Wu Weiwen, Shi Jun, Yu Hengyong, Wu Weifei, Vardhanabhuti Varut
Department of Diagnostic RadiologyThe University of Hong Kong Hong Kong China.
School of Communication and Information EngineeringShanghai Institute for Advanced Communication and Data Science, Shanghai University Shanghai 200444 China.
IEEE Trans Instrum Meas. 2021 Jan 19;70:4503012. doi: 10.1109/TIM.2021.3050190. eCollection 2021.
Methods to recover high-quality computed tomography (CT) images in low-dose cases will be of great benefit. To reach this goal, sparse-data subsampling is one of the common strategies to reduce radiation dose, which is attracting interest among the researchers in the CT community. Since analytic image reconstruction algorithms may lead to severe image artifacts, the iterative algorithms have been developed for reconstructing images from sparsely sampled projection data. In this study, we first develop a tensor gradient L-norm minimization (TGLM) for low-dose CT imaging. Then, the TGLM model is optimized by using the split-Bregman method. The Coronavirus Disease 2019 (COVID-19) has been sweeping the globe, and CT imaging has been deployed for detection and assessing the severity of the disease. Finally, we first apply our proposed TGLM method for COVID-19 to achieve low-dose scan by incorporating the 3-D spatial information. Two COVID-19 patients (64 years old female and 56 years old man) were scanned by the [Formula: see text]CT 528 system, and the acquired projections were retrieved to validate and evaluate the performance of the TGLM.
在低剂量情况下恢复高质量计算机断层扫描（CT）图像的方法将大有裨益。为实现这一目标，稀疏数据子采样是减少辐射剂量的常见策略之一，这引起了CT领域研究人员的兴趣。由于解析图像重建算法可能会导致严重的图像伪影，因此已经开发了迭代算法用于从稀疏采样的投影数据中重建图像。在本研究中，我们首先为低剂量CT成像开发了一种张量梯度L范数最小化（TGLM）方法。然后，使用分裂Bregman方法对TGLM模型进行优化。2019冠状病毒病（COVID-19）一直在全球蔓延，CT成像已被用于疾病的检测和严重程度评估。最后，我们首先将提出的TGLM方法应用于COVID-19，通过纳入三维空间信息实现低剂量扫描。两名COVID-19患者（64岁女性和56岁男性）由[公式：见正文]CT 528系统进行扫描，并获取投影数据以验证和评估TGLM的性能。
