In-vivo imaging with a low-cost MRI scanner and cloud data processing in low-resource settings
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| author | Guallart-Naval, Teresa Asiimwe, Robert Tusiime, Patricia Nassejje, Mary A. Kinyera, Leo Robin, Lemi Nayebare, Maureen Santos, Luiz G. C. Fernández-García, Marina Swistunow, Lucas Algarín, José M. Stairs, John Hansen, Michael Amodoi, Ronald Webb, Andrew Harper, Joshua Schiff, Steven J. Obungoloch, Johnes Alonso, Joseba |
| author_facet | Guallart-Naval, Teresa Asiimwe, Robert Tusiime, Patricia Nassejje, Mary A. Kinyera, Leo Robin, Lemi Nayebare, Maureen Santos, Luiz G. C. Fernández-García, Marina Swistunow, Lucas Algarín, José M. Stairs, John Hansen, Michael Amodoi, Ronald Webb, Andrew Harper, Joshua Schiff, Steven J. Obungoloch, Johnes Alonso, Joseba |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_19226 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | In-vivo imaging with a low-cost MRI scanner and cloud data processing in low-resource settings Guallart-Naval, Teresa Asiimwe, Robert Tusiime, Patricia Nassejje, Mary A. Kinyera, Leo Robin, Lemi Nayebare, Maureen Santos, Luiz G. C. Fernández-García, Marina Swistunow, Lucas Algarín, José M. Stairs, John Hansen, Michael Amodoi, Ronald Webb, Andrew Harper, Joshua Schiff, Steven J. Obungoloch, Johnes Alonso, Joseba Medical Physics 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. |
| title | In-vivo imaging with a low-cost MRI scanner and cloud data processing in low-resource settings |
| topic | Medical Physics |
| url | https://arxiv.org/abs/2511.19226 |