GAN enhanced magnetic induction imaging method and system based on complex value convolution

Abstract

The invention discloses a GAN enhanced magnetic induction imaging method and system based on complex valued convolution, and the method comprises the steps: S1, collecting voltage sequence data, constructing a complex valued neural network model, inputting the voltage sequence data into the complex valued neural network model for training, and obtaining a preliminary conductivity distribution image; s2, constructing a generative adversarial network model, and inputting the initial conductivity distribution image into the generative adversarial network model for training to obtain a generator for image enhancement; and S3, inputting the initial conductivity distribution image into the generator to obtain a high-precision target conductivity distribution diagram. According to the invention, the adversarial generative network model is used as an image optimization module to carry out image enhancement on the output of the complex valued convolutional network, the complex valued characteristics of the voltage sequence data are fully utilized, the training efficiency of the neural network and the accuracy of conductivity reconstruction are improved, and the resolution and precision of the final image are further improved.

Description

technical field [0001] The invention relates to the fields of biomedical imaging and deep learning, and mainly relates to a GAN-enhanced magnetic induction imaging method and system based on complex-valued convolution. Background technique [0002] Imaging technologies used for clinical diagnosis mainly include ultrasound imaging (ultrasonic imaging), X-ray computer-tomography (X-CT), magnetic resonance imaging (magnetic resonance imaging, MRI), and positron emission tomography. (positron emission tomography, PET) and so on. While these detection technologies have brought great impetus to human medicine, there are still various limitations. For example, high prices, inconvenient operation, radiation, and inability to monitor in real time. Especially with the economic development, my country's aging population is becoming more and more serious, and diseases such as cerebral hemorrhage and cerebral infarction are threatening the elderly population. Real-time image monitorin...

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Problems solved by technology

[0011] This invention applies the residual network in deep learning to solve the inverse problem of MIT, but it does not make full use of the information contained in the voltage sequence. Each voltage value collected by MIT equipment contains two parts, real part and imaginary part. , which contains phase information, but this invention only takes the amplitude of the voltage as useful information to input into the neural network, and loses part of its information

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