Reference for citation: Kodenko M. R., Vasiliev Yu. A., Kulberg N. S., Samorodov A. V., Reshetnikov R. V. Contrast agent distribution in the lumen and wall of the abdominal aorta according to CT-angiographic study data. Journal of Instrument Engineering. 2024. Vol. 67, N 9. P. 798–812 (in Russian). DOI: 10.17586/0021-3454-2024-67-9-798-812.

Abstract. An approach to the approximation and analysis of the CT density signal component associated with intravascular radiocontrast agent (RCA) based on computed tomography angiography (CTA) images of the abdominal aorta is presented. The aim of the work is to study the possibility of extracting and analyzing the RCA-induced component in the lumen and wall of the abdominal aorta on the CTA image. A functionality for describing one-dimensional and two-dimensional distribution of the CTA as a set of sums of sigmoid of a special type is proposed. The nonlinear least squares method with Levenberg – Marquardt optimization is used for approximation. The algorithm is tested on an open data set consisting of 594 CTA images. Data preparation is performed using specialized software Slicer 3D. The results demonstrate the absence of statistically significant differences in the CTA density values between the original images and the approximation results (p> 0.05, paired Wilcoxon test). The sensitivity of the model to different distributions of the RCA in the area of aneurysm, thrombosis and origin of the main arteries is demonstrated. Sensitivity is defined as the presence of statistically significant differences in the calculated parameters of the model for the area of homogeneous and nonhomogeneous distribution of the RCA within each of the CT studies. The values of the root-mean-square approximation error for the specified areas do not differ statistically significantly and are unimodally distributed (p > 0.7) within a single CT study. The proposed approach can be useful for personalizing CTA, developing algorithms for processing CTA data, synthesizing non-contrast CT data, and training artificial intelligence algorithms.

Keywords: computed tomography, angiography, image processing, contrast agent, modeling, X-ray density, artificial intelligence

Acknowledgement: this paper was prepared by a group of authors as a part of the research and development effort titled “Development of software for automated generation of data sets containing synthetic native-phase CT studies to train and validate AI algorithms” (USIS No. 123031500002-1); the authors express gratitude to the employees of the Scientific and Practical Center for Diagnostics and Tractology of the Moscow Department of Healthcare, radiologists I. A. Blokhin, A. V. Soloviev.

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