Tomographic Gamma Scanning Research Articles

Bayesian Inference for the Quantitative Analysis of Nonuniformly Distributed Uranium Radioactivity in a Waste Drum

The characteristics of radioactive waste should be identified for free release or permanent disposal. Radioactive waste from nuclear power plants and fuel fabrication facilities is usually stored in a 200 L drum. A sampling procedure or non-destructive assay (NDA) is used to evaluate the properties of the waste. The distribution of the measured nuclides in the drum is difficult to know in advance, resulting in a radioactivity bias in NDA. This non-uniform radioactivity distribution is a critical point that makes it hard to evaluate waste characteristics. The most commonly used NDA method for drum scanning is a Segment Gamma Scanning (SGS) method or a Tomographic Gamma Scanning (TGS) method. The SGS method produces inaccurate results when radionuclides are non-uniformly distributed within a drum. The TGS method, on the other hand, can accurately analyze radioactivity in non-uniform situations. However, the complexity of the mechanical configuration and a time-consuming calibration procedure causes inconvenience in equipment operation. In this study, we developed a Bayesian inference model for quantitatively analyzing non-uniformly distributed uranium radioactivity in a waste drum from a single measurement in a simple mechanical setup. Measurements with three-dimensionally distributed uranium powder (UO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ) in a waste drum show that the proposed method analyzes radioactivity on average about six times more accurately than the SGS method.

Reconstruction of tomographic gamma scanning transmission image from sparse projections based on convolutional neural networks

Tomographic Gamma Scanning (TGS) is an important Non-Destructive Assay (NDA) method for radioactive waste, and transmission imaging is the basis of the TGS. Traditional algorithms reconstruct the transmission image require the provision of the same amount of projection data as the pixel value of the transmission image, which requires a long time to scan and severely limits the industrial application of TGS. Sparse angle scanning is an optimal approach to improve the efficiency of the TGS system, but the amount of data generated by sparse angle scanning is difficult to support traditional algorithms, and blurs and artifacts will appear in the reconstructed image. In this work, Algebraic Reconstruction Technique (ART) combined with the Residual Network(ResNet) to reconstruct TGS transmission images was proposed. The training data set was built with simulation data by the Monte Carlo method, and the self-developed TGS equipment was used for the experimental test. Experimental results show that ResNet can be used to reconstruct high-resolution TGS transmission images from sparse projection data. And compared with traditional algorithms, the proposed method has faster reconstruction speed and higher image quality.

An improved ART algorithm for attenuation coefficient reconstruction of tomographic gamma scanners

A method of reconstructing the attenuation coefficient in the case of tomographic gamma scanning (TGS) imaging of nuclear waste tanks is proposed. The method was developed on the basis of the algebraic reconstruction technique (ART) method and is called the simultaneous iterative reconstruction techniques diagonally relaxed orthogonal projections (SIRT-DROP) method. To evaluate the accuracy of the method, a multi-layer model was designed. Each layer consisted of 25 (5 × 5) voxels, and each pixel measured 12 ×12× 12 cm. Results indicated that a maximum uncertainty of 34.88% and an average uncertainty of 14% were obtained when applying the ART method, while a maximum uncertainty of 16.11% and an average uncertainty of 9.96% were obtained when applying the proposed SIRT-DROP method. By performing linear regression in a C-language program on the energy–mass attenuation coefficient, the maximum and average uncertainties of the energy–mass attenuation coefficient were 35.32% and 15.67%, respectively, when the ART method was applied. When the SIRT-DROP method was applied, the maximum and average uncertainties of the energy–mass attenuation coefficient were lowered to 22.43% and 8.01%, respectively. Monte Carlo simulation results verified the capability of the proposed method, and the reconstructed attenuation coefficient was in an agreement with the pre-set value.

Transmission reconstruction algorithm by combining maximum-likelihood expectation maximization and a convolutional neural network for radioactive drum characterization

