Cheaper MRI machine could improve access to neuroimaging

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International researchers have developed a prototype MRI machine that can be built at low cost and produces high-quality neuroimaging, which could help provide access to MRI across low- and middle-income countries. By using deep learning, the team was able to cancel electromagnetic interference and the machine produced high quality brain images that could be used to successfully diagnose brain abnormalities, including stroke and tumours. When compared to conventional 3 Tesla MRI scans, their machine detected most key issues in 25 patients. The team estimates their machine could be built with material costs under US$20,000 and could operate from a standard AC wall power outlet, with neither radiofrequency nor magnetic shielding. They are making the key code and designs freely available in a public online repository.

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From: Springer Nature

Neuroscience: Inexpensive MRI scanner could improve access to neuroimaging

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A prototype MRI machine that can be built at low cost and produces high quality neuroimaging is reported in Nature Communications. The machine can help provide access to MRI across low- and middle-income countries, as well as at the point of care.

Magnetic resonance imaging (MRI) is the most valuable clinical tool used for assessing brain injuries and disorders, but it is estimated that around 70% of the world’s population have little or no access to it. Conventional MRI machines cost around US$1–3 million and have maintenance costs of around US$15,000 per month. They also have high power requirements to, for example, refrigerate the superconducting magnets.

Ed X. Wu and colleagues detail the development of a compact, low-cost, mobile, ultra-low field (0.055 Tesla) MRI machine. They estimate the machine could be built in quantity with material costs under US$20,000 and could operate from a standard AC wall power outlet, with neither radiofrequency nor magnetic shielding. By using deep learning, they were able to cancel electromagnetic interference and the machine produced high quality structural neuroimaging that could be used to successfully diagnose brain abnormalities. The authors conducted preliminary assessments with 25 patients to diagnose neurological diseases, including stroke and tumours. When compared to conventional 3 Tesla MRI scans, their machine detected most key pathologies in all patients.

The authors conclude that this technology has the potential to fulfil unmet clinical needs across healthcare communities. They are making the key code and designs freely available in a public online repository.

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conference:
Nature Communications
Research:Paper
Organisation/s: The University of Hong Kong, China
Funder: This work was supported by Hong Kong Research Grant Council (R7003-19F, HKU17112120 and HKU17127121 to E.X.W., and HKU17103819, HKU17104020 and HKU17127021 to A.T.L.L.), Lam Woo Foundation, and Guangdong Key Technologies for Treatment of Brain Disorders (2018B030332001) to E.X.W. We would like to thank Dr. X. Ma, Messrs. C. Man, V. Lau, C. Ho, Z. Yi, J. Hu, S. Su, Z. Huang, E. Hui, and Ms. N. Hou and L. Xie for their technical assistance, Drs. S. Lau, B. Taw, K. Cheng, L. Li, J. Zhuang and the clinical teams at the Neurosurgery Division of Department of Surgery and Neurology Division of Department of Medicine, Queen Mary Hospital for their clinical advice, and Ms. J. Chau, R. Liu and V. Sin for their assistance in handling the logistics of patient recruitment. We would also like to thank Drs. P. Khong, W. Chew, J. Gore, P. van Zijl, G. Pang, B. Rosen, and K. Chan for discussions.
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