Vascular guided wave elastography method and system based on machine learning

Abstract

The invention discloses a blood vessel guided wave elastic imaging method and system based on machine learning. The blood vessel guided wave elastic imaging method comprises the steps that numerical simulation is conducted on propagation of shear waves in a thin layer system of a blood vessel through finite element software, and a finite element analysis result is obtained; motion speed distribution of nodes in the whole field is obtained according to the finite element analysis result, the motion speeds of the nodes on the center line of a thin layer are extracted, and a frequency dispersioncurve is obtained to serve as an input signal of a neural network; and a training set and a test set of the neutral network are obtained according to the frequency dispersion curve, training is conducted through a neutral network method till the error on the training set is less than a preset value, and a blood vessel guided wave elastic imaged picture is obtained through the final neutral network. According to the blood vessel guided wave elastic imaging method, the measuring precision of blood vessel mechanical properties can be improved, good extensibility is achieved, in-vivo noninvasive rapid measurement of the blood vessel elastic property is achieved, operation is easy and convenient, and the measurement error is small.

Description

technical field [0001] The invention relates to the technical field of medical imaging, in particular to a machine learning-based vascular guided wave elastography method and system. Background technique [0002] Cardiovascular system diseases are the number one killer of human health in many countries and regions. Cardiovascular system diseases, such as arteriosclerosis and vascular plaque, will be accompanied by significant changes in the mechanical properties of blood vessels. In addition, some studies have confirmed that diabetes can lead to changes in the mechanical properties of blood vessels. Therefore, measuring the mechanical properties of blood vessels in vivo is of great significance for the early screening and diagnosis of many diseases. In related technologies, the mechanical properties of blood vessels are generally evaluated clinically by measuring the pulse wave velocity (Pulse Wave Velocity, PWV for short). However, this method can only roughly calculate t...

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Problems solved by technology

Since the wavelength of B-ultrasound is comparable to the thickness of blood vessels in magnitude, the actual measurement of blood vessel thickness with B-ultrasound often has large errors, which limits the further application of this method.

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