  Deep-learning-based brain magnetic resonance imaging (MRI) reconstruction
methods have the potential to accelerate the MRI acquisition process.
Nevertheless, the scientific community lacks appropriate benchmarks to assess
MRI reconstruction quality of high-resolution brain images, and evaluate how
these proposed algorithms will behave in the presence of small, but expected
data distribution shifts. The Multi-Coil Magnetic Resonance Image (MC-MRI)
Reconstruction Challenge provides a benchmark that aims at addressing these
issues, using a large dataset of high-resolution, three-dimensional,
T1-weighted MRI scans. The challenge has two primary goals: 1) to compare
different MRI reconstruction models on this dataset and 2) to assess the
generalizability of these models to data acquired with a different number of
receiver coils. In this paper, we describe the challenge experimental design,
and summarize the results of a set of baseline and state of the art brain MRI
reconstruction models. We provide relevant comparative information on the
current MRI reconstruction state-of-the-art and highlight the challenges of
obtaining generalizable models that are required prior to broader clinical
adoption. The MC-MRI benchmark data, evaluation code and current challenge
leaderboard are publicly available. They provide an objective performance
assessment for future developments in the field of brain MRI reconstruction.
