Mao Haiqun, Yang Feng, Huang Zheng, Cui Kai, Wang Xinxin
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2015 Aug;32(4):892-9.
In this paper, we propose an image-based key frame gating method to reduce motion artifacts in intravascular ultrasound (IVUS) longitudinal cuts. The artifacts are mainly caused by the periodic relative displacement between blood vessels and the IVUS catheter due to cardiac motion. The method is achieved in four steps as following. Firstly, we convert IVUS image sequences to polar coordinates to cut down the amount of calculation. Secondly, we extracted a one-dimensional signal cluster reflecting cardiac motion by spectral analysis and filtering techniques. Thirdly, we designed a Butterworth band-pass filter for filtering the one-dimensional signal clusters. Fourthly, we retrieved the extremes of the filtered signal clusters to seek key frames to compose key-frames gated sequences. Experimental results showed that our algorithm was fast and the average frame processing time was 17ms. Observing the longitudinal viewpictures, we found that comparing to the original ones, the gated sequences had similar trend, less saw tooth shape, and good continuity. We selected 12 groups of clinical IVUS sequences [images (876 +/- 65 frames), coronary segments length (14.61 +/- 1.08 mm)] to calculate vessel volume, lumen volume, mean plaque burden of the original and gated sequences. Statistical results showed that, on one hand, both vessel volume and lumen volume measured of the gated sequences were significantly smaller than those of the original ones, and there was no significant difference on mean plaque burden between original and gated sequences, which met the need of the clinical diagnosis and treatment. On the other hand, variances of vessel area and lumen area of the gated sequences were significantly smaller than those of the original sequences, indicating that the gated sequences would be more stable than the original ones.
在本文中，我们提出了一种基于图像的关键帧选通方法，以减少血管内超声（IVUS）纵向切面中的运动伪影。这些伪影主要是由心脏运动导致血管与IVUS导管之间的周期性相对位移引起的。该方法通过以下四个步骤实现。首先，我们将IVUS图像序列转换为极坐标以减少计算量。其次，我们通过频谱分析和滤波技术提取反映心脏运动的一维信号簇。第三，我们设计了一个巴特沃斯带通滤波器对一维信号簇进行滤波。第四，我们检索滤波后信号簇的极值以寻找关键帧，从而组成关键帧选通序列。实验结果表明，我们的算法速度很快，平均帧处理时间为17毫秒。观察纵向视图图片时，我们发现与原始序列相比，选通序列具有相似的趋势，锯齿形状更少，并且连续性良好。我们选择了12组临床IVUS序列[图像（876±65帧），冠状动脉节段长度（14.61±1.08毫米）]来计算原始序列和选通序列的血管体积、管腔体积、平均斑块负荷。统计结果表明，一方面，选通序列测量的血管体积和管腔体积均明显小于原始序列，并且原始序列和选通序列之间的平均斑块负荷没有显著差异，这满足了临床诊断和治疗的需求。另一方面，选通序列的血管面积和管腔面积的方差明显小于原始序列，这表明选通序列比原始序列更稳定。
