Background and objective: CTA is a gold standard of preoperative diagnosis of
abdominal aorta and typically used for geometric-only characteristic
extraction. We assume that a model describing the dynamic behavior of the
contrast agent in the vessel can be developed from the data of routine CTA
studies, allowing the procedure to be investigated and optimized without the
need for additional perfusion CT studies. Obtained spatial distribution of CA
can be valuable for both increasing the diagnostic value of a particular study
and improving the CT data processing tools. Methods: In accordance with the
Beer-Lambert law and the absence of chemical interaction between blood and CA,
we postulated the existence of a deterministic CA-induced component in the CT
signal density. The proposed model, having a double-sigmoid structure, contains
six coefficients relevant to the properties of hemodynamics. To validate the
model, expert segmentation was performed using the 3D Slicer application for
the CTA data obtained from publicly available source. The model was fitted to
the data using the non-linear least square method with Levenberg-Marquardt
optimization. Results: We analyzed 594 CTA images (4 studies with median size
of 144 slices, IQR [134; 158.5]; 1:1 normal:pathology balance). Goodness-of-fit
was proved by Wilcox test (p-value > 0.05 for all cases). The proposed model
correctly simulated normal blood flow and hemodynamics disturbances caused by
local abnormalities (aneurysm, thrombus and arterial branching). Conclusions:
Proposed approach can be useful for personalized CA modeling of vessels,
improvement of CTA image processing and preparation of synthetic CT training
data for artificial intelligence.

通过对公开可获得的 CTA 数据进行分析和拟合，提出了一个描述血管中动态对比剂行为的模型，该模型可以有助于个性化的对比剂建模、CTA 图像处理的改进，并为人工智能的合成 CT 训练数据的准备提供支持。