Method for predicting healthy radius of blood vessel path, method for predicting candidate stenosis of blood vessel path, device for predicting degree of vascular stenosis

Abstract

The present disclosure provides a method for predicting a healthy radius of a blood vessel path, a method for predicting a candidate stenosis of a blood vessel path, and a device for predicting a degree of vascular stenosis. The method for predicting the healthy radius includes: obtaining the vascular radius of the vascular path; using a processor, detecting the peak value of the vascular radius of the vascular path; and using the processor, predicting the healthy radius by performing regression on the radius peak. The device for predicting the degree of vascular stenosis includes a processor, which is configured to realize by executing executable instructions: acquiring image units along the centerline within the candidate stenosis range of each vascular path; and based on the acquired image units, using a learning network to determine The stenosis of each vascular path, wherein the learning network is based on a convolutional neural network and a recurrent neural network. The device can automatically determine the candidate stenosis and detect the stenosis according to the candidate stenosis range, which can significantly reduce the calculation load, improve the detection efficiency and effectively avoid missed detection.

Description

technical field [0001] The present disclosure relates to a method and device for predicting vascular physiological parameters, and more specifically, to a method for predicting the healthy radius of a vascular path (a section of blood vessel), a method for predicting a candidate stenosis of a vascular path, and using machine learning to A device for predicting the degree of narrowing of blood vessels. Background technique [0002] Recent studies have shown that the stenosis of blood vessels is of great significance in the diagnosis and prevention of cardiovascular diseases. Usually the stenosis of blood vessels is manually determined, that is, experts judge by manual observation of coronary CT angiography (CTA) and / or quantitative coronary angiography (QCA), but this judgment is time-consuming, tedious and wastes a lot of time. resources and relies heavily on the experience of experts. [0003] Although some automatic or semi-automatic methods for determining the degree of...

AI Technical Summary

Problems solved by technology

The independent training of the modules requires corresponding training data, and the requirements for the training data are high; and the training cannot learn from each other, and the optimal performance of the system as a whole cannot be achieved.

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