Deng Lu-zhen, Feng Peng, Chen Mian-yi, He Peng, Vo Quang-sang, Wei Biao
The Key Lab of Optoelectronic Technology and Systems of the Education Ministry of China, Chongqing University, Chongqing 400044, China.
Comput Math Methods Med. 2014;2014:753615. doi: 10.1155/2014/753615. Epub 2014 Jun 30.
Compressive sensing (CS) theory has great potential for reconstructing CT images from sparse-views projection data. Currently, total variation (TV-) based CT reconstruction method is a hot research point in medical CT field, which uses the gradient operator as the sparse representation approach during the iteration process. However, the images reconstructed by this method often suffer the smoothing problem; to improve the quality of reconstructed images, this paper proposed a hybrid reconstruction method combining TV and non-aliasing Contourlet transform (NACT) and using the Split-Bregman method to solve the optimization problem. Finally, the simulation results show that the proposed algorithm can reconstruct high-quality CT images from few-views projection using less iteration numbers, which is more effective in suppressing noise and artefacts than algebraic reconstruction technique (ART) and TV-based reconstruction method.
压缩感知（CS）理论在从稀疏视图投影数据重建CT图像方面具有巨大潜力。目前，基于全变差（TV）的CT重建方法是医学CT领域的一个研究热点，该方法在迭代过程中使用梯度算子作为稀疏表示方法。然而，用这种方法重建的图像常常存在平滑问题；为了提高重建图像的质量，本文提出了一种结合TV和非混叠Contourlet变换（NACT）的混合重建方法，并使用Split-Bregman方法来解决优化问题。最后，仿真结果表明，所提算法能够以较少的迭代次数从少视图投影中重建出高质量的CT图像，在抑制噪声和伪影方面比代数重建技术（ART）和基于TV的重建方法更有效。
