Meng Cai, Zhang Jun, Zhou Fugen, Wang Tianmiao
School of Astronautics, Beihang University, 37 Xueyuan Road, Beijing 100191, China.
School of Astronautics, Beihang University, 37 Xueyuan Road, Beijing 100191, China.
Comput Biol Med. 2014 Sep;52:49-56. doi: 10.1016/j.compbiomed.2014.06.009. Epub 2014 Jun 25.
Image distortion correction and geometric calibration are critical operations for using C-arm DSA (Digital Subtraction Angiography) images to digitally navigate vascular interventional surgery. In traditional ways, C-arm images are corrected with global or local correction methods where a supposed virtual ideal image is needed, and then the corrected images are utilized to calibrate the C-arm with a pin-hole model. In this paper, we propose a new method to calibrate the C-arm with a nonlinear model and to improve navigation performance. We first calibrate the C-arm with a nonlinear model and then the distortion correction is accomplished without virtual ideal image. In this paper, the nonlinear model of C-arm imaging system is addressed at first, and then the C-arm is calibrated with a two-stage method. In the first stage, the C-arm is calibrated with the markers in image center by RAC (radial alignment constraint) method, and in the second stage the calibration parameters are optimized with Levenberg-Marquadt algorithm by minimizing the sum of the square of difference between all markers׳ real distorted positions and their theoretical distorted positions in the phantom image. Based on the calibration result, the image distortion can be corrected. To verify our method, experiments were conducted with a conventional DSA C-arm machine in hospital. The errors in distortion correction and 3D (three-dimensional) reconstruction were quantitatively compared with the global polynomial correction method and visual model method, and the results showed that the proposed method had better performance in distortion correction and 3D reconstruction.
图像畸变校正和几何校准是使用C型臂数字减影血管造影（DSA）图像进行血管介入手术数字导航的关键操作。传统方法中，C型臂图像通过全局或局部校正方法进行校正，这种方法需要一个假定的虚拟理想图像，然后利用校正后的图像通过针孔模型对C型臂进行校准。在本文中，我们提出了一种使用非线性模型校准C型臂并提高导航性能的新方法。我们首先使用非线性模型校准C型臂，然后在无需虚拟理想图像的情况下完成畸变校正。本文首先阐述了C型臂成像系统的非线性模型，然后采用两阶段方法对C型臂进行校准。在第一阶段，通过径向对齐约束（RAC）方法使用图像中心的标记对C型臂进行校准，在第二阶段，通过最小化体模图像中所有标记的实际畸变位置与其理论畸变位置之间的差的平方和，使用列文伯格-马夸尔特算法对校准参数进行优化。基于校准结果，可以校正图像畸变。为验证我们的方法，在医院使用传统DSA C型臂机进行了实验。将畸变校正和三维（3D）重建中的误差与全局多项式校正方法和视觉模型方法进行了定量比较，结果表明所提出的方法在畸变校正和3D重建方面具有更好的性能。
