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Feasibility, image quality, and analytic usability of mobile 64 mT ultra-low-field MRI for infant brain imaging at 3 and 12 months in southern Malawi.

Overview

Authors: Maclean Vokhiwa1,2, Karen Chetcuti3, Frederik Lange4, Niall J. Bourke5, Louise Randall6, Steven Greenstein7, Marc Seal7, Richard Beare7, Adam Hussain8, Padma Rao8, Emil Ljungberg9,10, Francesco Padormo11, John Rogers11, Pip Torelli11, Steve Williams5, Derek K. Jones12, Sean C.L. Deoni13, Sant-Rayn Pasricha6, Kamija S. Phiri1,2, Eric Umar2, Unity Consortium14
ORCID iDs: Maclean Vokhiwa
14 affiliations
  1. Training & Research Unit of Excellence (TRUE), Blantyre, Malawi
  2. Kamuzu University of Health Sciences (KUHeS), Blantyre, Malawi
  3. Department of Radiology, Kamuzu University of Health Sciences (KUHeS), Blantyre, Malawi
  4. Centre for Integrative Neuroimaging (OxCIN), FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, United Kingdom
  5. Centre for Neuroimaging Sciences, Department of Psychology and Neuroscience, King’s College London, London, United Kingdom
  6. Population Health and Immunity, Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
  7. Developmental Imaging, Murdoch Children’s Research Institute, The Royal Children’s Hospital, Victoria, Australia
  8. Department of Medical Imaging, The Royal Children’s Hospital, Parkville, Victoria, Australia
  9. Department of Neuroimaging, King’s College London, London, United Kingdom
  10. Department of Medical Radiation Physics, Lund University, Lund, Sweden
  11. Hyperfine, Inc., Guilford, CT, USA
  12. Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
  13. Maternal, Newborn, Child Nutrition & Health, Gates Foundation, Seattle, WA, United States
  14. Ultra-Low-Field Neuroimaging in The Young (UNITY), United Kingdom
Institutions: Kamuzu University of Health Sciences (Malawi); University of Oxford (United Kingdom); Wellcome Centre for Integrative Neuroimaging (United Kingdom); King's College London (United Kingdom); Walter and Eliza Hall Institute of Medical Research (Australia); Royal Children's Hospital (Australia); Murdoch Children's Research Institute (Australia); Lund University (Sweden); Hyperfine, Inc. (United States) (United States); Cardiff University (United Kingdom); Gates Foundation (United States)
Journal: Journal of magnetic resonance open, volume 28, article None
Dates: published online September 2026; in print September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.jmro.2026.100225 · PMID 42732120 · PMCID PMC13569786 · OpenAlex W7203538366
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), developmental (subfield)
Methods: Connectivity, Spectral & time-frequency, Machine learning, Statistics
Keywords: Ultra-low-field MRI, Infancy, Neuroimaging, Volumetry, Image quality, Feasibility, Africa
Topic: Advanced MRI Techniques and Applications (Radiology, Nuclear Medicine and Imaging, Medicine), according to OpenAlex
Funding: Science for Africa Foundation; Bill and Melinda Gates Foundation (INV-090982, INV-010612); Wellcome Trust (DEL-22-002); European Regional Development Fund (EDCTP2-programme, EDCTP2); North-West University; Foreign, Commonwealth and Development Office
Citations: not cited yet (Europe PMC); 55 references in the paper

Abstract

Mobile ultra-low-field MRI (ULF-MRI) could reduce inequities in infant neuroimaging in sub-Saharan Africa, but evidence of its feasibility, image quality, and analytic usability remains limited. We evaluated the feasibility, image quality, and analytic usability of mobile 64 mT ULF-MRI for non-sedated infant brain imaging at three and twelve months of age within a longitudinal trial platform in southern Malawi. Feasibility was assessed through visit-level scan uptake and sequence completion; image quality through artifact-based quality control (QC) and radiologist interpretability review; and analytic usability through multi-structure volumetry and correspondence across independent processing workflows.

Across 810 eligible visits (3 months: 410; 12 months: 400), 654 MRI sessions were completed (80.7%; 86.6% at 3 months; 74.8% at 12 months), with high sequence acquisition success among completed scans. Of 654 completed sessions, 495 entered artifact-based QC and 442/495 (89.3%) met predefined full-brain quality criteria for volumetric processing; motion-related degradation was the principal determinant of exclusion. Radiologist review (n = 426) rated 396/426 scans (93.0%) as analysable. Volumetry was derived for 215 infants at 3 months and 227 at 12 months, with 99 paired observations demonstrating measurable age-related differences in tissue volumes (grey matter +39.0 ± 7.2%; white matter +48.8± 9.5%). An independent cloud-based pipeline showed high correspondence for supratentorial tissue and total intracranial volume at 12 months (r ≈ 0.97).

