Jain Ameet, Fichtinger Gabor
Department of Computer Science, Johns Hopkins University, USA.
Med Image Comput Comput Assist Interv. 2006;9(Pt 1):494-502. doi: 10.1007/11866565_61.
For quantitative C-arm fluoroscopy, we have developed a unified mathematical framework to tackle the issues of intra-operative calibration, pose estimation, correspondence and reconstruction, without the use of optical/electromagnetic trackers or precision-made fiducial fixtures. Our method uses randomly distributed unknown points in the imaging volume, either naturally present or induced by randomly sticking beads or other simple markers in the image pace. After these points are segmented, a high dimensional non-linear optimization computes all unknown parameters for calibration, C-arm pose, correspondence and reconstruction. Preliminary phantom experiments indicate an average C-arm tracking accuracy of 0.9 degrees and a 3D reconstruction error of 0.8 mm, with an 80 region of convergence for both the AP and lateral axes. The method appears to be sufficiently accurate for many clinical applications, and appealing since it works without any external instrumentation and does not interfere with the workspace.
对于定量C型臂荧光透视,我们开发了一个统一的数学框架,以解决术中校准、位姿估计、对应关系和重建问题,无需使用光学/电磁跟踪器或精密制造的基准固定装置。我们的方法使用成像体积中随机分布的未知点,这些点可以是自然存在的,也可以是通过在图像空间中随机粘贴珠子或其他简单标记物诱导产生的。在对这些点进行分割后,通过高维非线性优化来计算校准、C型臂位姿、对应关系和重建的所有未知参数。初步的体模实验表明,C型臂的平均跟踪精度为0.9度,三维重建误差为0.8毫米,前后位和侧位轴的收敛区域均为80。该方法对于许多临床应用似乎足够精确,并且很有吸引力,因为它无需任何外部仪器即可工作,且不会干扰工作空间。