In-vivo imaging with a low-cost MRI scanner and cloud data processing in low-resource settings

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

arXiv · 2511.19226

In-vivo imaging with a low-cost MRI scanner and cloud data processing in low-resource settings

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

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Abstract

Purpose: 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. Methods: 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. Results: The revamped system reached noise levels routinely below three times the thermal limit and demonstrated stable operation over multi-day measurements. Three-dimensional T1- and T2-weighted brain images were successfully acquired and distortion-corrected with remote GPU-based reconstructions and near real-time visualization through the user interface. Conclusions: 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.

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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. 2025-11-24. In-vivo imaging with a low-cost MRI scanner and cloud data processing in low-resource settings. https://arxiv.org/abs/2511.19226

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