Gamma radioactive imaging method based on deep learning

A gamma radioactivity and deep learning technology, applied in scientific instruments, nuclear radiation exploration, measurement devices, etc., can solve the problems of inability to obtain high-resolution images, complex encoding and decoding algorithms, and long imaging time, and achieve the goal of improving a single Imaging results, faster decoding, reduced exposure time effects

Active Publication Date: 2018-12-18
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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Problems solved by technology

However, the traditional coded aperture collimator is designed according to complex mathematical formulas, and its encoding and decoding algorithms are relatively complex, which limits the design of the coded aperture collimator

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  • Gamma radioactive imaging method based on deep learning
  • Gamma radioactive imaging method based on deep learning
  • Gamma radioactive imaging method based on deep learning

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[0026] The specific embodiments of the present invention will be further described below in conjunction with the drawings. The following implementations are for explaining the present invention and the present invention is not limited to the following embodiments.

[0027] See figure 1 , A gamma radiation imaging method based on deep learning, including the following steps:

[0028] Step 101: Use the Monte Carlo method to simulate the encoding imaging process to obtain a sufficient number of encoded image samples.

[0029] Specifically, the Monte Carlo method is used to model the imaging process of the encoding hole gamma camera, and the imaging process of radioactive sources at different positions in the detection plane is simulated to obtain different positions, different numbers, and different types of radioactive sources in the encoding hole. The coded image formed on the horse camera. Monte Carlo method is also called random sampling method or statistical experiment method. It ...

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Abstract

The invention discloses a gamma radioactive imaging method based on deep learning, and belongs to the fields of radiation detection technology and radioactivity monitoring. The gamma radioactive imaging method can shorten required time for gamma radiation imaging, improve the image quality, and accurately reflect purpose of radioactive spatial distribution. The method includes the steps that a Monte Carlo method is used for simulating process of coded imaging, and a sufficient number of coded image samples are obtained; coded images are processed and used as samples to train and test a deep learning network model, and design of a coded aperture collimator is optimized; a gamma radiation coded image of a detected target area is obtained by using a coded aperture gamma camera; the gamma radiation coded image is decoded and processed by using the deep learning network completed by training; a depth map and a optical image of the detected target area are obtained by using a depth vision detection system; and a decoded radiation hotspot image is fused with the depth map and the optical image, and a composite image of radioactive hotspot distribution of the detected target area is obtained.

Description

Technical field [0001] The invention belongs to the field of radiation detection technology and radioactivity monitoring, and in particular relates to a gamma radioactive imaging method based on deep learning. Background technique [0002] As the nuclear industry and nuclear technology applications have penetrated into various fields of national economic development, the safety supervision of radioactive materials and the ability to respond to nuclear accidents have become issues of particular concern to the nuclear safety and nuclear security industry. The traditional radioactivity distribution detection technology mainly uses radiation detectors to measure various points in the target area, or uses array detectors to perform two-dimensional imaging of the target area, but neither can obtain the accurate location of radioactive materials in the real environment. In particular, for radioactive positioning in complex scenes, it is also necessary to consider the three-dimensional s...

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Application Information

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IPC IPC(8): G01V5/00
CPCG01V5/00
Inventor 汤晓斌龚频王鹏朱晓翔张锐
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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