AI rapidly produces higher quality medical imaging from less data

Researchers at Massachusetts General Hospital have developed a new medical imaging technique based on artificial intelligence designed to enable clinicians to acquire higher quality images without having to collect additional data. The AI technique--called AUTOMAP (automated transform by manifold approximation)--produces high-quality images in less time with MRI or with lower radiation doses with X-ray, CT and PET. And, as a result of its very quick processing speed, the approach could help in making real-time clinical decisions about imaging protocols while the patient is in the scanner, according to MGH researchers. A description of the technique, published last week in the journal Nature, shows dramatic differences between images reconstructed from the same data with conventional approaches compared to AUTOMAP. "What we did was condition a neural network through machine learning to recognize what makes an image an image," says Matthew Rosen, director of the Low Field MRI and Hyperpolarized Media Laboratory and co-director of the Center for Machine Learning at MGH's Athinoula A. Martinos Center for Biomedical Imaging.