Characterization of portable ultra-low field MRI scanners for multi-center structural neuroimaging

dc.contributor.authorLjungberg, Emil
dc.contributor.authorPadormo, Francesco
dc.contributor.authorPoorman, Megan
dc.contributor.authorClemensson, Petter
dc.contributor.authorBourke, Niall
dc.contributor.authorEvans, John C.
dc.contributor.authorGholam, James
dc.contributor.authorVavasour, Irene
dc.contributor.authorKollind, Shannon H.
dc.contributor.authorLafayette, Samson L.
dc.contributor.authorBennallick, Carly
dc.contributor.authorDonald, Kirsten A.
dc.contributor.authorBradford, Layla E.
dc.contributor.authorLena, Beatrice
dc.contributor.authorVokhiwa, Maclean
dc.contributor.authorShama, Talat
dc.contributor.authorSiew, Jasmine
dc.contributor.authorSekoli, Lydia
dc.contributor.authorVan Rensburg, Jeanne
dc.contributor.authorPepper, Michael Sean
dc.contributor.authorKhan, Amna
dc.contributor.authorMadhwani, Akber
dc.contributor.authorBanda, Frank A.
dc.contributor.authorMwila, Mwila L.
dc.contributor.authorCassidy, Adam R.
dc.contributor.authorMoabi, Kebaiphe
dc.contributor.authorSephi, Dolly
dc.contributor.authorBoakye, Richard A.
dc.contributor.authorAe-Ngibise, Kenneth A.
dc.contributor.authorAsante, Kwaku P.
dc.contributor.authorHollander, William J.
dc.contributor.authorKaraulanov, Todor
dc.contributor.authorWilliams, Steven C.R.
dc.contributor.authorDeoni, Sean
dc.date.accessioned2025-06-19T11:12:38Z
dc.date.available2025-06-19T11:12:38Z
dc.date.issued2025-06
dc.descriptionDATA AVAILABILITY STATEMENT : All tabular data and tools used for data analysis in this work are shared open source together with some example imaging data. UNITY QC paper code: https://github.com/UNITY-Physics/unity_qa_paper — Tabular data and code for reproducing (most of) the figures in the paper. The GHOST repository: https://github.com/UNITY-Physics/GHOST — The main tool used for processing the phantom data. Works together with the fiducial segmentation models and phantom 3T template linked below. Fiducial segmentation models: https://doi.org/10.6084/m9.figshare.26892781.v1 — Pre-trained nnUNet models for segmenting the fiducial spheres in the UNITY phantom. UNITY Phantom reference 3T template: https://doi.org/10.6084/m9.figshare.26954638.v1 — High resolution template of the UNITY phantom for use with GHOST. Example QC Data: https://doi.org/10.6084/m9.figshare.26954056.v1 — Example QC data for UNITY acquired on the Swoop scanner at Lund University. SOP and instructional videos: www.unity-mri.com/info/qa.
dc.description.abstractThe lower infrastructure requirements of portable ultra-low field MRI (ULF-MRI) systems have enabled their use in diverse settings such as intensive care units and remote medical facilities. The UNITY Project is an international neuroimaging network harnessing this technology, deploying portable ULF-MRI systems globally to expand access to MRI for studies into brain development. Given the wide range of environments where ULF-MRI systems may operate, there are external factors that might influence image quality. This work aims to introduce the quality control (QC) framework used by the UNITY Project to investigate how robust the systems are and how QC metrics compare between sites and over time. We present a QC framework using a commercially available phantom, scanned with 64 mT portable MRI systems at 17 sites across 12 countries on four continents. Using automated, open-source analysis tools, we quantify signal-to-noise, image contrast, and geometric distortions. Our results demonstrated that the image quality is robust to the varying operational environment, for example, electromagnetic noise interference and temperature. The Larmor frequency was significantly correlated to room temperature, as was image noise and contrast. Image distortions were less than 2.5 mm, with high robustness over time. Similar to studies at higher field, we found that changes in pulse sequence parameters from software updates had an impact on QC metrics. This study demonstrates that portable ULF-MRI systems can be deployed in a variety of environments for multi-center neuroimaging studies and produce robust results.
dc.description.departmentImmunology
dc.description.librarianhj2025
dc.description.sdgSDG-03: Good health and well-being
dc.description.sponsorshipWellcome Leap and Bill and Melinda Gates Foundation.
dc.description.urihttps://onlinelibrary.wiley.com/journal/10970193
dc.identifier.citationLjungberg, E., Padormo, F., Poorman, M. et al. 2025, 'Characterization of portable ultra-low field MRI scanners for multi-center structural neuroimaging', Human Brain Mapping, vol. 46, no. 8, art. e70217, pp. 1-15, doi : 10.1002/hbm.70217.
dc.identifier.issn1065-9471 (print)
dc.identifier.issn1097-0193 (online)
dc.identifier.other10.1002/hbm.70217
dc.identifier.urihttp://hdl.handle.net/2263/102890
dc.language.isoen
dc.publisherWiley
dc.rights© 2025 The Author(s). Human Brain Mapping published by Wiley Periodicals LLC. This is an open access article under the terms of the Creative Commons Attribution License.
dc.subjectMulti‐center
dc.subjectNeuroimaging
dc.subjectPhantom
dc.subjectQuality control
dc.subjectUltra-low field MRI (ULF-MRI)
dc.subjectMagnetic resonance imaging (MRI)
dc.titleCharacterization of portable ultra-low field MRI scanners for multi-center structural neuroimaging
dc.typeArticle

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