半覆盖锥束CT中扁平物体的高效反投影滤波重建

全覆盖圆轨迹扫描的成像视野受探测器宽度限制, 对于大物体的成像效率较低.半覆盖扫描可以将成像视野扩展近1倍, 图像重建首推使用反投影滤波型算法. 反投影滤波型算法按PI线重建, 各PI线积分区间的不一致性导致通信和计算消耗大, 影响重建效率. 针对半覆盖成像中扁平形状物体的重建问题, 提出了一种改进的反投影滤波型算法, 且证明了当扁平物体的厚度小于2Rsin(2π/N_p) (R为扫描半径, N_p 为圆扫描一周均匀采集的投影数量)时, PI线积分区间的不一致性在数值计算过程中的误差是可以忽略的. 改进后的算法相比原半覆盖反投影滤波算法具有两个明显的优势: 一是数值计算过程中角度循环移至PI线循环之外, 算法的通信需求显著降低; 二是投影数据求导、反投影和沿PI线滤波三个步骤均能够并行计算, 算法的并行性得到增强.数值仿真与实际数据的实验结果表明, 本文算法与原半覆盖反投影滤波算法的重建精度相当, 但计算效率提高了4.6倍. 关键词： 锥束CT 半覆盖成像 反投影滤波 PI线     

A modified interval subdividing based geometric calibration method for interior tomography

The interior tomography is commonly met in practice, whereas the self-calibration method for geometric parameters remains far from explored. To determine the geometry of interior tomography, a modified interval subdividing based method, which was originally developed by Tan et al.,[11]was presented in this paper. For the self-calibration method, it is necessary to obtain the reconstructed image with only geometric artifacts. Therefore, truncation artifacts reduction is a key problem for the self-calibration method of an interior tomography. In the method, an interior reconstruction algorithm instead of the Feldkamp–Davis–Kress(FDK) algorithm was employed for truncation artifact reduction. Moreover, the concept of a minimum interval was defined as the stop criterion of subdividing to ensure the geometric parameters are determined nicely. The results of numerical simulation demonstrated that our method could provide a solution to the selfcalibration for interior tomography while the original interval subdividing based method could not. Furthermore, real data experiment results showed that our method could significantly suppress geometric artifacts and obtain high quality images for interior tomography with less imaging cost and faster speed compared with the traditional geometric calibration method with a dedicated calibration phantom.