quick X-ray dynamic real-time imaging denoising method based on deep learning

Abstract

The invention discloses a quick X-ray dynamic real-time imaging denoising method based on deep learning. The method comprises the following steps: S1, preprocessing clinically collected X-ray dynamic real-time video data to obtain a data set; s2, a deep learning model comprising at least one denoising module is constructed, and the at least one denoising module forms a layer of network or a cascaded two-layer network; s3, training the deep learning model by using the data set; and S4, inputting a plurality of continuous frames of the X-ray dynamic real-time video with low radiation dose into the deep learning model trained in the step S3, and outputting a denoising result of a middle frame of the plurality of continuous frames. According to the deep learning-based fast X-ray dynamic real-time imaging video denoising method provided by the invention, the denoising time is greatly shortened on the basis of ensuring the denoising effect, and the deep learning-based fast X-ray dynamic real-time imaging video denoising method can be applied to operation video real-time denoising processing which cannot be achieved by the conventional method.

Description

technical field [0001] The invention relates to the technical field of video quality enhancement, in particular to a method for denoising video of rapid X-ray dynamic real-time imaging during surgery based on deep learning. Background technique [0002] The perspective system guided by X-ray dynamic real-time imaging is used to guide the use of surgical instruments in the process of dissection by surgeons. The real-time feedback provided by these see-through systems captures the precise movements of the surgeon and displays them on monitors in the operating room. Therefore, this dynamic real-time imaging of X-rays has become the standard for many surgical procedures because it allows the surgeon to have more control over the operation while being less invasive, thereby reducing tissue trauma and disturbance. [0003] However, these fluoroscopy systems all require sufficient radiation doses to obtain high-quality video with sufficient tissue contrast, and this radiation expo...

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Problems solved by technology

[0005] The main purpose of the present invention is to overcome the deficiencies of the prior art, and propose a fast X-ray dynamic real-time imaging denoising method based on deep learning to solve the problem that the X-ray fluoroscopy video generated under low-dose radiation intensity has extremely high noise and cannot Questions for clinically guiding surgery

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