Facebook and NYU School of Medicine’s Department of Radiology have announced a new collaborative research project aimed to increase the speed of MRI scans using artificial intelligence. The project, called fastMRI, could produce MRI images up to 10 times faster than the current 15 minutes to an hour. These long scan times can make MRI machines challenging for young children, as well as for people who are claustrophobic or for whom lying down is painful. Additionally, there are MRI shortages in many rural areas and in other countries with limited access, resulting in long scheduling backlogs. Boosting the speed of MRI scanners, would make MRI technology available to more people, expanding access to this key diagnostic tool. “With the goal of radically changing the way medical images are acquired in the first place, our aim is not simply enhanced data mining with AI, but rather the generation of fundamentally new capabilities for medical visualization to benefit human health,” Facebook stated in a blog post.
Project data details
The imaging data set that will be used in the project was collected exclusively by NYU School of Medicine. It consists of 10,000 clinical cases and comprises approximately 3 million magnetic resonance images of the knee, brain, and liver. All data, including both images and raw scanner data, are fully stripped of patient names and all other protected health information. Comparisons of the performance between AI-based reconstructions and traditional reconstructions will, likewise, be devoid of any identifying information. No Facebook data of any kind will be used in the project.
“Unlike other AI-related projects, which use medical images as a starting point and then attempt to derive anatomical or diagnostic information from them (in emulation of human observers), this collaboration focuses on applying the strengths of machine learning to reconstruct the most high-value images in entirely new ways,” Facebook state in their post. Using AI, it may be possible to capture less data and therefore scan faster, while preserving or even enhancing the rich information content of magnetic resonance images. The key is to train artificial neural networks to recognize the underlying structure of the images in order to fill in views omitted from the accelerated scan.
Though this project will initially focus on MRI technology, its long-term impact could extend to many other medical imaging applications. For example, the improvements afforded by AI have the potential to revolutionize CT scans as well. Advanced image reconstruction might enable ultra-low-dose CT scans suitable for vulnerable populations, such as pediatric patients. Such improvements would not only help transform the experience and effectiveness of medical imaging, but they’d also help equalize access to an indispensable element of medical care.
“We believe the fastMRI project will demonstrate how domain-specific experts from different fields and industries can work together to produce the kind of open research that will make a far-reaching and lasting positive impact in the world,” Facebook concluded its post.
Update 26th November:
NYU School of Medicine’s Department of Radiology is releasing the first large-scale MRI dataset of its kind as part of fastMRI. “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.
While other sets of radiological images have been released previously, this dataset represents the largest public release of raw MRI data to date. The first phase of the project will involve data from knee MRI scans, but 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 CAI2R. “Our aim is not merely enhanced data mining with AI, but rather creating new capabilities for medical visualization, to benefit human health.”
Source: Facebook and NYU Langone Health
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