Method for coronary blood flow velocity estimation based on angiographic images

Rebrova A.A., Danilov A.A.
 pdf [in Russian] (1775K)  pdf [in English] (1734K)

In modern cardiology, accurate assessment of the functional significance of coronary artery stenoses is a critical factor for selecting treatment strategies and making informed clinical decisions. This paper presents an automated algorithm for processing dynamic X-ray angiographic image sequences aimed at estimating blood flow velocity. This parameter serves as the basis for determining the Quantitative Flow Ratio (QFR), which acts as an effective noninvasive alternative to traditional invasive fractional flow reserve (FFR) measurements. The proposed methodology successfully overcomes classic challenges of angiographic analysis, such as vessel motion artifacts during the cardio-respiratory cycle, variable contrast opacification, and the geometric complexity of the vascular tree in two-dimensional projections.

The presented processing workflow includes several key stages. Initially, frame preprocessing is performed to suppress noise and filter out the anatomical background. Subsequently, segmentation is implemented using a Sato filter and Otsu thresholding, followed by skeletonization to extract vessel centerlines. Particular attention is paid to the algorithm for automated identification of bifurcation points and the filtration of artifactual intersections caused by vessel overlapping. To ensure data continuity, a temporal tracking method for the target segment based on template correlation is applied, which is especially important during phases with low contrast agent concentration. The mathematical core of the algorithm is based on solving a 1D inverse problem for the advection-diffusion equation, allowing for the recovery of blood flow velocity from temporal intensity curves.

As part of the study, a detailed validation of the method was conducted by comparing automated calculation results with manual expert measurements across ten clinical datasets. The results confirm the robustness of the computational scheme within physiologically relevant ranges and its ability to significantly reduce inter-observer variability. The developed approach minimizes the need for physician intervention in the data processing stage, opening up prospects for creating real-time clinical decision support systems in the catheterization laboratory setting.

Keywords: medical image segmentation, coronary circulation, fractional flow reserve, coronary flow reserve, quantitative flow ratio, angiography, blood flow velocity, inverse problems, numerical methods
Citation in English: Rebrova A.A., Danilov A.A. Method for coronary blood flow velocity estimation based on angiographic images // Computer Research and Modeling, 2026, vol. 18, no. 3, pp. 715-735
Citation in English: Rebrova A.A., Danilov A.A. Method for coronary blood flow velocity estimation based on angiographic images // Computer Research and Modeling, 2026, vol. 18, no. 3, pp. 715-735
DOI: 10.20537/2076-7633-2026-18-3-715-735
URL: http://crm-en.ics.org.ru/journal/article/3741/
Creative Commons License This work is licensed under a Creative Commons Attribution-NoDerivs 3.0 Unported License.

Copyright © 2026 Rebrova A.A., Danilov A.A.

Indexed in Scopus

Full-text version of the journal is also available on the web site of the scientific electronic library eLIBRARY.RU

The journal is included in the Russian Science Citation Index

The journal is included in the RSCI

International Interdisciplinary Conference "Mathematics. Computing. Education"

Website Copyright © 2009–2026 Institute of Computer Science