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

TL;DR

This study demonstrates that a low-cost, low-field MRI scanner operated in Africa can produce clinically relevant images through hardware upgrades and cloud-based processing, enabling sustainable imaging in low-resource settings.

Contribution

The paper presents systematic hardware and software improvements that enable low-cost MRI systems to achieve high-quality imaging in low-resource environments.

Findings

Noise levels below three times the thermal limit achieved

Stable operation over multi-day measurements demonstrated

High-quality brain images acquired and distortion-corrected remotely

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.