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Limited-view Neutron CT Reconstruction with Sample Boundary.pdf
Available online at
ScienceDirect
Physics Procedia 69 (2015) 252 – 257
10 World Conference on Neutron Radiography 5-10 October 2014
Limited-view Neutron CT Reconstruction with Sample Boundary
Hu Wang, Yubin Zou*, Yuanrong Lu, Zhiyu Guo
State Key Laboratory of Nuclear Physics and Technology, Peking University, Beijing 100871, China
Abstract Reconstruction of limited-view CT is an ill-posed inversion problem. In order to suppress the artefacts and improve the image quality, it has been proved to be a good method to incorporate some a priori information of the sample (refers to as constraint in this paper) to the iterative process. In this paper, sample boundary is considered as a constraint and SART algorithm is chosen to test the performance of the constraint. Reconstructions from different number of projections of the famous Shepp-Logan head phantom with different levels of noise were simulated; projection data of a spark plug was acquired on the cold neutron CT platform of China Advanced Research Reactor (CARR) and the spark plug was reconstructed as well. Both the simulation and experimental results show that SART algorithm with sample boundary constraint leads to remarkable improvement of image quality and convergence speed for limited-view CT reconstruction when the noise level of projection data is less than 5%. ??ThheeAAuuthtohrosr.sP.uPbulibshliesdhebdy bEylsEevliseervBie.Vr .BT.hVis. is an open access article under the CC BY-NC-ND license SS(heetllteepcc:/tt/iicoornenaaatninvddepcpeoeemre-mrre-orvenievsw.ioerwugn/lduiecnrednreesrespsr/eobsnyps-ionbncil-sinitbydi/o4lif.t0yP/)ao.ufl PSacuhelrSrecrhIenrsrteitruItnstitut. Keywords: Neutron CT; Limited-view; SART; Sample boundary constraint
1. Introduction With sufficient projection views, the attenuation coefficient matrix of the sample can be reconstructed with the
filtered back-projection (FBP) algorithm fast and precisely; however, in many practical situations, such as industrial non-destructive te
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