Our technique greatly reduces the appearance of banding, without requiring any additional computation or post-processing at reconstructiontime.

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.

If you've ever had an MRI scan before, you'll know how unsettling the experience can be. You're placed in a claustrophobia-inducing tube and asked to stay completely still for up to an hour while unseen hardware whirs, creaks, and thumps around you like a medical poltergeist. New research, though, suggests AI can help with this predicament by making MRI scans four times faster, getting patients in and out of the tube quicker. The work is a collaborative project called fastMRI between Facebook's AI research team (FAIR) and radiologists at NYU Langone Health. Together, the scientists trained a machine learning model on pairs of low-resolution and high-resolution MRI scans, using this model to "predict" what final MRI scans look like from just a quarter of the usual input data.

Magnetic Resonance Imaging (MRI) has been providing physicians with vital insights into patients' insides since their development in the 1970s. However, the machines operate at a glacially slow pace and require the patient remain perfectly still. This makes them ill-suited for use with small children (who'd have to be sedated) and people experiencing time-critical medical emergencies such as strokes. Now, after two years of research, teams from Facebook AI and NYU Langone Health have developed a neural network that can cut the amount of time people have to spend in an MRI machine from more than an hour to just a few minutes. The network, dubbed fastMRI, shortens the scanning time because it only requires a quarter as much data to resolve the image.

Facebook artificial intelligence (AI) is working alongside radiologists to create MRI scans that last as little as five minutes, new research suggests. Current MRI scans require patients, who are often in pain, sit perfectly still for up to an hour-and-a-half while a scan is completed. This is due to such scans taking a series of 2D images of a person's insides, which are combined to create a 3D picture. Known as the FastMRI project, the new technique involves taking just a few 2D images, before AI that has been trained on millions of other scans'fills in the gaps'. Early results show the technique may be feasible, however, such projects take time.

Magnetic resonance imaging is an invaluable tool in the medical field, but it's also a slow and cumbersome process. It may take fifteen minutes or an hour to complete a scan, during which time the patient, perhaps a child or someone in serious pain, must sit perfectly still. NYU has been working on a way to accelerate this process, and is now collaborating with Facebook with the goal of cutting down MRI durations by 90 percent by applying AI-based imaging tools. It's important at the outset to distinguish this effort from other common uses of AI in the medical imaging field. An X-ray, or indeed an MRI scan, once completed, could be inspected by an object recognition system watching for abnormalities, saving time for doctors and maybe even catching something they might have missed.