Artificial intelligence (AI) can reconstruct coarsely-sampled, rapid magnetic resonance imaging (MRI) scans into high-quality images with similar diagnostic value as those generated through traditional MRI, according to a new study by the NYU Grossman School of Medicine and Meta AI Research.
Using AI to reconstruct MRI scans which are four times faster than standard scans promises to expand MRI access to more patients and reduce wait times for appointments, the study says.
The study, published on January 17 in Radiology, is part of the fastMRI initiative established by NYU Langone Health and Meta AI Research (formerly Facebook) in 2018. This initiative, aimed at using AI to make MRI faster, resulted in an AI model jointly developed by Meta AI researchers and NYU Langone imaging scientists and radiologists. It also produced the largest-ever publicly-available collection of raw MRI data, which has been used by scientists and engineers around the world.
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In an earlier “proof-of-principle” study, the NYU Langone team simulated accelerated scans by removing about three-fourths of the raw data acquired by conventional, slow MRI scans. The fastMRI AI model then generated images that matched those created from the slower scans. In this new study, the researchers performed accelerated scans with only one-fourth of the total data and used the AI model to “fill in” the missing information, similar to the way the brain builds images by filling in missing visual information from local context and previous experiences. In both studies, the fastMRI scans were found to be as accurate as traditional scans, with better quality.
“Our new study translates the results from the earlier laboratory-based study and applies it to actual patients,” says Michael P. Recht, MD, the Louis Marx Professor of Radiology and chair of the Department of Radiology at NYU Grossman School of Medicine. “FastMRI has the potential to dramatically change how we do MRI and increase accessibility of MRI to more patients.”
In the new study, a total of 170 participants received a diagnostic knee MRI using a conventional MRI protocol followed by an accelerated AI protocol between January 2020 and February 2021. Each examination was reviewed by six musculoskeletal radiologists, who looked for signs of meniscal or ligament tears and bone marrow or cartilage abnormalities. The radiologists evaluating the scans were not told which images were reconstructed with AI, and to limit the potential for recall bias, the evaluations of the standard images and AI-accelerated images were spaced at least four weeks apart.
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The radiologists judged the AI-reconstructed images to be diagnostically equivalent to the conventional images for detecting tears or abnormalities, and found the overall image quality of the accelerated scans to be significantly better than the conventional images.
“This research represents an exciting step towards translating AI accelerated imaging to clinical practice,” says Patricia M. Johnson, PhD, assistant professor in the Department of Radiology at NYU Grossman School of Medicine. “It truly paves the way for more innovation and advancements in the future.”
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