[Free trajectory cone beam computed tomography reconstruction method for synchronous scanning of geometric calibration phantom and imaging object]

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2021 Oct 25;38(5):951-959. doi: 10.7507/1001-5515.202101066.
[Article in Chinese]

Abstract

In order to suppress the geometrical artifacts caused by random jitter in ray source scanning, and to achieve flexible ray source scanning trajectory and meet the requirements of task-driven scanning imaging, a method of free trajectory cone-beam computed tomography (CBCT) reconstruction is proposed in this paper. This method proposed a geometric calibration method of two-dimensional plane. Based on this method, the geometric calibration phantom and the imaging object could be simultaneously imaged. Then, the geometric parameters could be obtained by online calibration method, and then combined with the geometric parameters, the alternating direction multiplier method (ADMM) was used for image iterative reconstruction. Experimental results showed that this method obtained high quality reconstruction image with high contrast and clear feature edge. The root mean square errors (RMSE) of the simulation results were rather small, and the structural similarity (SSIM) values were all above 0.99. The experimental results showed that it had lower image information entropy (IE) and higher contrast noise ratio (CNR). This method provides some practical value for CBCT to realize trajectory freedom and obtain high quality reconstructed image.

为抑制因射线源扫描时随机抖动造成的几何伪影,并且实现射线源扫描轨迹灵活,满足任务驱动扫描成像要求,本文提出了一种自由轨迹锥束计算机断层扫描(CBCT)成像的方法。本方法提出二维平面的几何标定方法,基于该方法可以实现几何标定模体和成像物体同时成像,进而通过在线标定方法求出几何参数,再结合几何参数应用于交替方向乘子法(ADMM)进行图像迭代重建。实验表明,本方法可以获得较高质量的重建图像,图像对比度较高,特征边缘清晰。模拟实验结果均方根误差(RMSE)较小,结构相似性(SSIM)值均在 0.99 以上。实际实验结果有着较低的图像信息熵(IE)值和较高的对比度噪声比(CNR)值。该方法为 CBCT 实现轨迹自由以及获得高质量重建图像提供了一定的实用价值。.

Keywords: cone-beam computed tomography; free trajectory; geometric calibration; iterative reconstruction.

MeSH terms

  • Algorithms*
  • Calibration
  • Cone-Beam Computed Tomography
  • Image Processing, Computer-Assisted*
  • Phantoms, Imaging

Grants and funding

国家重点研发课题(2016YFA0202003,2019YFC0121901);国家自然科学基金(61971463);广州市科技计划项目(202002030385);广州市珠江新星(201906010013,201906010014)