Article citations

    12. Melli S A, Wahid K A, Babyn P, et al. A compressed sensing based reconstruction algorithm for synchrotron source propagation-based X-ray phase contrast computed tomography. Nucl Instrum Methods Phys Res A, 2016, 806: 307-317.

has been cited by the following article:

  • TITLE: 基于正弦图平移匹配的投影旋转中心校正<br>Correction of the projection center of rotation based on the sinogram using translation matching method
  • AUTHORS: 赵琦,赵雨晴,丛长虹,冀东江,秦莉莉,陈晓冬,胡春红
  • KEYWORDS: X 射线计算机断层成像重建,正弦图,投影旋转中心,OTSU,L1 范数<br>computed tomography reconstruction,sinogram,center of rotation,OTSU,L1-norm
  • JOURNAL NAME: - DOI: 10.7507/1001-5515.201707065 Jun 09, 2019
  • ABSTRACT: 投影旋转中心(COR)精准定位是确保计算机断层成像(CT)重建图像质量的关键要素,经典的互相关匹配算法在投影角度为 0~180° 时难以满足高质量 CT 成像要求,需进行改进创新。本文根据正弦图上 0° 与 180° 投影数据翻转后有对应性的特点提出基于这两行数据平移匹配的 COR 校正算法,该算法利用 OTSU 进行阈值分割以减少背景噪声影响,通过 L1 范数量化 COR 最小偏移得到准确校正值后进行 CT 重建。分别采用加入随机梯度噪声和高斯噪声的 Sheep-Logan 模型和雄性 SD 大鼠样本的同质肝脏与异质牙齿图像验证新算法的有效性,并将新算法与互相关匹配算法做性能对比。结果表明:新算法运算量少、简便快速且具有良好的抗噪鲁棒性,校正精度高(稀疏采样投影数据在 10%~50% 时也能很好地校正 COR 值),CT 重建图像质量有显著改进,效果优于互相关算法。<br>The accurate position of the center of rotation (COR) is a key factor to ensure the quality of computed tomography (CT) reconstructed images. The classic cross-correlation matching algorithm can not satisfy the requirements of high-quality CT imaging when the projection angle is 0 and 180°, and thus needs to be improved and innovated. In this study, considering the symmetric characteristic of the 0° and flipped 180° projection data in sinogram, a novel COR correction algorithm based on the translation and match of the 0° and 180° projection data was proposed. The OTSU method was applied to reduce noise on the background, and the minimum offset of COR was quantified using the L1-norm, and then a precise COR was obtained for the image correction and reconstruction. The Sheep-Logan simulation model with random gradients and Gaussian noise and the real male SD rats samples which contained the heterogenous tooth image and the homogenous liver image, were adopted to verify the performance of the new algorithm and the cross-correlation matching algorithm. The results show that the proposed algorithm has better robustness and higher accuracy of the correction (when the sampled data is from 10% to 50% of the full projection data, the COR value can still be measured accurately using the proposed algorithm) with less computational burden compared with the cross-correlation matching algorithm, and it is able to significantly improve the quality of the reconstructed images.