# CG-SENSE revisited: Results from the first ISMRM reproducibility challenge

arXiv: https://arxiv.org/abs/2008.04308 · HTML: https://mlsanity.com/paper/2008.04308/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2020.

## Built on by (most cited first, 7 of 7)

- [Dual-Domain Self-Supervised Learning for Accelerated Non-Cartesian MRI Reconstruction](https://mlsanity.com/paper/2302.09244/) (2023)
- [On Instabilities of Conventional Multi-Coil MRI Reconstruction to Small Adverserial Perturbations](https://mlsanity.com/paper/2102.13066/) (2021)
- [End-to-End AI-based MRI Reconstruction and Lesion Detection Pipeline for Evaluation of Deep Learning Image Reconstruction](https://mlsanity.com/paper/2109.11524/) (2021)
- [MRpro: open framework for model-based, learned, and quantitative MR imaging](https://mlsanity.com/paper/2507.23129/) (2025)
- [A New k-Space Model for Non-Cartesian Fourier Imaging](https://mlsanity.com/paper/2505.05647/) (2025)
- [A Master Class on Reproducibility: A Student Hackathon on Advanced MRI Reconstruction Methods](https://mlsanity.com/paper/2601.18314/) (2026)
- [CoilDrop-MRI: Self-supervised physics-guided MRI reconstruction with coil dropout](https://mlsanity.com/paper/2606.00100/) (2026)

## Abstract (arXiv)

Purpose: The aim of this work is to shed light on the issue of reproducibility in MR image reconstruction in the context of a challenge. Participants had to recreate the results of "Advances in sensitivity encoding with arbitrary k-space trajectories" by Pruessmann et al. Methods: The task of the challenge was to reconstruct radially acquired multi-coil k-space data (brain/heart) following the method in the original paper, reproducing its key figures. Results were compared to consolidated reference implementations created after the challenge, accounting for the two most common programming languages used in the submissions (Matlab/Python). Results: Visually, differences between submissions were small. Pixel-wise differences originated from image orientation, assumed field-of-view or resolution. The reference implementations were in good agreement, both visually and in terms of image similarity metrics. Discussion and Conclusion: While the description level of the published algorithm enabled participants to reproduce CG-SENSE in general, details of the implementation varied, e.g., density compensation or Tikhonov regularization. Implicit assumptions about the data lead to further differences, emphasizing the importance of sufficient meta-data accompanying open data sets. Defining reproducibility quantitatively turned out to be non-trivial for this image reconstruction challenge, in the absence of ground-truth results. Typical similarity measures like NMSE of SSIM were misled by image intensity scaling and outlier pixels. Thus, to facilitate reproducibility, researchers are encouraged to publish code and data alongside the original paper. Future methodological papers on MR image reconstruction might benefit from the consolidated reference implementations of CG-SENSE presented here, as a benchmark for methods comparison.