One of the most critical factors that affect reconstructions of the activity of a radioactive drum is the accuracy of tomographic gamma scanning transmission reconstruction. The traditional algorithms applied for reconstructing the density map, such as maximum-likelihood expectation maximization (MLEM), algebraic reconstruction technique, or filtered back-projection, produce a grid distribution with severe grid artifacts and a high level of noise, which significantly degrade the detail of the transmission image, thereby increasing the reconstruction error for both the density map and the activity. Thus, we propose a novel algorithm for transmission reconstruction by combining MLEM and a deep convolutional neural network (CNN). The CNN is trained using a supervised learning approach with image pairs obtained from the drum's ground truth (target) and the result constructed using MLEM (input). Our experimental results indicate that the proposed reconstruction algorithm can significantly improve the spatial resolution while also removing grid artifacts. In addition, the algorithm is sufficiently robust when dealing with a noisy input image. When the input image contained 20% noise, the mean squared error of the output image decreased by 78.51% compared with the conventional reconstruction method, and the peak signal-to-noise ratio and structural similarity index measure for the output image improved by 81.19% and 71.74%, respectively. The new algorithm improves the accuracy of the radioactive drum density map reconstruction as well as decreasing the measurement time required. We consider that the proposed algorithm is an effective new method for use in the field of radioactive waste drum transmission image reconstruction.

The design of integrated gamma ray scanning system for the uranium sample

An integrated gamma ray scanning system has been designed and built to extend that localizes and quantifies highly enriched uranium sample. The detection mechanisms of the two modes (Segmented Gamma Scanner and Tomographic Gamma Scanning) are analyzed respectively. Moreover, the parameters influence of speed and the deviation of the measurement accuracy are analyzed for the uranium sample. In view of these problems, the system has improved by the optimization processing, which include the optimization of the collimator opening for detector, the crosstalk and the time step. Finally, the experimental were investigated and discussed in detail. The single measurement time of the system reduced to about 30 min with an error of less than 5%, so it enhance the measurement accuracy and the speed effectively. The system has broad application prospects in the fields of nuclear safety, radioactive recyclables and non-destructive measurement of nuclear waste.

Design and optimization of an integrated gamma ray scanning system for the uranium sample

Abstract An integrated gamma ray scanning system has been designed and built to extend that localizes and quantifies highly enriched uranium sample. The detection mechanisms of the two modes (segmented gamma scanner and tomographic gamma scanning) are analyzed respectively. Moreover, the parameters influence of speed and the deviation of the measurement accuracy are analyzed for the uranium sample. In view of these problems, the system has improved by the optimization processing, which include the optimization of the collimator opening for detector, the crosstalk and the time step. Finally, the experiment were investigated and discussed in detail. The single measurement time of the system can reduce to about 30 min with an error of less than ±5%, so the measurement accuracy and the speed are enhanced effectively. The system has broad application prospects in the fields of nuclear safety, radioactive recyclables and non-destructive measurement of nuclear waste.

A collimator design method for the tomographic gamma scanning system with a fan-shaped NaI(Tl) detector array

The detection speed of the tomographic gamma scanning (TGS) system with a detector array is faster than the single detector system. The NaI(Tl) detector is inexpensive and can work at room temperature, making it ideal for use in the TGS system with a fan-shaped detector array. The collimator of the TGS system is one of the critical elements to ensure the reconstructed image's quality. In addition to providing good detection efficiency of the detector while improving the system's spatial resolution, a proper collimator structure may also reduce cross-interference between segments. We propose a collimator design method for the TGS system with a fan-shaped NaI(Tl) detector array and combine it with the Monte Carlo method to optimize the collimator. We get the collimator aperture size and shape of the TGS system through the simulation results. Simultaneously, according to the detectors' equiangular sector arrangement limitation, we propose setting up a fan-shaped shield with adjustable depth and height at the detector collimator's front. The cross-interference between segments is effectively reduced without reducing the current segment's detection efficiency. The transmission image reconstruction shows that the collimator designed by this method can be used in the TGS system with the fan-shaped NaI(Tl) detector array.

High Spatial Resolution Tomographic Gamma Scanning Reconstruction With Improved MLEM Iterative Algorithm Based on Split Bregman Total Variation Regularization

High spatial resolution tomographic gamma scanning (TGS) reconstruction is very important for the radioassay of drummed low-level radioactive waste. High spatial resolution means that the divided voxels are finer. Due to the large size of the drum, the traditional image reconstruction method based on complete samples takes a long time to scan. To limit the scanning time of the drum, sparse sampling is required. The maximum likelihood expectation maximization (MLEM) is widely used in TGS image reconstruction from projection data, but for high spatial resolution TGS imaging, its quality is insufficient to accurately describe the media boundary and determine radioactivity. The improved MLEM algorithm based on total variation (TV) regularization, such as the MLEM- TV minimization (TVM) algorithm, has been applied to reconstruct high spatial resolution TGS images. The split Bregman algorithm can quickly solve the partial differential equations of TV regularization. In this work, the split Bregman anisotropic TV (SBATV) and the split Bregman isotropic TV (SBITV) are the first time adopted to improve the iterative process of the MLEM algorithm, which are MLEM- SBATV and MLEM- SBITV. Experimental results show that both the MLEM- SBATV algorithm and the MLEM- SBITV algorithm can accurately reconstruct high spatial resolution TGS transmission images with sparse sampling. The MLEM- SBITV algorithm performs better in reconstructing the TGS emission images from sparse sampling than the traditional MLEM, MLEM- TVM, and MLEM- SBATV algorithms, increasing radionuclide positioning and radioactivity reconstruction accuracy.

