Facebook AI and NYU Langone Health created the fastMRI project to accelerate MRI scans through AI. By using AI to create ground-truth-accurate images from significantly less raw data, fastMRI aims to make scans as much as ten times faster than they are today, thereby improving patient experience and making MRI scans less expensive and more accessible. As part of this project, we released the largest publicly available data set of de-identified raw MRI knee measurements, and we’re pleased to now share the results of the first fastMRI image reconstruction challenge.

Thirty-four teams

Through this challenge, researchers from across the AI community were able to explore new approaches and compare their results. Thirty-four teams participated, using u-nets, deformable convolutional networks, recurrent neural networks, and other model architectures. Three teams lead respectively by Patrick Putzky of the University of Amsterdam (single-coil 4x acceleration), Puyang Wang of United Imaging Intelligence/Johns Hopkins University (multi-coil 4x), and Nicola Pezzotti of Philips (multi-coil 8x) were judged to be the strongest performers. The teams have been invited to present at the Medical Imaging Meets NeurIPS workshop on Saturday, December 14, at the NeurIPS conference in Vancouver.

Panel of radiologists

Submissions were first evaluated by structural similarity measure (SSIM), which quantifies changes in the structural information of an image. The top four results by SSIM score were then judged by a panel of radiologists on their visual quality. Though this evaluation is useful for gaining insight into their relative strengths, the determination of whether the approaches are clinically viable will require thorough clinical studies, which have not yet been performed. (Note that the data set used in the challenge included two of the MRI sequences used in the clinical setting.)

Overall, the top models in the challenge all produced SSIM scores within a tenth of a decimal place of one another. This suggests that multiple approaches will be effective and adds to our optimism that AI will be able to improve MRI scans.

Read the whole blog.

Working toward using AI to improve MRIs in the real world

We’re encouraged by the challenge and happy to see the broader community successfully engaging on the project. We are confident that by working openly and collaboratively we will make progress toward our ultimate goal more quickly.

Computer vision research in other subfields often benefits from hosting these types of open, public challenges. But they are not yet common in the medical imaging space. We hope the fastMRI initiative will advance AI research, improve access to potentially life-saving technology, and inspire more open and reproducible research practices in the field.