Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran.
Phys Med Biol. 2024 Oct 23;69(21). doi: 10.1088/1361-6560/ad8293.
X-ray coronary angiograms (XCA) are widely used in diagnosing and treating cardiovascular diseases. Various structures with independent motion patterns in the background of XCA images and limitations in the dose of injected contrast agent have resulted in low-contrast XCA images. Background subtraction methods have been developed to enhance the visibility and contrast of coronary vessels in XCA sequences, consequently reducing the requirement for excessive contrast agent injections.The current study proposes an adaptive weighted total variation regularized online RPCA (WTV-ORPCA) method, which is a low-rank and sparse subspaces decomposition approach to subtract the background of XCA sequences. In the proposed method, the images undergo initial preprocessing using morphological operators to eliminate large-scale background structures and achieve image homogenization. Subsequently, the decomposition algorithm decomposes the preprocessed images into background and foreground subspaces. This step applies an adaptive weighted TV constraint to the foreground subspace to ensure the spatial coherency of the finally extracted coronary vessel images.To evaluate the effectiveness of the proposed background subtraction method, some qualitative and quantitative experiments are conducted on two clinical and synthetic low-contrast XCA datasets containing videos from 21 patients. The obtained results are compared with six state-of-the-art methods employing three different assessment criteria. By applying the proposed method to the clinical dataset, the mean values of the global contrast-to-noise ratio, local contrast-to-noise ratio, structural similarity index, and reconstruction error (RE) are obtained as5.976,3.173,0.987, and0.026, respectively. These criteria over the low-contrast synthetic dataset were4.851,2.942,0.958, and0.034, respectively.The findings demonstrate the superiority of the proposed method in improving the contrast and visibility of coronary vessels, preserving the integrity of the vessel structure, and minimizing REs without imposing excessive computational complexity.
X 射线冠状动脉造影 (XCA) 被广泛用于诊断和治疗心血管疾病。在 XCA 图像的背景中，各种具有独立运动模式的结构和注入对比剂的剂量限制导致了低对比度的 XCA 图像。背景减除方法已被开发用于增强 XCA 序列中冠状动脉的可视性和对比度，从而减少对过量对比剂注射的需求。本研究提出了一种自适应加权总变分正则化在线随机主成分分析 (WTV-ORPCA) 方法，这是一种低秩和稀疏子空间分解方法，用于减除 XCA 序列的背景。在所提出的方法中，图像首先经过形态学算子进行初始预处理，以消除大规模的背景结构并实现图像均匀化。随后，分解算法将预处理后的图像分解为背景和前景子空间。这一步骤对前景子空间应用自适应加权 TV 约束，以确保最终提取的冠状动脉图像的空间一致性。为了评估所提出的背景减除方法的有效性，在包含 21 个患者视频的两个临床和合成低对比度 XCA 数据集上进行了一些定性和定量实验。将获得的结果与六种最先进的方法进行比较，这些方法采用了三种不同的评估标准。通过将所提出的方法应用于临床数据集，获得了全局对比度噪声比、局部对比度噪声比、结构相似性指数和重建误差 (RE) 的平均值分别为 5.976、3.173、0.987 和 0.026。在低对比度合成数据集上的这些标准分别为 4.851、2.942、0.958 和 0.034。研究结果表明，该方法在提高冠状动脉的对比度和可视性、保持血管结构完整性和最小化 RE 方面具有优越性，同时不会增加过多的计算复杂度。
