Facebook and NYU Want to Speed up MRI Scans
Orange County, CA - August 24th, 2018 - Facebook Artificial Intelligence Research (FAIR) group is collaborating with NYU School of Medicine’s Department of Radiology in a new research project that will examine the use of artificial intelligence (AI) to make magnetic resonance imaging (MRI) scans up to 10 times faster. This new collaborative research project is called fastMRI, and researchers are looking into if AI can be used to reconstruct MR images in new, faster ways.
An MRI is used in radiology to form pictures of the anatomy and the physiological processes of the body in both health and disease. It is a very common procedure that uses a strong magnetic field and radio waves, to create detailed images of the organs and tissues within the body. It is a highly technical and informative way of imaging a human body. A scan can take 10 minutes to over an hour to complete, depending on the part of the body being imaged.
It might be possible to capture less data which will result in faster scan times while preserving or even enhancing the rich information content of an MRI using Artificial Intelligence (AI). It’s essential to train artificial neural networks to identify the fundamental structure of the images in order to fill in views omitted from the accelerated scan. It is very similar to how a human eye processes an image; the human brain receives an incomplete picture that turns into meaningful data.
NYU School of Medicine performed earlier work in April of 2017 that showed artificial neural networks can accomplish a similar task with generating high-quality images from far less information than was previously thought to be necessary. However, neural networks need to be able to efficiently bridge the gaps in scanning data, without sacrificing accuracy. A blog post on the topic states, “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.”
Faster MRI scan times could benefit areas that have a shortage of scanners. Shorter scans could allow more patients to be imaged and faster MRIs could also take the place of X-rays and CT scans for some applications (which both scans take less than a minute).
NYU School of Medicine exclusively collected the imaging dataset that was used in the project. It consists of 10,000 clinical cases that have approximately 3 million magnetic resonance images of the brain, knee, and liver. All of the images and raw scanner data were stripped of patient names, and all other protected health information. "Any labels associated with raw image data have been stripped," stressed Facebook's AI research manager Larry Zitnick.
This effort can reduce medical costs, and improve all-around patient experience. Hospitals around the world would be able to benefit from this project, which could speed up, and bring new efficiency to this process. Researchers are focusing on applying the power of machine learning to reconstruct the most high-value images in completely new ways. This project demonstrates how domain-specific experts, from different fields and industries can come together and produce the open research needed to make a long-lasting impact in the world.
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Facebook and NYU Want to Speed up MRI Scans Orange County, CA – August 24th, 2018 – Facebook Artificial Intelligence Research (FAIR) group is collaborating with NYU School of Medicine’s Department of Radiology in a new research project that will examine the use of artificial intelligence (AI) to make magnetic resonance imaging (MRI) scans up to […]