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一种用于标记和交互式可视化缺血性中风脑血管系统的算法。

An algorithm for the labeling and interactive visualization of the cerebrovascular system of ischemic strokes.

机构信息

Pattern Recognition Lab, FAU Erlangen-Nuremberg, Erlangen, Germany.

Computed Tomography, Siemens Healthcare GmbH, Forchheim, Germany.

出版信息

Biomed Phys Eng Express. 2022 Oct 4;8(6). doi: 10.1088/2057-1976/ac9415.

Abstract

During the diagnosis of ischemic strokes, the Circle of Willis and its surrounding vessels are the arteries of interest. Their visualization in case of an acute stroke is often enabled by Computed Tomography Angiography (CTA). Still, the identification and analysis of the cerebral arteries remain time consuming in such scans due to a large number of peripheral vessels which may disturb the visual impression. We propose VirtualDSA++, an algorithm designed to segment and label the cerebrovascular tree on CTA scans. Especially with stroke patients, labeling is a delicate procedure, as in the worst case whole hemispheres may not be present due to impeded perfusion. Hence, we extended the labeling mechanism for the cerebral arteries to identify occluded vessels. In the work at hand, we place the algorithm in a clinical context by evaluating the labeling and occlusion detection on stroke patients, where we have achieved labeling sensitivities comparable to other works between 92% and 95%. To the best of our knowledge, ours is the first work to address labeling and occlusion detection at once, whereby a sensitivity of 67% and a specificity of 81% were obtained for the latter. VirtualDSA++ also automatically segments and models the intracranial system leading to further processing possibilities. We present the generic concept of iterative systematic search for pathways on all nodes of said model, which enables new interactive features. Exemplary, we derive in detail, firstly, the interactive planning of vascular interventions like the mechanical thrombectomy and secondly, the interactive suppression of vessel structures that are not of interest in diagnosing strokes (like veins). We discuss both features as well as further possibilities emerging from the proposed concept.

摘要

在缺血性中风的诊断中,Willis 环及其周围的血管是感兴趣的动脉。它们在急性中风时的可视化通常通过计算机断层血管造影 (CTA) 来实现。然而,在这种扫描中,由于大量可能会干扰视觉印象的周围血管,识别和分析脑动脉仍然很耗时。我们提出了 VirtualDSA++,这是一种旨在对 CTA 扫描中的脑血管树进行分割和标记的算法。特别是对于中风患者,标记是一个精细的过程,因为在最坏的情况下,由于灌注受阻,整个半球可能都不存在。因此,我们扩展了脑动脉的标记机制,以识别闭塞的血管。在目前的工作中,我们将该算法置于临床环境中,通过对中风患者进行标记和闭塞检测来评估其性能,我们实现了与其他工作相当的标记灵敏度,范围在 92%至 95%之间。据我们所知,我们的工作是第一个同时解决标记和闭塞检测问题的工作,后者的灵敏度为 67%,特异性为 81%。VirtualDSA++还自动分割和建模颅内系统,从而实现进一步的处理可能性。我们提出了在所述模型的所有节点上进行路径迭代系统搜索的通用概念,这为新的交互功能提供了可能。例如,我们详细地推导出了血管介入的交互规划,如机械血栓切除术,以及在诊断中风时不感兴趣的血管结构(如静脉)的交互抑制。我们讨论了这两个功能以及从提出的概念中出现的其他可能性。

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