Making MRI Technology Available to Large Populations Around the World


Magnetic resonance imaging (MRI) is a common, albeit expensive, method of diagnosing brain lesions and strokes. However, because of its high procurement, installation, and operating expenses, it is out of reach for much of the poor world.

The University of Hong Kong (HKU) has created a novel magnetic resonance imaging (MRI) technique, the ultra-low field (ULF) 0.055 Tesla brain MRI, that can be powered by a conventional AC wall outlet and does not require radiofrequency or magnetic shielding. Furthermore, although a standard MRI machine might cost up to $3 million, the ULF-MRI scanner costs a fraction of that. Professor Ed X. Wu, Chair of Biomedical Engineering and Lam Woo Professorship in Biomedical Engineering, Department of Electrical and Electronic Engineering, HKU, led the research group. Nature Communications reported the findings, which were also featured in Nature Asia and Scientific American.


One of the three major ULF-MRI academic research groups in the world, with one based at Harvard/MGH, is dedicated to creating unique ULF-MRI technologies, and the HKU team is one of them. Professor Wu and other academics agree that their goal is to popularise and expand the use of MRI. Professor Wu, an MRI researcher for over 30 years, is overjoyed and fulfilled by the development of a “scaled-down” MRI scanner that is significantly more economical than what is now available in hospitals. Professor Wu explained that the human body is largely made up of water molecules, which MRI thrives on. MRI is a gift from nature, and we need to make better use of it. As a diagnostic tool, it is currently underutilized.


More than 90% of MRI scanners are thought to be in high-income nations, with two-thirds of the world’s population lacking access. The overall number of clinical scanners in the world is believed to be around 50,000. The ULF 0.055 Tesla brain MRI design and algorithms have been released open-source knowledge by the HKU team, and are available to anyone interested in further refining the technology or implementing it in various fields. This effectively opens the door to developments in MRI applications in numerous parts of healthcare provision. Professor Wu predicted that this will be a large field and that the team had established the concept and viability of a simpler version of MRI. There are numerous options for moving forward.


The team has removed a constraint in traditional MRI, namely the necessity to be protected from the outside radiofrequency signal, which results in a bulky, non-mobile set-up, by using a deep learning method. Existing MRI scanners are essentially enormous magnets that require a specially designed facility to insulate them from outside signals and contain the high magnetic fields created by their superconducting magnets, which necessitate expensive liquid helium cooling systems. The latest development was made feasible by the team’s novel computer and hardware concept.


Professor Wu believes that a critical mass of scholars can push knowledge to new heights. The open-source strategy, he said, is the quickest way to distribute knowledge. MRI may one day be employed in domains other than radiology, such as pediatrics, neurosurgery, and the emergency room. He stated that the team welcomes more people from the scientific, clinical, and industrial sectors to participate in studies that will enhance healthcare.


His team had validated the results of employing ULF-MRI by comparing them to pictures obtained from a standard 3 Tesla MRI scanner in collaboration with Professor Gilberto Leung of Neurosurgery and other clinicians at Queen Mary Hospital. Despite the lack of clarity and resolution required for precision diagnoses, they were able to identify the majority of the same diseases, including stroke and tumor outcomes.


“I believe computing and big data will be a fundamental and inevitable aspect of future MRI technology,” Professor Wu stated. Given the fundamental nature of MRI, I believe that widely deployed MRI technologies will open up enormous prospects for data-driven MRI picture generation and diagnostics in healthcare in the future. This will result in more patients benefiting from low-cost, effective, and intelligent clinical MRI applications.”