Coronary artery stenosis quantification method and device

Abstract

The invention provides a coronary artery stenosis quantification method and device, and the method comprises the steps: building the corresponding relation between the multi-view image features of a coronary artery multi-view medical image and the morphological parameters of coronary artery stenosis through the self-learning capability of an artificial neural network; obtaining current multi-viewimage features of the current coronary artery multi-view medical image of the patient; determining morphological parameters of the current coronary artery stenosis corresponding to the current multi-view image features through the corresponding relationship; specifically, determining the morphological parameters of the current coronary artery stenosis corresponding to the multi-view image features, which includes that the morphological parameters of the coronary artery stenosis corresponding to the multi-view image features identical to the current multi-view image features in the corresponding relation are determined as the morphological parameters of the current coronary artery stenosis. The morphological parameters of coronary artery stenosis are predicted based on multiple perspectives, and the influence of stenosis overlapping with other blood vessels on stenosis quantification is relieved.

Description

technical field [0001] The present application relates to the field of medical detection, in particular to methods and devices for quantifying coronary artery stenosis. Background technique [0002] Coronary atherosclerotic heart disease (CHD) is one of the most common cardiovascular diseases worldwide. Coronary artery disease occurs when atherosclerotic plaque builds up on the walls of the coronary arteries. Atherosclerotic plaque causes narrowing or occlusion of coronary arteries, which restricts blood supply to the heart muscle and can lead to myocardial ischemia. When coronary artery stenosis reaches a certain level, interventional therapy is required. At this time, it becomes very important to quantify coronary artery stenosis clinically. Quantified related parameters determine the degree of coronary artery stenosis and serve as a reference for interventional decision-making and stent selection. [0003] Clinically, X-ray coronary angiography is still the most common...

AI Technical Summary

Problems solved by technology

In 2015, Yang Jian et al. proposed a novel composite model of the average composite post-external force back projection composition in IEEE Transactions on Biomedical Engineering, and integrated it into the 3-D reconstruction from multiple angiographic coronary arteries. Deformation model framework, although it can obtain more accurate quantitative results, but through the intermediate process of reconstruction, more errors wil

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