Department of Liberal Arts, Hongik University, Sejong, Republic of Korea.
School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
PLoS One. 2019 Jan 11;14(1):e0210410. doi: 10.1371/journal.pone.0210410. eCollection 2019.
In this study, we investigate the feasibility of improving the imaging quality for low-dose multislice helical computed tomography (CT) via iterative reconstruction with tensor framelet (TF) regularization. TF based algorithm is a high-order generalization of isotropic total variation regularization. It is implemented on a GPU platform for a fast parallel algorithm of X-ray forward band backward projections, with the flying focal spot into account. The solution algorithm for image reconstruction is based on the alternating direction method of multipliers or the so-called split Bregman method. The proposed method is validated using the experimental data from a Siemens SOMATOM Definition 64-slice helical CT scanner, in comparison with FDK, the Katsevich and the total variation (TV) algorithm. To test the algorithm performance with low-dose data, ACR and Rando phantoms were scanned with different dosages and the data was equally undersampled with various factors. The proposed method is robust for the low-dose data with 25% undersampling factor. Quantitative metrics have demonstrated that the proposed algorithm achieves superior results over other existing methods.
在这项研究中，我们通过张量框架（TF）正则化的迭代重建来研究提高低剂量多层螺旋 CT（CT）成像质量的可行性。基于 TF 的算法是各向同性全变分正则化的高阶推广。它在 GPU 平台上实现了 X 射线正向带反向投影的快速并行算法，同时考虑了飞行焦点。图像重建的求解算法基于交替方向乘子法或所谓的分裂布格曼法。该方法使用来自西门子 SOMATOM Definition 64 层螺旋 CT 扫描仪的实验数据进行验证，与 FDK、Katsevich 和全变分（TV）算法进行了比较。为了用低剂量数据测试算法性能，使用不同剂量扫描了 ACR 和 Rando 体模，并以各种因子进行了等间隔欠采样。该方法对 25%欠采样因子的低剂量数据具有鲁棒性。定量指标表明，该算法优于其他现有方法。
