Digital X-ray machine automatic exposure control method and device

Abstract

The invention relates to a digital X-ray machine automatic exposure control method and device, and belongs to the technical field of digital radiography. The method mainly includes the steps of processing a pre-exposure image in secondary exposure and optimizing exposure parameters in a self-adaptive mode. The optimal threshold value is correspondingly found according to positions through gray stretch of the pre-exposure image, the image is divided into different areas, areas of interest with different components are extracted, the average gray level, the signal to noise ratio and the contrast ratio are calculated respectively, and then the main exposure parameters (mA, s and KV) are optimized in the self-adaptive mode; the precise time sequence is provided for the exposure process and data acquisition through a synchronous control unit, and the imaging effect is prevented from being influenced by time delay or antedating reaction. By the adoption of the multiple components, multiple target areas and multiple image standards, the exposure parameters are adjusted through a self-adaptive algorithm, and the top-quality image can be obtained through a small exposure dose when exposure is conducted under the synchronous control.

Description

technical field [0001] The invention belongs to the technical field of digital radiography (DR for short), and relates to an automatic exposure control method and device for a digital X-ray machine. Background technique [0002] In medicine, X-ray photography is a double-edged sword. While filming helps doctors diagnose, it also causes different degrees of radiation damage to patients. In the traditional manual exposure process, the requirements for operators are very high. The doctor should determine the exposure parameters (KV, mA, s) according to the patient's body shape and position, where KV determines the imaging contrast range, and the exposure dose (mA) determines the signal-to-noise of the image. Ratio; exposure time (s) determines the sharpness of the image), the quality of the film taken out is greatly affected by human factors, and the image quality is unstable. Automatic Exposure Control (AEC for short) technology can adaptively adjust exposure parameters durin...

AI Technical Summary

Problems solved by technology

[0004] At present, some technologies simply extract the region of interest in the image through secondary exposure, and adjust the main exposure parameter mAs based on the signal-to-noise ratio of the region. Compared with the automatic exposure control method of the ionization chamber, it can adaptively adjust the exposure according to the pre-exposure situation. parameter, which improves the imaging quality and the accuracy of dose control. However, the method based on the size, shape and gray level statistics of the anatomical structure is used to segment the pre-exposure image. The region of interest is extracted; the extracted region of interest has a single component, which cannot reflect the pre-exposure situation well; the exposure parameter mAs is only adjusted by the signal-to-noise ratio of the characteristic parameters of the region of interest, and the contrast and sharpness of the pre-exposure image are not used to optimize the exposure parameters. The imaging effect is not good, and the exposure dose control is not precise enough

[0005] There is also a technology that adjusts the exposure parameter mA or exposure time by performing grayscale statistics on the region of interest in the image and adjusting the exposure parameter mA or exposure time with the average grayscale, and extracts the region of interest by performing noise reduction processing on the pre-exposure image and extracting the edge contour. The image has undergone noise reduction processing, which cannot reflect the pre-exposure situation well. At the same time, only extracting a single component interest area cannot reflect the pre-exposure situation well; only adjust the exposure parameters mA or s with the average gray level of the interest area, The contrast and sharpness of the pre-exposure image are not used to optimize the exposure parameters, which affects the automatic exposure effect;

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