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

TL;DR

The paper tackles the challenge of bringing MRI to low-resource settings by demonstrating in-vivo imaging with a 46 mT Halbach-based scanner built in Africa, supported by hardware and software upgrades and cloud-based reconstructions. The core approach combines rigorous EMI shielding, grounding improvements, new RF coils, and updated open-source control software with Tyger-enabled cloud processing to produce 3D T1- and T2-weighted brain images despite strong B0 inhomogeneity. Key findings show noise levels approaching the thermal limit and successful distortion-corrected imaging, validating the feasibility of sustainable, low-cost MRI in LMICs while highlighting power stability and local capacity development as critical next steps. The work underscores the potential for scalable, open-source LF-MRI deployment in low-resource environments, contingent on robust power solutions, local manufacturing and maintenance capacity, and accessible cloud-based processing workflows.

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.

Figures (10)

• Figure 1: System evolution at MUST. a) Scanner after the initial assembly in 2023. b) First reconstructed image (bell pepper). Front (c), rear (d), and global (e) views of the MUST scanner in the present configuration, highlighting the improved electronics setup, RF coils, and mechanical integration.
• Figure 2: Electromagnetic environment at MUST. a) Noise levels logged over a few hours after the revamp but with suboptimal grounding, highlighting the strong dependence of the measured noise floor on building activity and grid conditions. b) Inserts show representative traces and spectra under "quiet" and "noisy" conditions. c) Frequency of power-off events recorded over a two-week period.
• Figure 3: Schematic of the complete electronics setup of the MUST scanner after the revamp. Red lines indicate RF signal paths (transmit and receive), green lines correspond to gradient drive signals, orange lines denote power connections, and black lines represent ground. The diagram highlights the main subsystems, including the magnet, RF and gradient coils, TxRx switch, LNA, 1 W RFPA, GPA, and the MaRCoS control unit.
• Figure 4: Electromagnetic noise characterization and suppression in the MUST scanner. a) Four-step protocol for systematic noise assessment in low-field MRI systems, adapted from Ref. Guallart-Naval2025a. Steps 2--4 involve repeated measurements through stages I--V to isolate the contribution of each subsystem as the scanner is progressively assembled and powered. The expression $v_{\mathrm{out}} = G \cdot \sqrt{k_\text{B} T R \Delta f} = G \cdot v_{\mathrm{n}} / 2$ defines the expected baseline noise voltage, where $G$ is the linear transducer gain of the amplifier, $k_\text{B} \approx 1.38e-23J/K$ is Boltzmann’s constant, and $v_{\mathrm{n}}$ is the RMS voltage noise generated by a resistor $R$ at temperature $T$ over a bandwidth $\Delta f$. b) Normalized noise measurements, expressed as multiples of the theoretical thermal limit, for different hardware configurations.
• Figure 5: Long-term noise monitoring with the revamped MUST scanner optimally shielded. The plot shows the evolution of the RMS noise level over a continuous period of nearly three days.