Inexpensive, AI-driven MRI machines could revolutionize medical imaging

Since it was introduced in the 1970s, the MRI has become one of the most impactful imaging techniques in medicine. MRIs are highly potent and versatile, capable of offering much better resolutions than a CT scan and being used in a wide array of situations, from scanning the brain to looking for tumors. But there’s a big problem: the conventional MRI is expensive to buy and maintain.

This is why a new study published in Nature Communications is so exciting. In it, researchers from the University of Hong Kong describe the construction of a new type of MRI that can be built for a fraction of the cost of existing machines.

Image credits: Liu et al (2021).

Democratizing MRIs

Ed X. Wu has been working in MRI research for the past 30 years. He’s worked on the engineering side as well as on image formation and biomedical applications. He’s seen the field grow and develop, as both the technology and the algorithms that operate MRI machines have become more capable and elegant.

“However, these continuously evolving high-end features also drive up the complexity of these scanners,” Wu tells ZME Science, “thus further increasing the cost of purchasing, hosting, and maintaining these clinical MRI scanners.”

Although the MRI is widely considered to be the most valuable and sophisticated medical imaging technology in modern healthcare, Wu explains, it comes at a cost of over $1 million per unit, and a maintenance cost of around $15,000 per month. As a result, despite their utility, MRIs are hardly affordable. Every hospital in the world needs at least one, but 2 in 3 people worldwide have limited or no MRI access.

“The accessibility to clinical MRI scanners is very low,” Wu continues. The total number of clinical scanners is only about 50,000 in the entire world. They are mostly installed inside the highly specialized radiology departments or centralized imaging facilities, operated by highly trained technicians. Meanwhile, there are actual unmet clinical needs for imaging needs in almost in every corner of healthcare, as demonstrated by the success of ultrasound imaging and x-ray imaging.”

Since MRI is especially used to diagnose conditions, not having access to one can delay or even prevent the discovery and treatment of serious medical conditions, increasing medical risks for billions of patients around the world. Having access to an MRI, even a less performant one, could save a lot of lives and improve many livelihoods.

“In short, we need to democratize MRI technologies to serve healthcare at low cost and large scale,” Wu explains.

In order to do this, the cost and complexity of MRI scanners must be brought down substantially. It’s not just the engineering part, but also the installation, maintenance, and operation costs that need to be brought down. For instance, commercial MRIs typically require high power outputs, which may not be available in some places. To achieve this, researchers developed an MRI that works at a very low field and can be constructed for only $20,000.

Lowering the Teslas

An MRI scanner is essentially a giant magnet. It employs powerful, superconducting magnets that force the protons in the human body to align to its magnetic field. To get a sense of how strong the magnet is, most MRIs operate at 1.5 Teslas (although the range can vary from 0.2 to 3 Teslas) and the magnetic field of the Earth is around 0.0000305 Teslas.

The MRI prototype developed by Wu and colleagues operates at 0.055 Teslas, much lower than existing commercial units. It can operate from a standard AC wall power outlet and requires neither radiofrequency (RF) nor magnetic shielding.

Images obtained with the low-cost MRI. Image credits: Liu et al.

The shielding part is particularly exciting. Normally, MRIs need the shielding to eliminate interference (for instance, with other electronic devices) — but researchers managed to eliminate the need for shielding by using a deep learning algorithm, Wu tells ZME Science:

“Our innovations encompass three aspects: (i) we eliminated the bulky RF shielding room requirement through deep learning, thus the MRI scan can now be made in open space; (ii) we implemented and demonstrated the feasibility of key and widely adopted clinical brain imaging protocols on this low-cost platform, which were previously believed challenging if not impossible at very low field and on low-cost hardware platforms; and (iii) we performed preliminary clinical study and validated results by directly comparing to 3T results.”

It’s not the first time something like this was attempted, but this innovation was only possible thanks to breakthroughs on the algorithm side. “In short, it’s our new algorithms & hardware concept that made this advance possible,” the researcher tells me in an email. In fact, Wu expects much of the innovation in the MRI field to come on the computing side.

“I believe computing and big-data will be an integral as well as inevitable part of the future MRI technology.  Given the inherent nature of MRI, I believe widely deployed MRI technologies will lead to immense opportunities in the future through data-driven MRI image formation and diagnosis in healthcare. This will lead to low-cost, effective, and more intelligent clinical MRI applications, ultimately benefiting more patients.”

For now, at least, the new technology isn’t meant to replace conventional MRIs, but rather to complement them and offer a low-cost solution where none is currently available. But if Wu is right and low-cost computing and AI can help push the field even further, we may be seeing these in hospitals in the not too distant future.

Wu hopes that this research could inspire more engineering and data scientists to develop and adopt such low-cost and low-power MRI technology — both in developed and underdeveloped countries. He believes that without any cost increase, the prototype can be improved to achieve more usable image quality and become a valuable tool for medical diagnosis.

“Our body is mostly made of water molecules, on which MRI thrives — MRI is a gift to mankind from nature, we’ve got to use it more,” the researcher concludes.

The study was published in Nature Communications.

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