Real-time tracking of contrast bolus propagation in X-ray peripheral angiography

Zhenyu Wu, J. Qian
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引用次数: 16

Abstract

This paper describes a robust and fast algorithm for real-time tracking of contrast bolus propagation in vessels during X-ray peripheral angiography studies, in which the authors are interested in imaging the arterial structures in the legs. A bolus of contrast medium is injected to the patient and a time-sequence of X-ray images records the bolus propagation. Since the field of view of the imaging device is small compared to the object length, the device has to step through an number of preset stations to follow the bolus. Currently it requires a physician to manually issue stepping commands according to his/her visual assessment of bolus propagation observed in a monitor. A smart data acquisition technology is being developed to replace this error-prone manual process in which the bolus tracking algorithm plays a key role. Novel feature normalization circumvents the need for estimating vessel sizes and enables one to estimate contrast density and trade bolus using only features that can be extracted efficiently from acquired image sequences. The use of a parametric model, developed to characterize extracted features, makes the algorithm resistant to noise and feature extraction errors. Extensive experiments on real and simulated angiographic sequences have demonstrated the robustness, accuracy and efficiency of the tracking algorithm.
x线周围血管造影造影剂传播的实时跟踪
本文描述了一种鲁棒且快速的算法,用于在x射线周围血管造影研究中实时跟踪造影剂在血管中的传播,其中作者对腿部动脉结构的成像感兴趣。向患者注射一剂造影剂,x射线图像的时间序列记录该剂的传播。由于成像设备的视场与物体长度相比很小,因此该设备必须经过许多预设站点才能跟随丸子。目前,它需要医生根据他/她在监视器上观察到的丸剂传播的视觉评估,手动发出步进命令。一种智能数据采集技术正在开发,以取代这种容易出错的人工过程,其中丸跟踪算法起着关键作用。新的特征归一化避免了估计血管大小的需要,使人们能够仅使用可以从获取的图像序列中有效提取的特征来估计对比度密度和交易量。使用参数化模型来描述提取的特征,使算法能够抵抗噪声和特征提取误差。在真实和模拟血管造影序列上的大量实验证明了该跟踪算法的鲁棒性、准确性和高效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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