Image Reconstruction Based on Total Variation Minimization for Radioactive Wastes Tomographic Gamma Scanning From Sparse Projections

Tomographic Gamma Scanning (TGS) is one of the most important non-destructive analyzed techniques for radioactive waste drums. By reconstructing the radioactivity distribution image, it can accurately realize the qualitative, quantitative, and positioning analysis of the radionuclides in the drum. However, the time consuming of the scanning is long and the reconstructed image is rough, which limits its good application in the practical assay of the waste drum. In this work, the total variational minimization (TVM) method was applied to improve the iterative process of the conventional algorithms of maximum likelihood expectation maximization (MLEM) and algebraic reconstruction technique (ART), then the MLEM-TVM and ART-TVM reconstruction methods were developed. The transmitted experiments were carried out where four kinds of materials were arranged in a segment whose densities ranging from 1.04 g/cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> to 2.02 g/cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> and a <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">152</sup> Eu isotope was set up as a transmission source. Compared with the traditional algorithms MLEM and ART, the MLEM-TVM and the ART-TVM algorithms have a better performance on the accuracy and the signal-to-noise ratio, and the MLEM-TVM algorithm achieves the best results, which means the quality of the reconstructed image is improved. The accuracy and effectiveness of the TVM method used in the TGS image reconstruction are verified in the work, and moreover, it can save the scanning time and enhance the TGS image resolution through sparse projection sampling.

Analysis and evaluation of tomographic gamma scanning image reconstruction algorithm

Abstract Tomographic Gamma Scanning (TGS) is a nondestructive analysis technique for accurately measuring the content of radioactive material in sealed containers. It has important applications in the fields of nuclear safety assurance and the measurement and analysis of radioactive waste drums. As the key of TGS technique, the quality of the emission image reconstruction algorithm will directly affect the accuracy of measurement results. In this paper, a computer simulation platform for TGS technique is established. The application of four emission image reconstruction algorithms in TGS technique is systematically studied by using 6 · 6 · 6 sample voxel model. Three representative sample media and radioactive source distribution models are established and the applicability of the four reconstruction algorithms is analyzed and evaluated in the presence and absence of detector noise interference. Computer simulation and experimental results show that ML-EM algorithm is the most applicable in the process of TGS image reconstruction.

Evaluation of an emission path length computation model applied at different resolutions in Tomographic Gamma Scanning

Tomographic Gamma Scanning (TGS) is a non-destructive technique used to assay and characterize low-level and mediate-level (LL & ML) radioactive waste drums qualitatively and quantitatively without opening waste drums. The ideal voxelized TGS model used in this paper is based on the assumption that matrices are uniformly distributed and radioactivity from gamma emitting radioactive sources is concentrated at the mass centroid of each voxel. An “point-detector (PD)” model in which the germanium crystal surface of High Purity Germanium (HPGe) detector is divided into different pixels are taken into account to compute the average emission path length of every gamma-ray through each voxel in the drum. Different from the previously described transmission PD model that treats the transmission source as the only “point”, this emission PD model treats the mass centroid of each voxel in the drum as an independent “point” and the finite field of views (FOVs) of the collimated High Purity Germanium (HPGe) detector must be carefully considered. The difference among this emission PD model applied at different resolutions is that the germanium crystal surface of HPGe detector is divided into different numbers of pixels respectively. Algebraic Reconstruction Technique (ART) with the non-negativity constraint and Maximum Likelihood Expectation Maximization (MLEM) are respectively used as the emission reconstruction algorithm. In order to compare these different emission path length computation models and emission reconstruction algorithms, three different series of waste drums are established, allowing to calculate the count distribution for various point source positions and different matrix configurations. Results show that ART algorithm is superior to MLEM algorithm to assay high-density drums. The more pixels the germanium crystal surface of HPGe detector is divided into, the better the emission-reconstructed results when using ART to assay high-density drums.

