不完全投影图像重建的压缩感知算法

doi:10.3969/j.issn.1001-2400.2015.04.016

Abstract

Abstract:

Aiming at the problem of the long scanning time of the classical scanning modalities in X-ray luminescence computed tomography and based on the traditional total variation minimization (TVM) algorithm, an improved compressive sensing (CS) algorithm for image reconstruction under incomplete projection situations is suggested and investigated. The algebraic reconstruction technique (ART) process under the non-negative constraint and the total variation minimization process solved by the gradient descent method are combined in the improved algorithm. To solve the constrained optimization problem, the ART process is performed several times first and then alternates with the total variation minimization process. The CS reconstructions under two incomplete projection situations, few-view projection and limited-angle projection, are discussed, and the effects of the adjustment factor and the iterative number in the total variation minimization process on the performance are investigated. The effectiveness of the proposed method is demonstrated by the simulation experiments based on the Shepp-Logan head phantom.

Key words: image reconstruction, incomplete projection, compressive sensing, total variation minimization

KUANG Tao;HUANG Liyu;ZHONG Yufang;LI Chao. Improved compressive sensing algorithm for CT image reconstruction with incomplete projection data[J].J4, 2015, 42(4): 95-99+113.

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Improved compressive sensing algorithm for CT image reconstruction with incomplete projection data

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