# Offset Sampling Improves Deep Learning based Accelerated MRI Reconstructions by Exploiting Symmetry

arXiv: https://arxiv.org/abs/1912.01101 · HTML: https://mlsanity.com/paper/1912.01101/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2019.

## Built on by (most cited first, 4 of 4)

- [Learning-Rate-Free Learning by D-Adaptation](https://mlsanity.com/paper/2301.07733/) (2023)
- [Adaptivity without Compromise: A Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic Optimization](https://mlsanity.com/paper/2101.11075/) (2021)
- [On Retrospective k-space Subsampling schemes For Deep MRI Reconstruction](https://mlsanity.com/paper/2301.08365/) (2023)
- [Information-Theoretic Optimization for Task-Adapted Compressed Sensing Magnetic Resonance Imaging](https://mlsanity.com/paper/2604.12709/) (2026)

## Abstract (arXiv)

Deep learning approaches to accelerated MRI take a matrix of sampled Fourier-space lines as input and produce a spatial image as output. In this work we show that by careful choice of the offset used in the sampling procedure, the symmetries in k-space can be better exploited, producing higher quality reconstructions than given by standard equally-spaced samples or randomized samples motivated by compressed sensing.