An improved OSEM iterative reconstruction algorithm for transmission tomographic gamma scanning

As one of the most advanced non-destructive analytical techniques for nuclear wastes, tomographic Gamma Scanning (TGS) is able to give accurate quantitative and qualitative measurements of nuclear waste barrels. OSEM (Ordered Subsets Expectation Maximization) has been used in transmission TGS image reconstruction on account of its good reconstruction quality and ideal convergence rate. In this paper, an improved method—NMO-OSEM (Non-minimization optimization OSEM) was proposed, it's an iterative algorithm with corrected initial values optimized by non-minimization optimization method. To evaluate its performance, a TGS system is used to perform transmission measurements on barreled nuclear wastes. The results show: ①Compared with the reconstructed images by traditional OSEM under 6 transmission energies (122 keV, 344 keV, 779 keV, 964 keV, 1112 keV, 1408 keV), the improved NMO-OSEM has a great advantage in reducing the artifacts and effectively improving the quality of the reconstructed images. ②the attenuation coefficients values of 72 voxels under 3 emission energies (662 keV, 1173 keV and 1332 keV) reconstructed by the proposed algorithm are more accurate (range of error: 0.22%–13.83%) than reconstructed by traditional OSEM (range of error: 1.31%–32.21%), which proves this method has more stable reconstruction precision and could be regarded as an ideal option for real applications.

An improved algebraic reconstruction technique for reconstructing tomographic gamma scanning image

Tomographic gamma scanning (TGS) is one of the most advanced non-destructive techniques for assaying the radioactive waste drum. When traditional algorithms are adopted to accurately reconstruct TGS images, the measurement data must be completely sampled by TGS system, which inevitably leads to a long-term assay. In order to save time, small number of data is measured by dividing the drum into several large voxels, which leads to inaccurate TGS images. In this work, an improved algebraic reconstruction technique (IART) is proposed to reconstruct TGS images. The total variation minimization method and the self-adaptive relaxation factor are applied to improve the iterative process of traditional algebraic reconstruction technique (ART). Experimental results show that this IART algorithm can accurately reconstruct TGS images simply based on small amount of measurement data. Compared with traditional technique, this method can reduce the mean square error and improve the signal-to-noise ratio of transmission images. And it can improve the positioning accuracy of radioisotope and the accuracy of reconstructed activity.

Novel edge treatment method for improving the transmission reconstruction quality in Tomographic Gamma Scanning

Tomographic Gamma Scanning (TGS) is a method used for the nondestructive assay of radioactive wastes. In TGS, the actual irregular edge voxels are regarded as regular cubic voxels in the traditional treatment method. In this study, in order to improve the performance of TGS, a novel edge treatment method is proposed that considers the actual shapes of these voxels. The two different edge voxel treatment methods were compared by computing the pixel-level relative errors and normalized mean square errors (NMSEs) between the reconstructed transmission images and the ideal images. Both methods were coupled with two different interative algorithms comprising Algebraic Reconstruction Technique (ART) with a non-negativity constraint and Maximum Likelihood Expectation Maximization (MLEM). The results demonstrated that the traditional method for edge voxel treatment can introduce significant error and that the real irregular edge voxel treatment method can improve the performance of TGS by obtaining better transmission reconstruction images. With the real irregular edge voxel treatment method, MLEM algorithm and ART algorithm can be comparable when assaying homogenous matrices, but MLEM algorithm is superior to ART algorithm when assaying heterogeneous matrices.

Tomographic 2-D gamma scanning for industrial process troubleshooting

Gamma scanning is a nuclear inspection technique widely used to troubleshoot industrial equipments in refineries and petrochemicals plants such as distillation columns and reactors. In this technique, a sealed radiation source and detector move along the equipment, and the intensity readouts generate the density profile of the equipment. Although many improvements have been introduced in recent years, the result of gamma scan still consists of a simple 1-D density plot. In this work, we present the tomographic gamma scanning that, using image reconstruction techniques, shows the result as a 2-D image of density distribution. Clearly, an image reveals more features of the equipment than a 1-D graph and many problems that could not be troubleshooted using the conventional technique can now be solved with this imaging technique. We use ART (Algebraic Reconstruction Technique) intercalated with total variation minimization filter. The use of total variation minimization leads to compressive sensing tomography, allowing to obtain good quality reconstruction from few irradiation data. We simulated the reconstruction of different density distributions. We applied the new technique to data obtained by irradiating with gamma rays phantoms that emulate industrial equipments. Finally, we present the result obtained by applying the innovative technique to real operating distillation column. It seems that the new technique has identified a problem in this equipment that is very difficult to detect using conventional gamma scan.

