# Simulating single-coil MRI from the responses of multiple coils

arXiv: https://arxiv.org/abs/1811.08026 · HTML: https://mlsanity.com/paper/1811.08026/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2018.

## Built on by (most cited first, 11 of 11)

- [Bayesian Uncertainty Estimation of Learned Variational MRI Reconstruction](https://mlsanity.com/paper/2102.06665/) (2021)
- [CMRxRecon: An open cardiac MRI dataset for the competition of accelerated image reconstruction](https://mlsanity.com/paper/2309.10836/) (2023)
- [Self-supervised Deep Unrolled Reconstruction Using Regularization by Denoising](https://mlsanity.com/paper/2205.03519/) (2022)
- [Jointly Modeling Inter- & Intra-Modality Dependencies for Multi-modal Learning](https://mlsanity.com/paper/2405.17613/) (2024)
- [Adaptive Sampling of k-Space in Magnetic Resonance for Rapid Pathology Prediction](https://mlsanity.com/paper/2406.04318/) (2024)
- [Learned Half-Quadratic Splitting Network for MR Image Reconstruction](https://mlsanity.com/paper/2112.09760/) (2021)
- [PUERT: Probabilistic Under-sampling and Explicable Reconstruction Network for CS-MRI](https://mlsanity.com/paper/2204.11189/) (2022)
- [On the Feasibility of Machine Learning Augmented Magnetic Resonance for Point-of-Care Identification of Disease](https://mlsanity.com/paper/2301.11962/) (2023)
- [Conditional Denoising Diffusion Model-Based Robust MR Image Reconstruction from Highly Undersampled Data](https://mlsanity.com/paper/2510.06335/) (2025)
- [Anatomical Token Uncertainty for Transformer-Guided Active MRI Acquisition](https://mlsanity.com/paper/2603.21806/) (2026)
- [Phase-map synthesis from magnitude-only MR images using conditional score-based diffusion models with application in training of accelerated MRI reconstruction models](https://mlsanity.com/paper/2605.01185/) (2026)

## Abstract (arXiv)

We convert the information-rich measurements of parallel and phased-array MRI into noisier data that a corresponding single-coil scanner could have taken. Specifically, we replace the responses from multiple receivers with a linear combination that emulates the response from only a single, aggregate receiver, replete with the low signal-to-noise ratio and phase problems of any single one of the original receivers (combining several receivers is necessary, however, since the original receivers usually have limited spatial sensitivity). This enables experimentation in the simpler context of a single-coil scanner prior to development of algorithms for the full complexity of multiple receiver coils.