Mao Hai-Qun, Yang Feng, Lin Mu-Dan, Huang Zheng, Cui Kai, Wang Xin-Xin
College of Biomedical Engineering, Southern Medical University, Southern Medical University, Nanfang Hospital, Guangzhou 510515, China. E-mail:
Nan Fang Yi Ke Da Xue Xue Bao. 2015 Apr;35(4):492-8.
We propose an image-based key frames gating method for intravascular ultrasound (IVUS) sequence based on manifold learning to reduce motion artifacts in IVUS longitudinal cuts.
We achieved the gating with Laplacian eigenmaps, a manifold learning technique, to determine the low-dimensional manifold embedded in the high-dimensional image space. A distance function was constructed by the low-dimensional feature vectors to reflect the heart movement. The IVUS images were classified as end-diastolic and non-end-diastolic based on the distance function, and the IVUS images collected in end-diastolic stage constitutes the key frames gating sequences.
We tested the algorithm on 13 in vivo clinical IVUS sequences (images 915±142 frames, coronary segments length 15.24±2.37 mm) to calculate the vessel volume, lumen volume, and the mean plaque burden of the original and gated sequences. Statistical results showed that both the vessel volume and lumen volume measured from the gated sequences were significantly smaller than the original ones, indicating that the gated sequences were more stable; the mean plaque burden was comparable between the original and gated sequences to meet the need in clinical diagnosis and treatment. In the longitudinal views, the gated sequences had less saw tooth shape than the original ones with a similar trend and a good continuity. We also compared our method with an existing gating method.
The proposed algorithm is simple and robust, and the gating sequences can effectively reduce motion artifacts in IVUS longitudinal cuts.
我们提出一种基于流形学习的血管内超声（IVUS）序列图像关键帧选通方法，以减少IVUS纵向切面中的运动伪影。
我们使用拉普拉斯特征映射（一种流形学习技术）来实现选通，以确定嵌入在高维图像空间中的低维流形。通过低维特征向量构建距离函数以反映心脏运动。基于该距离函数将IVUS图像分类为舒张末期和非舒张末期，舒张末期采集的IVUS图像构成关键帧选通序列。
我们在13个体内临床IVUS序列（图像915±142帧，冠状动脉节段长度15.24±2.37mm）上测试该算法，以计算原始序列和选通序列的血管体积、管腔体积以及平均斑块负荷。统计结果表明，选通序列测量的血管体积和管腔体积均显著小于原始序列，表明选通序列更稳定；原始序列和选通序列的平均斑块负荷相当，满足临床诊断和治疗需求。在纵向视图中，选通序列的锯齿形状比原始序列少，具有相似趋势且连续性良好。我们还将我们的方法与现有的选通方法进行了比较。
所提出的算法简单且稳健，选通序列可有效减少IVUS纵向切面中的运动伪影。
