scholarly journals Local statistical regularization method for solving image reconstruction problems in emission Tomography with Poisson data

2021 ◽  
Author(s):  
N. Denisova ◽  
H. Kertész ◽  
T. Beyer
Keyword(s):  
Image Reconstruction ◽  
Regularization Method ◽  
Emission Tomography ◽  
Statistical Regularization ◽  
Poisson Data

scholarly journals Quantitative imaging and automated fuel pin identification for passive gamma emission tomography

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ming Fang ◽  
Yoann Altmann ◽  
Daniele Della Latta ◽  
Massimiliano Salvatori ◽  
Angela Di Fulvio
Keyword(s):  
Inverse Problem ◽  
Image Reconstruction ◽  
Image Resolution ◽  
Emission Tomography ◽  
Nuclear Safeguards ◽  
Activity Levels ◽  
Spent Fuel ◽  
Gamma Emission ◽  
Cross Sectional ◽  
Fuel Rods

AbstractCompliance of member States to the Treaty on the Non-Proliferation of Nuclear Weapons is monitored through nuclear safeguards. The Passive Gamma Emission Tomography (PGET) system is a novel instrument developed within the framework of the International Atomic Energy Agency (IAEA) project JNT 1510, which included the European Commission, Finland, Hungary and Sweden. The PGET is used for the verification of spent nuclear fuel stored in water pools. Advanced image reconstruction techniques are crucial for obtaining high-quality cross-sectional images of the spent-fuel bundle to allow inspectors of the IAEA to monitor nuclear material and promptly identify its diversion. In this work, we have developed a software suite to accurately reconstruct the spent-fuel cross sectional image, automatically identify present fuel rods, and estimate their activity. Unique image reconstruction challenges are posed by the measurement of spent fuel, due to its high activity and the self-attenuation. While the former is mitigated by detector physical collimation, we implemented a linear forward model to model the detector responses to the fuel rods inside the PGET, to account for the latter. The image reconstruction is performed by solving a regularized linear inverse problem using the fast-iterative shrinkage-thresholding algorithm. We have also implemented the traditional filtered back projection (FBP) method based on the inverse Radon transform for comparison and applied both methods to reconstruct images of simulated mockup fuel assemblies. Higher image resolution and fewer reconstruction artifacts were obtained with the inverse-problem approach, with the mean-square-error reduced by 50%, and the structural-similarity improved by 200%. We then used a convolutional neural network (CNN) to automatically identify the bundle type and extract the pin locations from the images; the estimated activity levels finally being compared with the ground truth. The proposed computational methods accurately estimated the activity levels of the present pins, with an associated uncertainty of approximately 5%.

scholarly journals Analytic time-of-flight positron emission tomography reconstruction: two-dimensional case

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Gengsheng L. Zeng ◽  
Ya Li ◽  
Qiu Huang
Keyword(s):  
Positron Emission Tomography ◽  
Image Reconstruction ◽  
Iterative Algorithm ◽  
Emission Tomography ◽  
Two Dimensional ◽  
Profile Function ◽  
Positron Emission ◽  
Pet Image Reconstruction ◽  
2D Filter ◽  
Tof Pet

AbstractIn a positron emission tomography (PET) scanner, the time-of-flight (TOF) information gives us rough event position along the line-of-response (LOR). Using the TOF information for PET image reconstruction is able to reduce image noise. The state-of-the-art TOF PET image reconstruction uses iterative algorithms. Analytical image reconstruction algorithm exits for TOF PET which emulates the iterative Landweber algorithm. This paper introduces such an algorithm, focusing on two-dimensional (2D) reconstruction. The proposed algorithm is in the form of backprojection filtering, in which the backprojection is performed first, and then a 2D filter is applied to the backprojected image. For the list-mode data, the backprojection is carried out in the event-by-event fashion, and a profile function may be used along the projection LOR. The 2D filter depends on the TOF timing resolution as well as the backprojection profile function. In order to emulate the iterative algorithm effects, a Fourier-domain window function is suggested. This window function has a parameter, k, which corresponds to the iteration number in an iterative algorithm.

scholarly journals Examining an image reconstruction method in infrared emission tomography

2019 ◽  
Vol 98 ◽  
pp. 266-277 ◽  
Author(s):  
Loizos Koutsantonis ◽  
Aristotelis-Nikolaos Rapsomanikis ◽  
Efstathios Stiliaris ◽  
Costas N. Papanicolas
Keyword(s):  
Image Reconstruction ◽  
Infrared Emission ◽  
Emission Tomography ◽  
Reconstruction Method ◽  
Image Reconstruction Method
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