“Deep Learning Speeds MRI Scans”
Communications of the ACM, April 2021, Vol. 64 No. 4, Pages 12-14
By Paul Marks
“Deep Learning is much better than the traditional parallel imaging and compressed sensing approaches.”
Since its invention in the 1970s, magnetic resonance imaging (MRI) has opened up a window onto the world beneath our skin. By exploiting the way the nuclei of hydrogen atoms in water and fat molecules resonate in a strong magnetic field, MRI can generate high-contrast three-dimensional images of soft body tissues, joints, and bones. MRI allows clinicians to see evidence of injury and disease within the body, ranging from torn muscle to damaged cartilage, ligaments, and tendons, as well as tumors or other disease lesions within major organs, and blood-flow blockages in the brain, all without the ionizing radiation of the X-rays used in computed tomography (CT) scans.
There is, however, a considerable usability problem with the MRI scanner as we currently know it: the technology takes far too long to acquire images, forcing patients to lie still in the confined maw of a massive magnet for up to an hour. With the observable world reduced to a halo of grayish plastic just inches from one’s nose, it is a particularly tough experience for those suffering from claustrophobia. It can be disturbingly noisy, too: the scanner’s magnetic components can rattle at 110 decibels or more when energized.
“It can take typically three or four minutes to acquire each magnetic resonance image, and if you’re lying on the table for 30 or 40 minutes, or sometimes even an hour, depending on the type of exam, it gets hard to lie still for that entire time. It’s uncomfortable for the patient, and especially so if they’re a child,” says Michael Recht, Louis Marx Professor and chairman of the department of radiology at NYU Langone Health.
Help is now at hand, and from an unlikely quarter. Facebook, the Palo Alto, CA-based online social network, has teamed up with radiologists at New York University’s Langone Medical Center in Manhattan to develop an artificial intelligence (AI)-based imaging accelerator for MRI scanners.
What Facebook and NYU Langone have developed is a deep learning neural network (DNN) that allows MRI scans to be performed many times faster. Called FastMRI, the DNN, has been trained to generate MRI images using far less magnetic resonance data than before—and that sparse data requirement significantly accelerates the scan time.
About the Author:
Paul Marks is a technology journalist, writer, and editor based in London, U.K.
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