目的 针对2D/3D医学图像配准过程中数字影像重建技术(DRR)生成图像和相似性程度测量两个步骤计算量大、耗时较长这一问题,提出了一种基于Bresenham直线生成算法改进的模式强度与梯度相结合的混合配准算法.方法 首先利用Bresenham直线生成算法原理改进传统光线投射算法(Ray-Casting),完成DRR图像的生成;其次模式强度与梯度相结合并引入多分辨率策略来降低相似性测度的计算复杂度;最终利用改进的鲍威尔优化算法对参数进行优化,完成整个配准过程.结果 实验结果表明,改进的混合配准算法与基于相关系数、互信息和模式强度的配准算法相比,配准效率大幅提升.模拟配准实验和临床配准实验完成时间分别为76.2 s和64.9 s,比传统配准算法效率提升3~6倍.结论 提出的算法在保证配准精度和高鲁棒性的前提下,大幅度地提高了2D/3D医学图像配准算法的运算速度,可以满足临床上精确引导手术进行的实时性要求.

Objective To overcome existing problems in generating digitallg reconstructed radiongrapl (DRR) and to measure the similarity needed for large computations in the process of 2D/3D medical image registration, a hybrid registration algorithm was proposed. This algorithm combines pattern intensity with gradient on the basis of the Bresenham line generation algorithm. Method First, the space coordinate system, in which the position of the virtual point light source, 3D volume data, and projection panel can be determined, is established. Second, virtual point light source emits virtual light and the light passes through volume data. The DRR image can be obtained by computing the gray value of points projected by every virtual ray to the image panel via the ray-casting algorithm, which is an enhancement of the Bresenham line generation algorithm. Third, the registration components, including interpolator, space transformation component, optimizer, and pyramid filter, are instantiated. The pyramid filter is initialized such that the number of layers and shrinkage factor of each layer are set. Fourth, the reference image and floating image are processed separately into different resolution sequence images and image pyramids. The image resolution increases from top to bottom in the two pyramids. The arranged images in a pyramid correspond to those of another pyramid. The transformation parameters are calculated by the improved mode intensity measure, and the optimal transformation parameters are obtained by the improved Powell algorithm. The above process will be executed circularly until the image registration is complete. Result The experimental results show that compared with an algorithm based on correlation coefficient, mutual information, and pattern intensity, the proposed method shows a substantial increase in registration efficiency. The completion time of the simulated registration experiment and clinical registration experiment are 76.2 s and 64.9 s, respectively. The proposed method increases efficiency by 3~6 times compared with the traditional registration algorithm. Conclusion The hybrid algorithm not only has strong robustness and high clinical requirement accuracy but also significantly reduces the time required for registration.