Robust PCA Unrolling Network for Super-resolution Vessel Extraction in X-ray Coronary Angiography
This work addresses vessel extraction challenges in medical imaging for X-ray coronary angiography, representing an incremental improvement with novel method integration.
The authors tackled the problem of extracting vessels from X-ray coronary angiography images by proposing a robust PCA unrolling network with sparse feature selection, which significantly outperformed state-of-the-art methods in restoring vessel intensity and geometry profiles.
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.