Automatic recognition of subject-specific cerebrovascular trees.
Automatic recognition of subject-specific cerebrovascular trees.
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DOI：
10.1002/mrm.26087
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发表时间：
2017-01
影响因子：
3.3
通讯作者：
Linninger A
中科院分区：
文献类型：
--
作者：
Hsu CY;Schneller B;Alaraj A;Flannery M;Zhou XJ;Linninger A
本文介绍了一种用于从磁共振图像重建脑血管树的滤波器。目前的成像技术可靠地捕获主要脑血管，但通常无法检测小血管，由于分辨率有限、血流速度慢以及分叉或非血管结构周围的扭曲，小血管的对比度受到抑制。血管构筑的不完整视图限制了医生可用的信息。提出了一种新的基于Hessian的滤波器，用于MR血管造影和静脉造影的血管重建中的对比度增强，而不引入悬挂段。我们量化滤波器的性能与接收器的操作特性和骰子相似系数分析。计算提取的血管总长度、节段数、体积、表面距离和位置误差以进行验证。脑血管树的重建从MR图像的6名志愿者表明，新的过滤器呈现更完整的代表性的主题特定的脑血管网络。使用体模模型进行的验证表明，滤波器可正确检测所有长度尺度的血管，而不会在分叉或扭曲直径处失败。新的过滤器可以通过提供度量和血管解剖结构来潜在地改善脑血管疾病的诊断。它还通过计算没有操作员主观性的生物特征来促进大型数据集的自动分析。高质量的重建使计算网格生成对象特定的血流动力学模拟。
An image filter designed for reconstructing cerebrovascular trees from MR images is described. Current imaging techniques capture major cerebral vessels reliably, but often fail to detect small vessels, whose contrast is suppressed due to limited resolution, slow blood flow rate, and distortions around bifurcations or non-vascular structures. An incomplete view of angioarchitecture limits the information available to physicians. A novel Hessian-based filter for contrast-enhancement in MR angiography and venography for blood vessel reconstruction without introducing dangling segments is presented. We quantify filter performance with receiver-operating-characteristic and dice-similarity-coefficient analysis. Total extracted vascular length, number-of-segments, volume, surface-to-distance, and positional error are calculated for validation. Reconstruction of cerebrovascular trees from MR images of six volunteers show that the new filter renders more complete representations of subject-specific cerebrovascular networks. Validation with phantom models shows the filter correctly detects blood vessels across all length scales without failing at bifurcations or distorting diameters. The novel filter can potentially improve the diagnosis of cerebrovascular diseases by delivering metrics and anatomy of the vasculature. It also facilitates the automated analysis of large datasets by computing biometrics free of operator subjectivity. The high quality reconstruction enables computational mesh generation for subject-specific hemodynamic simulations.