基于超声容积数据的血管网络重建与误差检测

K. Masuda, A. Bossard, Yuki Sugano, Toshikazu Kato, S. Onogi
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引用次数: 1

摘要

最近,我们描述了一种通过三维细化血管网络来检测血管分叉的重建方法,并将其应用于体内微泡的控制。然而,这种方法不包括错误检测,并且只在形状非常简单的人造血管上进行了验证。本文提出了一个包含血管网络抽象方法的系统。然后利用图论和重构网络中的误差模式对该模型进行分析。在体外实验中,我们获取了直径为0.5 ~ 2.0mm、不同流速的多分叉人工毛细血管的体积数据。我们能够重建具有多分支的体外人工毛细血管的血管网络。结果表明,该系统在不受超声分辨率限制的情况下,成功地重建了相应的网络。
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Reconstruction and error detection of blood vessel network from ultrasound volume data
Recently, we described a reconstruction method of the blood vessel network by 3D thinning to detect vessels bifurcations, which is applied to the control of microbubbles in vivo. However, that method did not include error detections and was only verified on a very simply shaped artificial blood vessel. In this paper we propose a system including an abstraction method for the blood vessel network. Such a model is then analyzed through graph theory and error patterns in the reconstructed network. We proceeded in vitro by acquiring volume data from an artificial capillary with multi-bifurcations whose diameter ranges from 0.5 to 2.0mm and with different flow velocities. We were able to reconstruct the blood vessel network of an in vitro artificial capillary with multi-bifurcations. Results show that our system successfully reconstructed the corresponding networks as much as the limitation of resolution of echography.
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