School of Electrical and Information Engineering, Tianjin University, Tianjin, China.
School of Information Engineering, Tianjin University of Commerce, Tianjin, China.
J Xray Sci Technol. 2019;27(2):273-285. doi: 10.3233/XST-180442.
In computed tomography (CT), a patient motion would result in degraded spatial resolution and image artifacts.
To eliminate motion artifacts, this study presents a method to estimate the motion parameters from sinograms based on extended difference function.
Based on our previous work, we first divide the projection data into two parts according to view angles and take Radon transform. Then, we calculate the extended difference functions and search for the minimum points. The relative displacements can be determined by the minimum points, and the motion can be estimated by the relationships between the relative displacements and motion parameters. Finally, we introduce the estimated parameters into the reconstruction process to compensate for the motion effects.
The simulation results show that the running times can reduce by about 30% than our previous work. In phantom experiments, the relative mean rotation excursion (RMRE) and relative mean translation excursion (RMTE) of the new method are lower than the conventional Helgason-Ludwig consistency condition (HLCC) based method and comparable to our previous work. Compare with the HLCC method, the root mean square error (RMSE) of the new method also reduces, while the Pearson correlation coefficient (CC) and mean structural similarity index (MSSIM) increase.
The proposed new method yields the improved performance on accuracy of motion estimation with higher computational efficiency, and thus it can produce high-quality images.
在计算机断层扫描(CT)中,患者运动会导致空间分辨率降低和图像伪影。
为了消除运动伪影,本研究提出了一种基于扩展差分函数从投影数据中估计运动参数的方法。
基于我们之前的工作,我们首先根据视场角将投影数据分为两部分,并进行 Radon 变换。然后,我们计算扩展差分函数并搜索最小值点。相对位移可以通过最小点确定,运动可以通过相对位移和运动参数之间的关系来估计。最后,我们将估计的参数引入到重建过程中,以补偿运动效应。
模拟结果表明,与我们之前的工作相比,运行时间可以减少约 30%。在体模实验中,新方法的相对平均旋转偏移(RMRE)和相对平均平移偏移(RMTE)均低于基于传统 Helgason-Ludwig 一致性条件(HLCC)的方法,与我们之前的工作相当。与 HLCC 方法相比,新方法的均方根误差(RMSE)也有所降低,而 Pearson 相关系数(CC)和平均结构相似性指数(MSSIM)增加。
该方法在准确性方面具有更高的计算效率,从而产生高质量的图像。