  Purpose: To advance research in the field of machine learning for MR image
reconstruction with an open challenge. Methods: We provided participants with a
dataset of raw k-space data from 1,594 consecutive clinical exams of the knee.
The goal of the challenge was to reconstruct images from these data. In order
to strike a balance between realistic data and a shallow learning curve for
those not already familiar with MR image reconstruction, we ran multiple tracks
for multi-coil and single-coil data. We performed a two-stage evaluation based
on quantitative image metrics followed by evaluation by a panel of
radiologists. The challenge ran from June to December of 2019. Results: We
received a total of 33 challenge submissions. All participants chose to submit
results from supervised machine learning approaches. Conclusion: The challenge
led to new developments in machine learning for image reconstruction, provided
insight into the current state of the art in the field, and highlighted
remaining hurdles for clinical adoption.
