Technology giants, Facebook have partnered with the New York University (NYU) School of Medicine’s Department of Radiology to announce a solution that aims to make Magnetic Resonance Imaging (MRI) scans up to ten times faster. The announcement was made by Facebook on the Facebook Engineering Blog in August.
The collaborative research project backed by Facebook will investigate the use of artificial intelligence (AI) to make magnetic resonance imaging (MRI) scans up to 10 times faster.
According to Facebook, if the project is successful, it will make MRI technology available to more people, expanding access to this key diagnostic tool.
Doctors and patients rely on images provided by MRI scanners, especially those with a higher level of detail in tissues such as blood vessels and organs. In scenarios where detailed visualization of tissues is needed, imaging from other forms of medical imaging devices may not suffice.
Despite the visual advantage with MRIs, they are usually pretty slow, taking anywhere from 15 minutes to over an hour, compared with less than a second or up to a minute, respectively, for X-ray and CT scans.
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These long scan times can make MRI machines challenging for young children, as well as for people who are claustrophobic or for whom lying down is painful.
Also, in rural and remote areas, MRI machines are not readily available, resulting in long scheduling backlogs. By boosting the speed of MRI scanners, a larger number of patients can have access to these devices for better diagnostic care.
Sufficiently accelerated MRI devices could also reduce the amount of time patients must hold their breath during imaging of the heart, liver, or other organs in the abdomen and torso. Increased speed could let MRI machines fill the role of X-ray and CT machines for some applications, allowing patients to avoid the ionizing radiation associated with those scans.
The research team on the project have affirmed that the project will initially focus on changing how MRI machines operate. Currently, scanners work by gathering raw numerical data in a series of sequential views and turning the data into cross-sectional images of internal body structures that doctors then use to evaluate a patient’s health. The larger the data set to be gathered, the longer the scan will take.
AI To Be Utilized In The fastMRI Research
Using AI, it may be possible to capture less data and therefore scan faster, while preserving or even enhancing the rich information content of magnetic resonance images. The key is to train artificial neural networks to recognize the underlying structure of the images in order to fill in views omitted from the accelerated scan.
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This approach is similar to how humans process sensory information. When we experience the world, our brains often receive an incomplete picture — as in the case of obscured or dimly lit objects — that we need to turn into actionable information. Early work performed at NYU School of Medicine shows that artificial neural networks can accomplish a similar task, generating high-quality images from far less data than was previously thought to be necessary.
NYU School of Medicine, a department of NYU Langone Health, has a long-standing history of pushing the boundaries of medical research and education to benefit the lives of patients. The Radiology Department’s Center for Advanced Imaging Innovation and Research (CAI²R) includes a multidisciplinary team of engineers, physicists, mathematicians, radiologists, and other clinicians and scientists with key expertise in rapid image acquisition, parallel imaging, and advanced image reconstruction. Their focus is on developing novel imaging technologies and rapidly translating those technologies into clinical practice.
What The Project Hopes To Achieve:
Though this project will initially focus on MRI technology, its long-term impact could extend to many other medical imaging applications. For example, the improvements afforded by AI have the potential to revolutionize CT scans as well.
Advanced image reconstruction might enable ultra-low-dose CT scans suitable for vulnerable populations, such as pediatric patients. Such improvements would not only help transform the experience and effectiveness of medical imaging, but they’d also help equalize access to an indispensable element of medical care.
The fastMRI project inititators (Facebook and NYU) believe the project will demonstrate how domain-specific experts from different fields and industries can work together to produce the kind of open research that will make a far-reaching and lasting positive impact in the world.
This article was originally published by Facebook here with excerpts extracted and reposted.