# pISTA-SENSE-ResNet for Parallel MRI Reconstruction

arXiv: https://arxiv.org/abs/1910.00650 · HTML: https://mlsanity.com/paper/1910.00650/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2019.

## Built on by (most cited first, 10 of 10)

- [AI-based Reconstruction for Fast MRI -- A Systematic Review and Meta-analysis](https://mlsanity.com/paper/2112.12744/) (2021)
- [One-dimensional Deep Low-rank and Sparse Network for Accelerated MRI](https://mlsanity.com/paper/2112.04721/) (2021)
- [An Optimal Control Framework for Joint-channel Parallel MRI Reconstruction without Coil Sensitivities](https://mlsanity.com/paper/2109.09738/) (2021)
- [Multi-task Magnetic Resonance Imaging Reconstruction using Meta-learning](https://mlsanity.com/paper/2403.19966/) (2024)
- [A Faithful Deep Sensitivity Estimation for Accelerated Magnetic Resonance Imaging](https://mlsanity.com/paper/2210.12723/) (2022)
- [AliasNet: Alias Artefact Suppression Network for Accelerated Phase-Encode MRI](https://mlsanity.com/paper/2302.08861/) (2023)
- [Edge-weighted pFISTA-Net for MRI Reconstruction](https://mlsanity.com/paper/2302.07468/) (2023)
- [Convex Dual Theory Analysis of Two-Layer Convolutional Neural Networks with Soft-Thresholding](https://mlsanity.com/paper/2304.06959/) (2023)
- [Advancing MRI Reconstruction: A Systematic Review of Deep Learning and Compressed Sensing Integration](https://mlsanity.com/paper/2501.14158/) (2025)
- [Unrolled Reconstruction with Integrated Super-Resolution for Accelerated 3D LGE MRI](https://mlsanity.com/paper/2603.18309/) (2026)

## Abstract (arXiv)

Magnetic resonance imaging has been widely applied in clinical diagnosis, however, is limited by its long data acquisition time. Although imaging can be accelerated by sparse sampling and parallel imaging, achieving promising reconstruction images with a fast reconstruction speed remains a challenge. Recently, deep learning approaches have attracted a lot of attention for its encouraging reconstruction results but without a proper interpretability. In this letter, to enable high-quality image reconstruction for the parallel magnetic resonance imaging, we design the network structure from the perspective of sparse iterative reconstruction and enhance it with the residual structure. The experimental results of a public knee dataset show that compared with the optimization-based method and the latest deep learning parallel imaging methods, the proposed network has less error in reconstruction and is more stable under different acceleration factors.