# Edge-weighted pFISTA-Net for MRI Reconstruction

arXiv: https://arxiv.org/abs/2302.07468 · HTML: https://mlsanity.com/paper/2302.07468/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 38 references that are in the map.
- First posted on arXiv 15 Feb 2023.

## Builds on (most cited first, 6 of 6)

- [A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction](https://mlsanity.com/paper/1704.02422/) (2017)
- [Image reconstruction by domain transform manifold learning](https://mlsanity.com/paper/1704.08841/) (2017)
- [Deep Generalization of Structured Low-Rank Algorithms (Deep-SLR)](https://mlsanity.com/paper/1912.03433/) (2019)
- [Joint CS-MRI Reconstruction and Segmentation with a Unified Deep Network](https://mlsanity.com/paper/1805.02165/) (2018)
- [Multi-Contrast MRI Reconstruction with Structure-Guided Total Variation](https://mlsanity.com/paper/1511.06631/) (2015)
- [pISTA-SENSE-ResNet for Parallel MRI Reconstruction](https://mlsanity.com/paper/1910.00650/) (2019)

## Abstract (arXiv)

Deep learning based on unrolled algorithm has served as an effective method for accelerated magnetic resonance imaging (MRI). However, many methods ignore the direct use of edge information to assist MRI reconstruction. In this work, we present the edge-weighted pFISTA-Net that directly applies the detected edge map to the soft-thresholding part of pFISTA-Net. The soft-thresholding value of different regions will be adjusted according to the edge map. Experimental results of a public brain dataset show that the proposed yields reconstructions with lower error and better artifact suppression compared with the state-of-the-art deep learning-based methods. The edge-weighted pFISTA-Net also shows robustness for different undersampling masks and edge detection operators. In addition, we extend the edge weighted structure to joint reconstruction and segmentation network and obtain improved reconstruction performance and more accurate segmentation results.