Robust PCA Unrolling Network for Super-resolution Vessel Extraction in X-ray Coronary Angiography [article]

Binjie Qin, Haohao Mao, Yiming Liu, Jun Zhao, Yisong Lv, Yueqi Zhu, Song Ding, Xu Chen
<span title="2022-04-24">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
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
more &raquo; ... 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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="">arXiv:2204.08466v2</a> <a target="_blank" rel="external noopener" href="">fatcat:msgakedt4na6xmgzokaskno25a</a> </span>
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