School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China.
Phys Med Biol. 2018 Aug 1;63(15):155019. doi: 10.1088/1361-6560/aacddf.
Effective vessel enhancement in x-ray coronary angiograms (XCA) is essential for the diagnosis of coronary artery disease, yet challenged by complex background structures of varying intensities as well as motion patterns. As a typical layer-separation method, robust principal component analysis (RPCA) has been proposed to automatically improve vessel visibility via sparse and low-rank decomposition. However, the attenuated motion of vessels in x-ray angiograms leads to the unsatisfactory vessel enhancement performance of the decomposition framework. To address this problem, we propose a vesselness-constrained RPCA method (VC-RPCA), where a vessel-like appearance prior is incorporated into the layer separation framework for accurate vessel enhancement. We first pre-compute the vessel-like appearance prior based on a Frangi filter to highlight the curvilinear structures. After removing large-scale background structures via a morphological closing operation, we then integrate the pre-computed vessel-like appearance prior into a low-rank decomposition framework to separate the fine vessel structures. In addition, we develop an adaptive regularization strategy that imposes structured-sparse constraints to solve the scale issue and capture vessels without salient motion. The proposed method was validated on 13 clinical XCA sequences containing 777 images in total. The contrast-to-noise ratio, Dice coefficient and area under the ROC curve were employed for quantitative evaluation of the vessel enhancement performance. Experiments show that (1) the adaptive regularization strategy helps to obtain a complete coronary tree in the separated vessel layer; (2) our low-rank decomposition framework is robust against false positive/negative responses of the Frangi filter; and (3) the proposed VC-RPCA is computationally fast and outperforms other state-of-the-art RPCA methods for vessel enhancement in the full-contrast and low-contrast scenarios. The results demonstrate that the proposed VC-RPCA can accurately separate coronary arteries and prominently improve vessel visibility in x-ray angiograms.
在 X 射线冠状动脉造影（XCA）中，有效的血管增强对于冠状动脉疾病的诊断至关重要，但由于背景结构的强度和运动模式复杂，这一过程具有挑战性。作为一种典型的分层分离方法，稳健主成分分析（RPCA）已被提出，通过稀疏和低秩分解自动提高血管可见度。然而，X 射线血管造影中血管的衰减运动导致分解框架的血管增强性能不理想。为了解决这个问题，我们提出了一种血管约束 RPCA 方法（VC-RPCA），其中将血管样外观先验纳入分层分离框架，以实现准确的血管增强。我们首先基于 Frangi 滤波器预先计算血管样外观先验，以突出曲线结构。在通过形态学闭合操作去除大尺度背景结构之后，我们将预先计算的血管样外观先验集成到低秩分解框架中，以分离精细的血管结构。此外，我们开发了一种自适应正则化策略，该策略施加结构稀疏约束来解决尺度问题并捕获没有明显运动的血管。该方法在总共包含 777 张图像的 13 个临床 XCA 序列上进行了验证。对比度噪声比、Dice 系数和 ROC 曲线下面积用于定量评估血管增强性能。实验表明：（1）自适应正则化策略有助于在分离的血管层中获得完整的冠状动脉树；（2）我们的低秩分解框架对 Frangi 滤波器的假阳性/假阴性响应具有鲁棒性；（3）所提出的 VC-RPCA 在全对比度和低对比度情况下的血管增强方面，计算速度快，性能优于其他最先进的 RPCA 方法。结果表明，所提出的 VC-RPCA 可以准确地分离冠状动脉并显著提高 X 射线血管造影中的血管可见度。
