Semi-automatic Co-Registration of 3D CFD Vascular Geometry to 1000 FPS High-Speed Angiographic (HSA) Projection Images for Flow Determination Comparisons.
Semi-automatic Co-Registration of 3D CFD Vascular Geometry to 1000 FPS High-Speed Angiographic (HSA) Projection Images for Flow Determination Comparisons.
复制标题
将 3D CFD 血管几何形状半自动联合配准到 1000 FPS 高速血管造影 (HSA) 投影图像，以进行流量测定比较。
DOI：
10.1117/12.2612361
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Ionita,CiprianN
中科院分区：
文献类型：
--
作者：
Chudzik,Mitchell;Williams,Kyle;Shields,Allison;Nagesh,SvSetlur;Paccione,Eric;Bednarek,DanielR;Rudin,Stephen;Ionita,CiprianN
图像联合配准是一种重要工具，通常用于定量或定性比较随时间、来源等变化的图像或数据集的信息。本研究提出了一种将颅内动脉瘤的 3D 血管几何结构半自动联合配准到新型高速血管造影 (HSA) 1000 fps 投影图像的方法。使用 Tecplot 360 软件，可以提取针对特定患者脉管系统模型的计算流体动力学 (CFD) 生成的 3D 测速数据并将其上传到 Python 中。然后可以执行 3D 测速数据的膨胀、平移和角度旋转，以便将其几何形状与 3D 打印血管模型的相应 2D HSA 投影图像共同配准。一旦 3D CFD 测速数据进行几何对齐，就可以生成 2D 测速图，并可以计算 Sørensen-Dice 系数，以确定联合配准过程是否成功。对两种不同的血管模型进行了十次联合配准过程，平均 Sørensen-Dice 系数为 0.84 ± 0.02。本研究中提出的方法可以直接比较 3D CFD 测速数据和体外 2D 测速方法。通过 3D CFD，除了测速数据之外，我们还可以将各种流量特性与 HSA 导出的流量指标进行比较。该方法对于其他血管几何形状也具有鲁棒性。
Image co-registration is an important tool that is commonly used to quantitatively or qualitatively compare information from images or data sets that vary in time, origin, etc. This research proposes a method for the semi-automatic coregistration of the 3D vascular geometry of an intracranial aneurysm to novel high-speed angiographic (HSA) 1000 fps projection images. Using the software Tecplot 360, 3D velocimetry data generated from computational fluid dynamics (CFD) for patient-specific vasculature models can be extracted and uploaded into Python. Dilation, translation, and angular rotation of the 3D velocimetry data can then be performed in order to co-register its geometry to corresponding 2D HSA projection images of the 3D printed vascular model. Once the 3D CFD velocimetry data is geometrically aligned, a 2D velocimetry plot can be generated and the Sørensen–Dice coefficient can be calculated in order to determine the success of the co-registration process. The co-registration process was performed ten times for two different vascular models and had an average Sørensen–Dice coefficient of 0.84 ± 0.02. The method presented in this research allows for a direct comparison between 3D CFD velocimetry data and in-vitro 2D velocimetry methods. From the 3D CFD, we can compare various flow characteristics in addition to velocimetry data with HSAderived flow metrics. The method is robust to other vascular geometries as well.