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一种用于将 7T TOF-MRI 与 7T PC-MRI 颅内血管数据进行配准的混合分层策略。

A hybrid hierarchical strategy for registration of 7T TOF-MRI to 7T PC-MRI intracranial vessel data.

机构信息

Department of Simulation and Graphics, Otto-von-Guericke University (OvGU), Magdeburg, Germany.

Forschungscampus STIMULATE, Magdeburg, Germany.

出版信息

Int J Comput Assist Radiol Surg. 2023 May;18(5):837-844. doi: 10.1007/s11548-023-02836-y. Epub 2023 Jan 20.

Abstract

PURPOSE

7T time-of-flight (TOF) MRI provides high resolution for the evaluation of cerebrovascular vessels and pathologies. In combination with 4D flow fields acquired with phase-contrast (PC) MRI, hemodynamic information can be extracted to enhance the analysis by providing direct measurements in the larger arteries or patient-specific boundary conditions. Hence, a registration between both modalities is required.

METHODS

To combine TOF and PC-MRI data, we developed a hybrid registration approach. Vessels and their centerlines are segmented from the TOF data. The centerline is fit to the intensity ridges of the lower resolved PC-MRI data, which provides temporal information. We used a metric that utilizes a scaled sum of weighted intensities and gradients on the normal plane. The registration is then guided by decoupled local affine transformations. It is applied hierarchically following the branching order of the vessel tree.

RESULTS

A landmark validation over Monte Carlo simulations yielded an average mean squared error of 184.73 mm and an average Hausdorff distance of 15.20 mm. The hierarchical traversal that transforms child vessels with their parents registers even small vessels not detectable in the PC-MRI.

CONCLUSION

The presented work combines high-resolution tomographic information from 7T TOF-MRI and measured flow data from 4D 7T PC-MRI scan for the arteries of the brain. This enables usage of patient-specific flow parameters for realistic simulations, thus supporting research in areas such as cerebral small vessel disease. Automatization and free deformations can help address the limiting error measures in the future.

摘要

目的

7T 时间飞跃(TOF)MRI 可提供高分辨率的脑血管和病变评估。与相位对比(PC)MRI 采集的 4D 流场相结合,可提取血流动力学信息,通过提供较大动脉的直接测量值或患者特定的边界条件来增强分析。因此,需要对两种模式进行配准。

方法

为了结合 TOF 和 PC-MRI 数据,我们开发了一种混合配准方法。从 TOF 数据中分割出血管及其中心线。将中心线拟合到低分辨率 PC-MRI 数据的强度脊线上,以提供时间信息。我们使用了一种度量标准,该标准利用了法向平面上加权强度和梯度的标量和。然后,通过解耦的局部仿射变换引导配准。它按照血管树的分支顺序分层应用。

结果

通过对蒙特卡罗模拟进行地标验证,平均均方误差为 184.73mm,平均 Hausdorff 距离为 15.20mm。分层遍历可对带有父母的子血管进行变换,从而注册在 PC-MRI 中不可检测的小血管。

结论

本研究将来自 7T TOF-MRI 的高分辨率层析信息与来自 4D 7T PC-MRI 扫描的测量血流数据相结合,用于大脑动脉。这使得可以使用患者特定的流动参数进行真实模拟,从而支持诸如脑小血管疾病等领域的研究。自动化和自由变形将来可以帮助解决限制误差的措施。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4fe/10113302/97b132eba9db/11548_2023_2836_Fig1_HTML.jpg

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