4.7 Article

Robust PCA Unrolling Network for Super-Resolution Vessel Extraction in X-Ray Coronary Angiography

期刊

IEEE TRANSACTIONS ON MEDICAL IMAGING
卷 41, 期 11, 页码 3087-3098

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMI.2022.3177626

关键词

Feature extraction; Spatiotemporal phenomena; Noise measurement; Computational modeling; Image segmentation; Data mining; Angiography; Algorithm unrolling; RPCA unrolling network; X-ray coronary angiography; vessel extraction; sparse feature selection; super-resolution

资金

  1. Science and Technology Commission of Shanghai Municipality [19dz1200500, 19411951507]
  2. National Natural Science Foundation of China [61271320, 82070477]
  3. Shanghai Shenkang Hospital Development Center [SHDC12019X12]
  4. Interdisciplinary Program of Shanghai Jiao Tong University [ZH2018ZDA19, YG2021QN122, YG2021QN99]

向作者/读者索取更多资源

In this study, a novel robust PCA unrolling network with sparse feature selection is proposed for super-resolution XCA vessel imaging. The network is embedded within a patch-wise spatiotemporal super-resolution framework and can gradually prune complex vessel-like artefacts and noisy backgrounds in XCA. It can 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 in imaging the vessel network and its distal vessels.
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. The source code is available at https://github.com/Binjie-Qin/RPCA-UNet

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