Wang Linyuan, Cai Ailong, Zhang Hanming, Yan Bin, Li Lei, Hu Guoen, Bao Shanglian
National Digital Switching System Engineering and Technological Research Center, Zhengzhou, Henan, China.
The Beijing City Key Lab of Medical Physics and Engineering, Peking University, Beijing, China.
J Xray Sci Technol. 2015;23(1):83-99. doi: 10.3233/XST-140472.
With the development of compressive sensing theory, image reconstruction from few-view projections has been paid considerable research attention in the field of computed tomography (CT). Total variation (TV)-based CT image reconstruction has been shown experimentally to be capable of producing accurate reconstructions from sparse-view data. Motivated by the need of solving few-view reconstruction problem with large scale data, a general block distribution reconstruction algorithm based on TV minimization and the alternating direction method (ADM) has been developed in this study. By utilizing the inexact ADM, which involves linearization and proximal point techniques, the algorithm is relatively simple and hence convenient for the derivation and distributed implementation. And because the data as well as the computation are distributed to individual nodes, an outstanding acceleration factor is achieved. Experimental results demonstrate that the proposed method can accelerate the alternating direction total variation minimization (ADTVM) algorithm with nearly no loss of accuracy, which means compared with ADTVM, the proposed algorithm has a better accuracy with same running time.
随着压缩感知理论的发展，基于少视图投影的图像重建在计算机断层扫描（CT）领域受到了广泛的研究关注。基于总变分（TV）的CT图像重建已通过实验证明能够从稀疏视图数据中生成准确的重建结果。受解决大规模数据少视图重建问题需求的推动，本研究开发了一种基于TV最小化和交替方向法（ADM）的通用块分布重建算法。通过使用涉及线性化和近端点技术的不精确ADM，该算法相对简单，便于推导和分布式实现。并且由于数据和计算都分布到各个节点，实现了显著的加速因子。实验结果表明，所提出的方法能够在几乎不损失精度的情况下加速交替方向总变分最小化（ADTVM）算法，这意味着与ADTVM相比，所提出的算法在相同运行时间下具有更好的精度。
