AUTHOR=Beauferris Youssef , Teuwen Jonas , Karkalousos Dimitrios , Moriakov Nikita , Caan Matthan , Yiasemis George , Rodrigues Lívia , Lopes Alexandre , Pedrini Helio , Rittner Letícia , Dannecker Maik , Studenyak Viktor , Gröger Fabian , Vyas Devendra , Faghih-Roohi Shahrooz , Kumar Jethi Amrit , Chandra Raju Jaya , Sivaprakasam Mohanasankar , Lasby Mike , Nogovitsyn Nikita , Loos Wallace , Frayne Richard , Souza Roberto TITLE=Multi-Coil MRI Reconstruction Challenge—Assessing Brain MRI Reconstruction Models and Their Generalizability to Varying Coil Configurations JOURNAL=Frontiers in Neuroscience VOLUME=16 YEAR=2022 URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2022.919186 DOI=10.3389/fnins.2022.919186 ISSN=1662-453X ABSTRACT=

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 the 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 MRI (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.