Accelerated barrier optimization compressed sensing (ABOCS) for CT reconstruction with improved convergence.
Accelerated barrier optimization compressed sensing (ABOCS) for CT reconstruction with improved convergence.
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DOI：
10.1088/0031-9155/59/7/1801
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发表时间：
2014-04-07
影响因子：
3.5
通讯作者：
Zhu L
中科院分区：
文献类型：
--
作者：
Niu T;Ye X;Fruhauf Q;Petrongolo M;Zhu L
最近，我们提出了一种用于迭代CT重建的加速屏障优化压缩感知算法（ABOCS）。先前的ABOCS实现使用梯度投影（GP）和Barzilai-Borwein （BB）步长选择方案（GP-BB）来搜索最优解。该算法具有非单调性，不能稳定收敛。本文采用未知参数Nesterov （UPN）方法进一步改进ABOCS的收敛性，并研究ABOCS在临床患者数据上的重构性能。对计算机模拟重建、物理幻影重建和头颈部病人重建进行了对比研究。在所有这些研究中，使用UPN的ABOCS结果显示出比GPBB方法和最先进的bregman型方法更稳定和更快的收敛速度。从Shepp-Logan幻影的仿真研究中可以看出，UPN获得了与GPBB和bregman型方法相同的图像质量，但迭代次数分别减少了50%和90%。在Catphan©600幻影研究中，与全视图结果相比，使用17%投影（60视图）的UPN获得了相对重建误差（RRE）小于3%的高质量图像。在传统的滤波反投影（filter -backprojection， FBP）重建中，相同投影数据对应的RRE大于15%。在头颈部患者中进一步证明了ABOCS与UPN实现的优越性能。使用25%的投影（91个视图），该方法将RRE从FBP结果的21%降低到7.3%。总之，我们提出了ABOCS实施的UPN。与GPBB和bregman型方法相比，新方法具有稳定性高、迭代次数少的优点，显著提高了收敛性。
Recently, we proposed a new algorithm of accelerated barrier optimization compressed sensing (ABOCS) for iterative CT reconstruction. The previous implementation of ABOCS uses gradient projection (GP) with a Barzilai-Borwein (BB) step-size selection scheme (GP-BB) to search for the optimal solution. The algorithm does not converge stably due to its non-monotonic behavior. In this paper, we further improve the convergence of ABOCS using the unknown-parameter Nesterov (UPN) method and investigate the ABOCS reconstruction performance on clinical patient data. Comparison studies are carried out on reconstructions of computer simulation, a physical phantom and a head-and-neck patient. In all of these studies, the ABOCS results using UPN show more stable and faster convergence than those of the GPBB method and a state-of-the-art Bregman-type method. As shown in the simulation study of the Shepp-Logan phantom, UPN achieves the same image quality as those of GPBB and the Bregman-type method, but reduces the iteration numbers by up to 50% and 90%, respectively. In the Catphan©600 phantom study, a high-quality image with relative reconstruction error (RRE) less than 3% compared to the full-view result is obtained using UPN with 17% projections (60 views). In the conventional filtered-backprojection (FBP) reconstruction, the corresponding RRE is more than 15% on the same projection data. The superior performance of ABOCS with the UPN implementation is further demonstrated on the head-and-neck patient. Using 25% projections (91 views), the proposed method reduces the RRE from 21% as in the FBP results to 7.3%. In conclusion, we propose UPN for ABOCS implementation. As compared to GPBB and the Bregman-type methods, the new method significantly improves the convergence with higher stability and less iterations.
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