# A Master Class on Reproducibility: A Student Hackathon on Advanced MRI Reconstruction Methods

arXiv: https://arxiv.org/abs/2601.18314 · HTML: https://mlsanity.com/paper/2601.18314/

## 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.

## Builds on (most cited first, 7 of 7)

- [fastMRI: An Open Dataset and Benchmarks for Accelerated MRI](https://mlsanity.com/paper/1811.08839/) (2018)
- [MoDL: Model Based Deep Learning Architecture for Inverse Problems](https://mlsanity.com/paper/1712.02862/) (2017)
- [FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare](https://mlsanity.com/paper/2309.12325/) (2023)
- [HUMUS-Net: Hybrid unrolled multi-scale network architecture for accelerated MRI reconstruction](https://mlsanity.com/paper/2203.08213/) (2022)
- [CG-SENSE revisited: Results from the first ISMRM reproducibility challenge](https://mlsanity.com/paper/2008.04308/) (2020)
- [The MICCAI Hackathon on reproducibility, diversity, and selection of papers at the MICCAI conference](https://mlsanity.com/paper/2103.05437/) (2021)
- [An untrained deep learning method for reconstructing dynamic magnetic resonance images from accelerated model-based data](https://mlsanity.com/paper/2205.01604/) (2022)

## 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.