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Robust PCA Unrolling Network for Super-resolution Vessel Extraction in X-ray Coronary Angiography
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<span title="2022-04-24">2022</span>
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arXiv
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<span class="release-stage" >pre-print</span>
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<span title="2022-04-16">2022</span>
<span class="release-stage" >pre-print</span>
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2204.08466v1">arXiv:2204.08466v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/s442xqea2beqzlhtuctgmb2dtm">fatcat:s442xqea2beqzlhtuctgmb2dtm</a> </span>
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2204.08466v1">arXiv:2204.08466v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/s442xqea2beqzlhtuctgmb2dtm">fatcat:s442xqea2beqzlhtuctgmb2dtm</a> </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
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... 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.
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