In Vivo Imaging With a Low-Cost MRI Scanner and Cloud Data Processing in Low-Resource Settings.

Authors

Teresa Guallart-Naval, Robert Asiimwe, Patricia Tusiime, Mary A Nassejje, Leo Kinyera, Lemi Robin, Maureen Nayebare, Luiz G C Santos, Marina Fernández-García, Lucas Swistunow, José M Algarín, John Stairs, Michael Hansen, Ronald Amodoi, Andrew Webb, Joshua Harper, Steven J Schiff, Johnes Obungoloch, Joseba Alonso

Published in

NMR in biomedicine. Volume 39. Issue 6. Pages e70293.

Abstract

The goal of this work is to demonstrate in vivo imaging with a low-cost, low-field MRI scanner built and operated in Africa and to show how systematic hardware and software improvements can mitigate the main operational limitations encountered in low-resource environments. To this end, a 46-mT Halbach scanner located at the Mbarara University of Science and Technology (Uganda) was upgraded through a complete reorganization of grounding and shielding, installation of new control electronics, and open-source user-interface software. Noise performance was quantified using a standardized protocol and in vivo brain images were acquired with three-dimensional RARE sequences. Distortion correction was implemented using cloud-based reconstructions incorporating magnetic field maps. The revamped system reached noise levels routinely below three times the thermal limit and demonstrated stable operation over multi-day measurements. Three-dimensional T ${}_1$ - and T ${}_2$ -weighted brain images were successfully acquired and distortion-corrected with remote GPU-based reconstructions and near real-time visualization through the user interface. The results show that low-cost MRI systems can achieve clinically relevant image quality when electromagnetic noise and power-grid instabilities are properly addressed. This work highlights the feasibility of sustainable MRI development in low-resource settings and identifies stable power delivery and local capacity building as the key next steps toward clinical translation.

PMID:
42003440
Bibliographic data and abstract were imported from PubMed on 20 Apr 2026.

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