Construction method of system for realizing noise reduction and resolution recovery of projection data

Abstract

The invention relates to the technical field of radiation imaging, in particular to a construction method of a system for realizing projection data noise reduction and resolution recovery, which comprises the following steps: performing simulation modeling on the imaging process of the existing radiation imaging system, and setting the imaging key parameters in a simulation model to be adjustable; a digital imaging target object model is adopted for the simulation model, and a corresponding digital model is generated; calling a digital noise adding algorithm of a corresponding type to generate a low-resolution noise-containing original projection image sample; adjusting imaging key parameters in the simulation model, generating an optimized digital model and obtaining optimized projection drawing data as a high-resolution and noiseless optimized projection drawing sample; and training a deep learning algorithm network by using the original projection image sample and the optimized projection image sample to obtain a system capable of simultaneously realizing noise reduction and resolution recovery of the projection image data. According to the invention, the noise reduction and resolution recovery of the projection image data can be realized at the same time, so that the purpose of improving the image quality under the condition that the hardware condition is not changed is achieved.

Description

technical field [0001] The invention relates to the technical field of radiation imaging, in particular to a method for constructing a system for realizing noise reduction and resolution recovery of projection image data. Background technique [0002] Medical or industrial radiation imaging systems based on X-rays or gamma rays mainly include X-ray plain film imaging, X-ray computed tomography (CT), gamma cameras, single photon emission computed tomography (SPECT), etc. In the above system, the hardware parameters that determine the imaging resolution are mainly the focal point size of the X-ray source or the SPECT collimator scheme, the inherent resolution of the detector, and the object distance (the distance from the imaging target to the focal point and the image distance (the distance from the detector to the focal point) ). Due to the limitations of physical principles, technical level, cost and application scenarios (space), it is more difficult to improve the above h...

AI Technical Summary

Problems solved by technology

For imaging systems that require algorithmic reconstruction, noise reduction and resolution recovery operations are performed on the projection image data, which has the advantage that the effect is not affected by the reconstruction algorithm, and the noise and resolution characteristics of the projection image are simpler than those of the final reconstructed image. The disadvantage is that the image reconstruction algorithm needs to be adjusted in a targeted manner

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