Department of Computer Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 151-742, Republic of Korea.
School of Computer Science & Engineering, Soongsil University, 369 Sangdo-ro, Dongjak-Gu, Seoul 156-743, Republic of Korea.
Comput Methods Programs Biomed. 2016 Jan;123:15-26. doi: 10.1016/j.cmpb.2015.09.011. Epub 2015 Sep 28.
Enhancing 2D angiography while maintaining a low radiation dose has become an important research topic. However, it is difficult to enhance images while preserving vessel-structure details because X-ray noise and contrast blood vessels in 2D angiography have similar intensity distributions, which can lead to ambiguous images of vessel structures. In this paper, we propose a novel and fast vessel-enhancement method for 2D angiography. We apply filtering in the principal component analysis domain for vessel regions and background regions separately, using assumptions based on energy compaction. First, we identify an approximate vessel region using a Hessian-based method. Vessel and non-vessel regions are then represented sparsely by calculating their optimal bases separately. This is achieved by identifying periodic motion in the vessel region caused by the flow of the contrast medium through the blood vessels when viewed on the time axis. Finally, we obtain noise-free images by removing noise in the new coordinate domain for the optimal bases. Our method was validated for an X-ray system, using 10 low-dose sets for training and 20 low-dose sets for testing. The results were compared with those for a high-dose dataset with respect to noise-free images. The average enhancement rate was 93.11±0.71%. The average processing time for enhancing video comprising 50-70 frames was 0.80±0.35s, which is much faster than the previously proposed technique. Our method is applicable to 2D angiography procedures such as catheterization, which requires rapid and natural vessel enhancement.
在保持低辐射剂量的同时增强 2D 血管造影已成为一个重要的研究课题。然而,由于 X 射线噪声和对比度血管在 2D 血管造影中的强度分布相似,这使得血管结构的图像变得模糊,因此很难在增强图像的同时保留血管结构的细节。在本文中,我们提出了一种新颖而快速的 2D 血管造影血管增强方法。我们分别在主成分分析域中对血管区域和背景区域应用滤波,使用基于能量压缩的假设。首先,我们使用基于 Hessian 的方法来识别近似的血管区域。然后,通过分别计算它们的最优基来稀疏地表示血管和非血管区域。这是通过在时间轴上观察到对比剂流过血管时血管区域中由于流动引起的周期性运动来实现的。最后,我们通过去除新坐标域中最优基的噪声来获得无噪声图像。我们的方法在 X 射线系统上进行了验证,使用 10 个低剂量集进行训练和 20 个低剂量集进行测试。将结果与高剂量数据集的无噪声图像进行了比较。平均增强率为 93.11±0.71%。增强包含 50-70 帧的视频的平均处理时间为 0.80±0.35s,比之前提出的技术快得多。我们的方法适用于导管插入术等 2D 血管造影程序,需要快速和自然的血管增强。