# Benchmarking learned non-Cartesian k-space trajectories and reconstruction networks

arXiv: https://arxiv.org/abs/2201.11356 · HTML: https://mlsanity.com/paper/2201.11356/

## 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 27 Jan 2022.

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

- [fastMRI: An Open Dataset and Benchmarks for Accelerated MRI](https://mlsanity.com/paper/1811.08839/) (2018)
- [Learning-based Optimization of the Under-sampling Pattern in MRI](https://mlsanity.com/paper/1901.01960/) (2019)
- [B-spline Parameterized Joint Optimization of Reconstruction and K-space Trajectories (BJORK) for Accelerated 2D MRI](https://mlsanity.com/paper/2101.11369/) (2021)
- [PILOT: Physics-Informed Learned Optimized Trajectories for Accelerated MRI](https://mlsanity.com/paper/1909.05773/) (2019)
- [Hybrid learning of Non-Cartesian k-space trajectory and MR image reconstruction networks](https://mlsanity.com/paper/2110.12691/) (2021)
- [3D FLAT: Feasible Learned Acquisition Trajectories for Accelerated MRI](https://mlsanity.com/paper/2008.04808/) (2020)
- [Learning the sampling density in 2D SPARKLING MRI acquisition for optimized image reconstruction](https://mlsanity.com/paper/2103.03559/) (2021)

## Abstract (arXiv)

We benchmark the current existing methods to jointly learn non-Cartesian k-space trajectory and reconstruction: PILOT, BJORK, and compare them with those obtained from the recently developed generalized hybrid learning (HybLearn) framework. We present the advantages of using projected gradient descent to enforce MR scanner hardware constraints as compared to using added penalties in the cost function. Further, we use the novel HybLearn scheme to jointly learn and compare our results through a retrospective study on fastMRI validation dataset.