Influence of different path length computation models and iterative reconstruction algorithms on the quality of transmission reconstruction in Tomographic Gamma Scanning

This paper studies the influence of different path length computation models and iterative reconstruction algorithms on the quality of transmission reconstruction in Tomographic Gamma Scanning. The research purpose is to quantify and to localize heterogeneous matrices while investigating the recovery of linear attenuation coefficients (LACs) maps in 200 liter drums. Two different path length computation models so called ”point to point (PP)” model and ”point to detector (PD)” model are coupled with two different transmission reconstruction algorithms - Algebraic Reconstruction Technique (ART) with non-negativity constraint, and Maximum Likelihood Expectation Maximization (MLEM), respectively. Thus 4 modes are formed: ART-PP, ART-PD, MLEM-PP, MLEM-PD. The inter-comparison of transmission reconstruction qualities of these 4 modes is taken into account for heterogeneous matrices in the radioactive waste drums. Results illustrate that transmission-reconstructed qualities of MLEM algorithm are better than ART algorithm to get the most accurate LACs maps in good agreement with the reference data simulated by Monte Carlo. Moreover, PD model can be used to assay higher density waste drum and has a greater scope of application than PP model in TGS.

Semi-Tomographic Gamma Scanning Technique for Non-Destructive Assay of Radioactive Waste Drums

Segmented gamma scanning (SGS) and tomographic gamma scanning (TGS) are two traditional detection techniques for low and intermediate level radioactive waste drum. This paper proposes one detection method named semi-tomographic gamma scanning (STGS) to avoid the poor detection accuracy of SGS and shorten detection time of TGS. This method and its algorithm synthesize the principles of SGS and TGS. In this method, each segment is divided into annual voxels and tomography is used in the radiation reconstruction. The accuracy of STGS is verified by experiments and simulations simultaneously for the 208 liter standard waste drums which contains three types of nuclides. The cases of point source or multi-point sources, uniform or nonuniform materials are employed for comparison. The results show that STGS exhibits a large improvement in the detection performance, and the reconstruction error and statistical bias are reduced by one quarter to one third or less for most cases if compared with SGS.

Geometric matrix research for nuclear waste drum tomographic gamma scanning transmission image reconstruction**Supported by NSFC (41374130), National Science Fund for Distinguished Young Scholars (41025015) National Science Foundation (41274109, 41104118)

A geometric matrix model of nuclear waste drums is proposed for transmission image reconstruction from tomographic gamma scans (TGS). The model assumes that rays are conical, with intensity uniformly distributed within the cone. The attenuation coefficients are centered on the voxel (cube) of the geometric center. The proposed model is verified using the EM algorithm and compared to previously reported models. The calculated results show that the model can obtain good reconstruction results even when the sample models are highly heterogeneous.

Reconstruction of finer voxel grid transmission images in Tomographic Gamma Scanning

Tomographic Gamma Scanning (TGS) is a technique used to assay the nuclide distribution and radioactivity in nuclear waste drums. Limited to few transmission measurements, the drum is divided into large voxels and thus leads to the inhomogeneity of the voxels and negatively affects the results. A new algorithm is presented to reconstruct finer voxel grid transmission images with the same number of measurements. The small voxel size decreases the effect of the inhomogeneity and makes the result more accurate. The algorithm employs total variation minimization and precisely describes the attenuation process. The influences of the different scan modes are discussed with Monte Carlo simulations. Experiments are performed to verify the effectiveness of our method.

Study on detection simplification of tomographic gamma scanning using dynamic grids applied in the emission reconstruction

This paper presents a new emission reconstruction algorithm employing dynamic grids for tomographic gamma scanning in order to simplify the scanning process during the detection. Five types of scanning schemes to the single-point source and multi-point sources are studied aiming at both the uniform and heterogeneous matrixes in the radioactive waste drum. Comparing with the fixed grids, the error of emission reconstruction is obviously reduced if dynamic grids are applied. This reduction occurs because the grids are refined automatically around the emission source and consequently the precise positioning of the emission source leads to the low error of detection efficiency and emission reconstruction. Moreover, the simplification of the scanning process to half has less influence on the detection precision if dynamic grids are used, which will shorten the period of TGS detection.