Contrast-agent-induced deterministic component of CT-density in the abdominal aorta during routine angiography: proof of concept study

TL;DR

This study develops a deterministic model based on contrast agent dynamics in CTA images of the abdominal aorta, enabling improved diagnosis and image processing without additional scans.

Contribution

It introduces a novel double-sigmoid model for describing contrast agent behavior in CTA, validated on a large dataset, enhancing personalized vessel analysis.

Findings

Model accurately fits CTA data and simulates normal and abnormal hemodynamics.

Goodness-of-fit confirmed by statistical tests across multiple cases.

Potential for improving CTA diagnostics and AI training data generation.

Abstract

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.