GAN-based method for converting multimodal low-dose CT to high-dose CT

Abstract

The invention discloses a method and system for converting multi-mode low-dose CT into high-dose CT based on a GAN, and a medium. The method comprises the steps: inputting low-dose CT in any mode; performing two-dimensional discrete wavelet transform on the low-dose CT to obtain a plurality of decomposition results; and inputting the low-dose CT and a plurality of decomposition results into a trained encoder in the GAN network, encoding, and decoding the encoding result through a decoder in the GAN network to obtain a corresponding high-dose modal image. The method is based on the wide development of GAN in multi-domain conversion and the decomposition capability of traditional wavelet transform, the low-dose CT and the wavelet transform result are inputted into the trained encoder in theGAN network together for encoding, the encoding result is decoded through the decoder in the GAN network to obtain the corresponding high-dose modal image, and low-dose CT image conversion in any modeis conveniently converted to the high-dose CT image.

Description

technical field [0001] The present invention relates to the field of medical image processing, in particular to a method, system and medium for converting multi-modal low-dose CT into high-dose CT based on GAN (Generative Adversarial Network), which is used for low-dose CT of any modality by generating countermeasures. Network conversion generates high-dose CT. Background technique [0002] As one of the modern mainstream medical images, the detection method of computer tomography (Computed tomography, CT) has been widely used in clinical diagnosis in various clinical fields. With the popularization and development of CT scanning, more and more people have begun to pay attention to the possible radiation hazards of CT scanning to the human body. CT scans are generally accompanied by high levels of x-ray radiation, and medical research has shown that excessive x-ray exposure may induce metabolic abnormalities or cancer, leukemia or other genetic diseases. The researchers ho...

AI Technical Summary

Problems solved by technology

In order to solve this inherent physical problem, many methods have been designed in the past to improve the image quality of low-dose CT (Low-Dose CT, LDCT). The traditional methods include model-based iterative reconstruction, filtering before reconstruction, and reconstructed images. Post-processing, the processing technology before reconstruction is specific to the scanner, and the data of commercial scanners is not easy to provide to researchers, and the processing technology after reconstruction cannot accurately determine the noise distribution in the image domain, so that the algorithm cannot be used in Optimal trade-off between structure preservation and noise reduction

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