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NYU, Facebook Offer Dataset for MRI Artificial Intelligence Project

NYU and Facebook have released a large set of open source MRI data as they work to use artificial intelligence to speed up the diagnostic process.

NYU and Facebook offer dataset for MRI artificial intelligence project

Source: Thinkstock

By Jessica Kent

- NYU School of Medicine’s Department of Radiology, in collaboration with Facebook AI Research (FAIR), are releasing a large-scale, open source MRI dataset as part of fastMRI, a project that aims to accelerate MRI scans using artificial intelligence (AI).

This initial dataset will include more than 1.5 million anonymous MRI images of the knee, drawn from 10,000 scans, in addition to raw measurement data from nearly 1600 scans. The data release is the newest phase in the fastMRI collaboration, which was announced by Facebook and NYU in August 2018.

fastMRI will leverage AI to change how MRI machines operate, allowing MRIs to capture less data and scan faster while still maintaining the quality of images. The newly released set of images will facilitate the development of AI systems to make MRI scans 10 times faster.

“We hope that the release of this landmark data set, the largest-ever collection of fully-sampled MRI raw data, will provide researchers with the tools necessary to overcome the challenges inherent in accelerating MR imaging. This work has the potential to not only help increase access to MR imaging, but also improve patient care worldwide,” says Michael P. Recht, MD, chair and the Louis Marx Professor of Radiology at NYU Langone Health.

This imaging set represents the largest public release of raw MRI data to date. Future releases will include data from liver and brain scans.

“This collaboration focuses on applying the strengths of machine learning to reconstruct high-value images in new ways. Rather than using existing images to train AI algorithms, we will radically change the way medical images are acquired in the first place,” says Daniel Sodickson, MD, PhD, professor of radiology and neuroscience and physiology and director of the Center for Advanced Imaging Innovation and Research (CAI2R).

“Our aim is not merely enhanced data mining with AI, but rather creating new capabilities for medical visualization, to benefit human health.”

Leaders of the fastMRI project believe that if it is successful, it could decrease the need for anesthesia or sedation for patients that find it challenging to complete the lengthy test, and increase MRI access in underserved areas.

“fastMRI not only could have an important impact in the medical field, it's also an interesting research challenge that will help to advance the field of AI,” said Larry Zitnick, Research Manager, Facebook AI Research.

“To be medically useful, our AI-reconstructed images need to be more than just good-looking, they must also be accurate representations of the ground-truth, even though they're created from significantly less data. The dataset NYU Langone is releasing and the baseline models we've open-sourced will enable other researchers to join us in working on this challenging problem, and we believe this open approach will bring about positive results more quickly.”

The fastMRI partnership will foster research producibility and empower the wider community of AI and medical imaging scientists to improve MRI scanning.

“Our collaborative priority for the next phase of this work is to use the experimental foundations that have been established — the data, baselines and evaluation metrics — to further explore AI-based image reconstruction techniques,” says Yvonne Lui, MD, associate professor of radiology and associate chair of artificial intelligence. 

“Additionally, any progress made at NYU School of Medicine and FAIR will be part of a larger effort that spans multiple research communities. Results will be compiled and compared on a fastMRI leaderboard, as well as in research papers and workshops to come.”

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