# Robust PCA Unrolling Network for Super-resolution Vessel Extraction in X-ray Coronary Angiography

arXiv: https://arxiv.org/abs/2204.08466 · HTML: https://mlsanity.com/paper/2204.08466/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 75 references that are in the map.
- First posted on arXiv 16 Apr 2022.

## Builds on (most cited first, 16 of 16)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks](https://mlsanity.com/paper/2102.00554/) (2021)
- [Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing](https://mlsanity.com/paper/1912.10557/) (2019)
- [Deep Learning for Image Super-resolution: A Survey](https://mlsanity.com/paper/1902.06068/) (2019)
- [FISTA-Net: Learning A Fast Iterative Shrinkage Thresholding Network for Inverse Problems in Imaging](https://mlsanity.com/paper/2008.02683/) (2020)
- [CS2-Net: Deep Learning Segmentation of Curvilinear Structures in Medical Imaging](https://mlsanity.com/paper/2010.07486/) (2020)
- [Deep Unfolded Robust PCA with Application to Clutter Suppression in Ultrasound](https://mlsanity.com/paper/1811.08252/) (2018)
- [Deep Vessel Segmentation By Learning Graphical Connectivity](https://mlsanity.com/paper/1806.02279/) (2018)
- [Graph Unrolling Networks: Interpretable Neural Networks for Graph Signal Denoising](https://mlsanity.com/paper/2006.01301/) (2020)
- [Sequential vessel segmentation via deep channel attention network](https://mlsanity.com/paper/2102.05229/) (2021)
- [Learning-Based Algorithms for Vessel Tracking: A Review](https://mlsanity.com/paper/2012.08929/) (2020)
- [Interpretable Detail-Fidelity Attention Network for Single Image Super-Resolution](https://mlsanity.com/paper/2009.13134/) (2020)
- [Vessel Segmentation and Catheter Detection in X-Ray Angiograms Using Superpixels](https://mlsanity.com/paper/1709.02741/) (2017)
- [Edge, Ridge, and Blob Detection with Symmetric Molecules](https://mlsanity.com/paper/1901.09723/) (2019)
- [Texture variation adaptive image denoising with nonlocal PCA](https://mlsanity.com/paper/1810.11282/) (2018)

## Built on by (most cited first, 6 of 6)

- [Robust Implementation of Foreground Extraction and Vessel Segmentation for X-ray Coronary Angiography Image Sequence](https://mlsanity.com/paper/2209.07237/) (2022)
- [Angio-Diff: Learning a Self-Supervised Adversarial Diffusion Model for Angiographic Geometry Generation](https://mlsanity.com/paper/2506.19455/) (2025)
- [Deep Unrolling for Nonconvex Robust Principal Component Analysis](https://mlsanity.com/paper/2307.05893/) (2023)
- [Working memory inspired hierarchical video decomposition with transformative representations](https://mlsanity.com/paper/2204.10105/) (2022)
- [Heavy-Tailed Principal Component Analysis](https://mlsanity.com/paper/2603.11308/) (2026)
- [Temporal Diffusion Bridge Model for Vessel Extraction Using Physics-informed Synthetic Data](https://mlsanity.com/paper/2610.14105/) (2026)

## Abstract (arXiv)

Although robust PCA has been increasingly adopted to extract vessels from X-ray coronary angiography (XCA) images, challenging problems such as inefficient vessel-sparsity modelling, noisy and dynamic background artefacts, and high computational cost still remain unsolved. Therefore, we propose a novel robust PCA unrolling network with sparse feature selection for super-resolution XCA vessel imaging. Being embedded within a patch-wise spatiotemporal super-resolution framework that is built upon a pooling layer and a convolutional long short-term memory network, the proposed network can not only gradually prune complex vessel-like artefacts and noisy backgrounds in XCA during network training but also iteratively learn and select the high-level spatiotemporal semantic information of moving contrast agents flowing in the XCA-imaged vessels. The experimental results show that the proposed method significantly outperforms state-of-the-art methods, especially in the imaging of the vessel network and its distal vessels, by restoring the intensity and geometry profiles of heterogeneous vessels against complex and dynamic backgrounds.