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Automatic coronary artery plaque thickness comparison between baseline and follow‐up CCTA images

机译:基线和后续CCTA图像之间的自动冠状动脉斑块厚度比较

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Purpose Currently, coronary plaque changes are manually compared between a baseline and follow‐up coronary computed tomography angiography (CCTA) images for long‐term coronary plaque development investigation. We propose an automatic method to measure the plaque thickness change over time. Methods We model the lumen and vessel wall for both the baseline coronary artery tree ( CAT‐BL ) and follow‐up coronary artery tree ( CAT‐FU ) as smooth three‐dimensional (3D) surfaces using a subdivision fitting scheme with the same coarse meshes by which the correspondence among these surface points is generated. Specifically, a rigid point set registration is used to transform the coarse mesh from the CAT‐FU to CAT‐BL. The plaque thickness and the thickness difference is calculated as the distance between corresponding surface points. To evaluate the registration accuracy, the average distance between manually defined markers on clinical scans is calculated. Artificial CAT‐BL and CAT‐FU pairs were created to simulate the plaque decrease and increase over time. Results For 116 pairs of markers from nine clinical scans, the average marker distance after registration was 0.95?±?0.98?mm (two times the voxel size). On the 10 artificial pairs of datasets, the proposed method successfully located the plaque changes. The average of the calculated plaque thickness difference is the same as the corresponding created value (standard deviation?±?0.1?mm). Conclusions The proposed method automatically calculates local coronary plaque thickness differences over time and can be used for 3D visualization of plaque differences. The analysis and reporting of coronary plaque progression and regression will benefit from an automatic plaque thickness comparison.
机译:目的目前,冠状动脉斑块的改变是手动基线和随访冠状动脉CT血管造影(CCTA)图像长期冠状动脉斑块发展的调查比较。我们提出来衡量一段时间内斑块厚度变化的自动方法。方法:我们的基线冠状动脉树(CAT-BL)均管腔和血管壁建模和后续的冠状动脉树(CAT-FU)为平滑的三维(3D)表面采用细分与同一粗装修方案由其中产生这些表面点之间的对应的网格。具体地,刚性点集配准用于从CAT-FU到CAT-BL变换粗网。斑块厚度和厚度差被计算为相应的表面点之间的距离。为了评估对位精度,在临床扫描手动定义标记之间的平均距离被计算。人工CAT-BL和CAT-FU对创建模拟斑块减少,随着时间增加。结果对于116双来自九个临床扫描标记,登记后的平均标记的距离为0.95?±0.98?毫米(两次的体素尺寸)。在10人工对数据集的,所提出的方法成功地位于斑块变化。平均所计算的斑块厚度差是一样的相应的创建值(标准偏差σ±?0.1?毫米)。结论所提出的方法自动地计算随时间局部冠状动脉斑块的厚度差,并且可以被用于斑块区别3D可视化。冠状动脉斑块进展和回归分析和报告将受益于自动斑块厚度比较。

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