A Master Class on Reproducibility: A Student Hackathon on Advanced MRI Reconstruction Methods
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Abstract (arXiv)
We report the design, protocol, and outcomes of a student reproducibility hackathon focused on replicating the results of three influential MRI reconstruction papers: (a) MoDL, an unrolled model-based network with learned denoising; (b) HUMUS-Net, a hybrid unrolled multiscale CNN+Transformer architecture; and (c) an untrained, physics-regularized dynamic MRI method that uses a quantitative MR model for early stopping. We describe the setup of the hackathon and present reproduction outcomes alongside additional experiments, and we detail fundamental practices for building reproducible codebases.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 12 references that are in the map. First posted on arXiv 26 Jan 2026.
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References · 7
- 2018fastMRI: An Open Dataset and Benchmarks for Accelerated MRIcited by 470
- 2017MoDL: Model Based Deep Learning Architecture for Inverse Problemscited by 387
- 2023FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcarecited by 60
- 2022HUMUS-Net: Hybrid unrolled multi-scale network architecture for accelerated MRI reconstructioncited by 30
- 2020CG-SENSE revisited: Results from the first ISMRM reproducibility challengecited by 7
- 2021The MICCAI Hackathon on reproducibility, diversity, and selection of papers at the MICCAI conferencecited by 3
- 2022An untrained deep learning method for reconstructing dynamic magnetic resonance images from accelerated model-based datacited by 2
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