These findings demonstrate that non-sedated infant ULF-MRI can acheive high uptake, interpretable imaging, and scalable volumetric processing in a low-resource setting. The results support its analytic readiness for integration into developmental neuroscience research where conventional MRI is unavailable.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

No file of the authors' code could be read here: it is described below, and read at its source.

UNITY-Physics

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: the link answers
Software Heritage: not checked
Found in: the text, “Image quality assessment and processing”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 26 September 2026: the link answers (HTTP 200)
  • 26 September 2026: the link answers (HTTP 200)
At the source: github.com/UNITY-Physics

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 0 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability

Data will be made available on request.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 3, 28 September 2026

  • Publisher: n/a → Elsevier BV
  • Authors: added Maclean Vokhiwa (0000-0002-8706-6031); removed Maclean Vokhiwa
  • Funding: added Science for Africa Foundation; Bill and Melinda Gates Foundation: INV-090982, INV-010612; Wellcome Trust: DEL-22-002; European Commission: EDCTP2-programme, EDCTP2; North-West University; Foreign, Commonwealth and Development Office

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 21 authors, 7 keywords, 54 references.

Cite

This paper

Vokhiwa, M., Chetcuti, K., Lange, F., Bourke, N. J., Randall, L., Greenstein, S., Seal, M., Beare, R., Hussain, A., Rao, P., Ljungberg, E., Padormo, F., Rogers, J., Torelli, P., Williams, S., Jones, D. K., Deoni, S. C., Pasricha, S.-R., Phiri, K. S., . . . Unity Consortium. (2026). Feasibility, image quality, and analytic usability of mobile 64 mT ultra-low-field MRI for infant brain imaging at 3 and 12 months in southern Malawi. Journal of magnetic resonance open, 28, None. https://doi.org/10.1016/j.jmro.2026.100225

BibTeX

@article{vokhiwa2026feasibility,
author = {Vokhiwa, Maclean and Chetcuti, Karen and Lange, Frederik and Bourke, Niall J. and Randall, Louise and Greenstein, Steven and Seal, Marc and Beare, Richard and Hussain, Adam and Rao, Padma and Ljungberg, Emil and Padormo, Francesco and Rogers, John and Torelli, Pip and Williams, Steve and Jones, Derek K. and Deoni, Sean C.L. and Pasricha, Sant-Rayn and Phiri, Kamija S. and Umar, Eric and {Unity Consortium}},
title = {{Feasibility, image quality, and analytic usability of mobile 64 mT ultra-low-field MRI for infant brain imaging at 3 and 12 months in southern Malawi}},
journal = {Journal of magnetic resonance open},
year = {2026},
month = sep,
volume = {28},
pages = {None},
publisher = {Elsevier BV},
issn = {2666-4410},
doi = {10.1016/j.jmro.2026.100225},
url = {https://doi.org/10.1016/j.jmro.2026.100225},
pmid = {42732120},
pmcid = {PMC13569786}
}

RIS

TY - JOUR
AU - Vokhiwa, Maclean
AU - Chetcuti, Karen
AU - Lange, Frederik
AU - Bourke, Niall J.
AU - Randall, Louise
AU - Greenstein, Steven
AU - Seal, Marc
AU - Beare, Richard
AU - Hussain, Adam
AU - Rao, Padma
AU - Ljungberg, Emil
AU - Padormo, Francesco
AU - Rogers, John
AU - Torelli, Pip
AU - Williams, Steve
AU - Jones, Derek K.
AU - Deoni, Sean C.L.
AU - Pasricha, Sant-Rayn
AU - Phiri, Kamija S.
AU - Umar, Eric
AU - Unity Consortium
TI - Feasibility, image quality, and analytic usability of mobile 64 mT ultra-low-field MRI for infant brain imaging at 3 and 12 months in southern Malawi
T2 - Journal of magnetic resonance open
J2 - J Magn Reson Open
PY - 2026
DA - 2026/09/01
VL - 28
SP - None
SN - 2666-4410
PB - Elsevier BV
DO - 10.1016/j.jmro.2026.100225
UR - https://doi.org/10.1016/j.jmro.2026.100225
LA - en
ER -

CSL-JSON

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The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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