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

Youssef Beauferris, Jonas Teuwen, Dimitrios Karkalousos, Nikita Moriakov, Matthan Caan, George Yiasemis, L. via Rodrigues, Alexandre Lopes, Helio Pedrini, Letícia Rittner, Maik Dannecker, Viktor Studenyak, Fabian Gröger, Devendra Vyas, Shahrooz Faghih-Roohi, Amrit Kumar Jethi, Jaya Chandra Raju, Mohanasankar Sivaprakasam, Mike Lasby, Nikita NogovitsynWallace Loos, Richard Frayne, Roberto Souza

Research output: Contribution to journalArticleAcademicpeer-review

8 Citations (Scopus)

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.
Original languageEnglish
Article number919186
JournalFrontiers in neuroscience
Volume16
DOIs
Publication statusPublished - 6 Jul 2022

Keywords

  • benchmark
  • brain imaging
  • image reconstruction
  • inverse problems
  • machine learning
  • magnetic resonance imaging (MRI)

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