Study On Detection Method Based On 3D Imaging Using γ Photon

In the detection of industrial equipment and complex devices,the detection method of the inner wall of the chamber and the detection method of the flow field in a closed environment are subject to factors including but not limited to environment,device structure and materials,thus undisturbed and nondestructive high-precision detection results are hard to achieve.This has become an urgent problem to be solved.Based on the principle of positron annihilation and the characteristics of industrial testing,this paper investigated the key techniques regarding scattering correction and 3D image reconstruction in detection using γ-photon,in order to meet the testing requirements for two types of detection,i.e.,intra-cavity detection of complex devices in the industrial field and flow-field detection of dense materials in confined space.The 3D imaging detection method using γ-photon was systematically investigated by taking full advantages of characteristics of γ-photon,such as strong penetration,antiinterference,high detection precision,etc.In view of the problem that the materials of the measured object cause serious γ-photon scattering issues in industrial testing,in this paper,based on the γ-photon single scattering model in γ-photon 2D acquisition mode,an optimized γ-photon single scattering model in 3D acquisition mode was proposed,and based on this model,a γ-photon single scattering correction method that traverses the scattering angle was proposed.The running path of the scattered γ-photon was reverted to the real running path of the γ-photon from a spatial geometric angle.Through setting the energy window,the scattering angle of the γ-photon can be corresponding to the energy window.Then,the number of the scattered γ-photon can be determined and the γ-photon scattering can thus be accurately corrected.This γ-photon correction method can effectively improve the 3D imaging quality.Through simulation experiments and comparison with other scattering correction methods,the correction effect and technical advantages of this method are verified.To solve the problem that the short detection sampling time of γ-photon affects imaging quality,this paper introduced the 3D image reconstruction method that enhances the target by equally dividing and compensating γ-photon based on the OSEM iterative algorithm and the OTSU algorithm.The image effect was verified by image reconstruction experiment through GATE simulation platform.Meanwhile,deep learning theory was introduced into image reconstruction.The convolutional neural network was selected and transfer learning was used to achieve the few-shot learning of γ-photon image.Then,combining deep learning recognition with image reconstruction,the super-resolution image reconstruction was performed to realize the super-resolution display of γ-photon 3D images,which explores a new technical approach to improve the image quality.To detect and localize the defects of inner cavity of industrial complex devices,the proposed method in this paper followed the principle of positron annihilation and radionuclide decay.Through selecting appropriate radionuclide,the positron source reagent was prepared by chemical technique.After injecting the radionuclide solution into the inner cavity,the γ-photon array detector ring was used to record the annihilation events.Finally,the 3D image detection of the inner wall and defects of the device were achieved by mathematical model and 3D image reconstruction algorithm.In this paper,an experimental system was established to excite positrons and detect γ-photons for 3D imaging.Through conducting 3D image detection experiments on test pieces with different materials and structures,the feasibility,technical advantages and detection precision of 3D image detection method using γ-photon were verified.Also,the technical advantages of 3D image detection method using γ-photon were verified by comparing the 3D imaging detection method with other imaging detection methods.To detect the flow field in confined space with dense materials in industrial field,a γ-photon 3D imaging technology was proposed in this paper to detect flow field through establishing the mapping relationship between γ-photon imaging and flow field state.Through marking positron nuclides in the working medium,γ-photon was detected in the working field state,and then 3D imaging detection was conducted by using the volume rendering image.The ball rafting experiment and the spray experiment were designed to verify the feasibility of 3D imaging in flow field detection using γ-photon.The industrial combustion was simulated in this research.The experiment on combustion flow field detection was designed and conducted,and the distribution of field state was obtained.Moreover,CFD simulation results were compared.The detection results were verified,which provides a theoretical basis for undisturbed,non-destructive testing and imaging for closed pipelines,the inner flow field of containers,chambers,etc.in industrial equipment.The research work in this paper provides a technical support for the application of 3D imaging using γ-photon in the industrial field and presents a novel non-destructive detection method for key components and process flow monitoring in industry,especially in the high-end equipment manufacturing field,thus providing a theoretical basis for developing industrial image detection device using γ-photon.