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CT image reconstruction of half-covered projection with fewer iterations based on SB-TVM

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There are situations in industrial CT examination where the whole cross-section of the scanned object may not be completely covered in the X-ray field area of the field of view (FOV). A FOV half-covered projection CT iterative reconstruction is proposed to meet the need of detecting larger-sized objects with a relatively smaller-sized detector to the FOV of CT scanning. In order to solve this problem under the condition of incomplete projection CT image reconstruction, and based on the compressive sensing (CS) technology, the regular optimisation reconstruction model of CT images, which is under the L1-norm and TV constraint at the same time, is introduced and the SB-TVM algorithm is proposed as a solution. The numerical simulation of the Shepp-Logan image results show that for half-covered sparse projection CT image reconstruction, the proposed SB-TVM algorithm has advantages for better reconstruction quality with fewer iterations when compared with that of the existing ART algorithm and L1-norm constraint reconstruction algorithm. The experiment results also show that the reconstruction time for SB-TVM is much longer than the corresponding ones of ART and split Bregman. The proposed iteration method of half-covered CT image reconstruction and the SB-TVM algorithm has certain theoretical significance and practical reference value.
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Document Type: Research Article

Publication date: July 1, 2016

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