Facebook and NYU School of Medicine’s Department of Radiology are collaborating on a research project to investigate the use of artificial intelligence (AI) to make magnetic resonance imaging (MRI) scans up to 10 times faster.
According to a blog post from Larry Zitnick from the Facebook Artificial Intelligence Research (FAIR) group and Daniel Sodickson, M.D., Ph.D, and Michael Recht, M.D., from NYU School of Medicine, if the project, called fastMRI, is successful, it will make MRI technology available to more people, expanding access to this key diagnostic tool. And, the project also represents one of Facebook’s major moves into healthcare.
MRI scanners provide doctors and patients with images that typically show a greater level of detail related to soft tissues — such as organs and blood vessels — than is captured by other forms of medical imaging. But they are relatively 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, Zitnick, Sodickson and Recht wrote in the blog post.
“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. Additionally, there are MRI shortages in many rural areas and in other countries with limited access, resulting in long scheduling backlogs. By boosting the speed of MRI scanners, we can make these devices accessible to a greater number of patients,” they wrote.
The imaging data set used in the project, collected exclusively by NYU School of Medicine, consists of 10,000 clinical cases and comprises approximately 3 million magnetic resonance images of the knee, brain, and liver. To address privacy concerns, all data, including both images and raw scanner data, are fully stripped of patient names and all other protected health information, the two organizations stated.
“The work is fully HIPAA-compliant and approved under NYU Langone’s Institutional Review Board, which oversees all human subject research at the medical center. The project is governed by strict human subject data protection protocols and supported by the world-class information technology team at NYU Langone,” NYU and Facebook officials wrote.
What’s more, the magnetic resonance images used for this project have been scrubbed of any potential distinguishing features. Comparisons of the performance between AI-based reconstructions and traditional reconstructions will, likewise, be devoid of any identifying information. No Facebook data of any kind will be used in the project, the organizations said.
The organizations also plan to open-source their work to allow the wider research community to build on our developments. As the project progresses, Facebook will share the AI models, baselines, and evaluation metrics associated with this research, and NYU School of Medicine will open-source the image data set.
According to Facebook and NYC, the project will initially focus on changing how MRI machines operate. The two organizations are investigating whether AI could allow MRIs 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. This approach is similar to how humans process sensory information,” NYU and Facebook wrote in the blog post.
Sodickson notes in the blog post that early work performed at NYU School of Medicine, a department of NYU Langone Health, 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.
“In practice, reconstructing images from partial information poses an exceedingly hard problem. Neural networks must be able to effectively bridge the gaps in scanning data without sacrificing accuracy. A few missing or incorrectly modeled pixels could mean the difference between an all-clear scan and one in which radiologists find a torn ligament or a possible tumor. Conversely, capturing previously inaccessible information in an image can quite literally save lives,” Zitnick, Sodickson and Recht wrote.
The organizations also said that while the project will initially focus on MRI technology, its long-term impact could extend to many other medical imaging applications, such as CT scans. “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 organizations said.
