the impact of iron overload in neurological disorders and on cognitive performance in aging. Several software toolboxes are currently available to reconstruct QSM maps from 3D GRE MR Images. However, few if any software packages currently exist that offer fully automated pipelines for QSM-based data analyses: from DICOM images to region-of-interest (ROI) based QSM values. Even less QSM-based software exist that offer quality control measures for evaluating the QSM output. Here, we address these gaps in the field by introducing and demonstrating the reliability and external validity of Ironsmith; an open-source, fully automated pipeline for creating and processing QSM maps, extracting QSM values from subcortical and cortical brain regions (89 ROIs) and evaluating the quality of QSM data using SNR measures and assessment of outlier regions on phase images. Ironsmith also features automatic filtering of QSM outlier values and precise CSF-only QSM reference masks that minimize partial volume effects. Testing of Ironsmith revealed excellent intra- and inter-rater reliability. Finally, external validity of Ironsmith was demonstrated via an anatomically selective relationship between motor performance and Ironsmith-derived QSM values in motor cortex. In sum, Ironsmith provides a freely-available, reliable, turn-key pipeline for QSM-based data analyses to support research on the impact of brain iron in aging and neurodegenerative disease.

Idioma originalEnglish
Número de artículo118835
PublicaciónNeuroImage
Volumen249
DOI
EstadoPublished - abr 1 2022

Nota bibliográfica

Publisher Copyright:
© 2021

Financiación

This work was supported by the National Institutes of Health (grant numbers NIA R01 AG055449, NIA R01 AG068055, NIA P30 AG072946 and NIGMS S10 OD023573). The content is solely the responsibility of the authors and does not necessarily represent the official views of these granting agencies. The authors thank Shoshana Bardach for help with participant recruitment and Beverly Meacham and Eric Foreman for assisting/conducting the MRI scans. We also thank Drs. Xu Li, Hanzhang Lu, Anders Anderson, Ahmed Bahrani, Pascal Spincemaille and Yi Wang for helpful discussions.

FinanciadoresNúmero del financiador
National Institutes of Health (NIH)
U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)R01 AG055449, R01AG068055, P30 AG072946
National Institute of General Medical Sciences DP2GM119177 Sophie Dumont National Institute of General Medical SciencesS10 OD023573

    ASJC Scopus subject areas

    • Neurology
    • Cognitive Neuroscience

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