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Deep-learning-based brain magnetic resonance imaging (MRI) reconstructionmethods have the potential to accelerate the MRI acquisition process.Nevertheless, the scientific community lacks appropriate benchmarks to assessMRI reconstruction quality of high-resolution brain images, and evaluate howthese proposed algorithms will behave in the presence of small, but expecteddata distribution shifts. The Multi-Coil Magnetic Resonance Image (MC-MRI)Reconstruction Challenge provides a benchmark that aims at addressing theseissues, using a large dataset of high-resolution, three-dimensional,T1-weighted MRI scans. The challenge has two primary goals: 1) to comparedifferent MRI reconstruction models on this dataset and 2) to assess thegeneralizability of these models to data acquired with a different number ofreceiver 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 MRIreconstruction models. We provide relevant comparative information on thecurrent MRI reconstruction state-of-the-art and highlight the challenges ofobtaining generalizable models that are required prior to broader clinicaladoption. The MC-MRI benchmark data, evaluation code and current challengeleaderboard are publicly available. They provide an objective performanceassessment for future developments in the field of brain MRI reconstruction.

Multi-Coil MRI Reconstruction Challenge -- Assessing Brain MRI Reconstruction Models and their Generalizability to Varying Coil Configurations