X-ray 2D和CT 3D图像配准流程和算法总结 Fluoro-image and CT 3D image registration flowwork and algorithm

X-ray 2D和CT 3D图像配准流程和算法总结 Fluoro-image and CT 3D image registration flowwork and algorithm

Reference to: Registration of 2D C-Arm and 3D CT Images for a C-Arm Image-Assisted Navigation System for Spinal Surgery

之间已经讲过的内容包括相机的校准,transformation matrix转换矩阵的应用(空间坐标系和平面图像坐标系的传递)。那么基于上面的一些算法,再结合图像融合配准的算法,我们可以做些什么实际的应用呢?下面我将结合一个X-ray和CT图像的融合辅助手术导航的实例,帮助扩展一下思维。

X-ray和CT图像的融合配准分为下面几个步骤:

  1. CT 图像的三维重建,因为拿到了三维图像后,才可以更好的对应映射到X-ray二维的某一张图片上
  2. 接下来,通过C-arm可以获取到有效的 AP(anterposteria)和 LT(lateral)两个位置上的 X-ray 2D 图像,因为通过两张图片才能获取人体在空间坐标系中的坐标关系
  3. CT 3D image DDR image generation DRR可以生成有效的投影灰度图像,用于配准X-ray image
  4. 生成图像遮罩去遮盖 dynamic reference frame (DRF) of 2D X-ray 图像,这样确保了两个图像校准的准确定,以免DRF的一些特征去印象了校准的精度
  5. CT DRR 和 X-ray Fluoro 图像的配准
  6. Error measurement测试算法
    【fig1】

X-ray 2D和CT 3D图像配准流程和算法总结 Fluoro-image and CT 3D image registration flowwork and algorithm_第1张图片
在六个过程中分别由对应的算法应用,总结如下:

Index Algorithm
1 marching cube algorithm (W. E. Lorensen and H. E. Cline, “Marching cubes: a high resolution 3D surface construction algorithm,” Computer Graphics, vol. 21, no. 4, pp. 163–169, 1987.)
2 ray-casting algorithm hardware configuration: NVIDIA CUBA (GTX570) with 480 CUBA process
3 calculated from two X-ray images
4 region growth algorithm (RGA)
5 graidient-based Powell’s method; geometric-based downhill simplex algorithm; probabilistic-based genetic algorithm (P. Markelj, D. Tomaˇzeviˇc, B. Likar, and F. Pernuˇs, “A review of 3D/2D registration methods for image-guided interventions,” Medical Image Analysis, vol. 16, no. 3, pp. 642–661, 2012.) (Y. Kim, K.-I. Kim, J. H. Choi, and K. Lee, “Novel methods for 3D postoperative analysis of total knee arthroplasty using 2D- 3D image registration,” Clinical Biomechanics, vol. 26, no. 4, pp. 384–391, 2011.)
6 normalized cross correlation (NCC); Gradient correlation (GC); pattern intensity (PI); Gradient difference correlation (GDC); mutual information (MI) (G. P. Penney, J. Weese, J. A. Little, P.Desmedt,D. L.G.Hill, and D. J. Hawkes, “A comparison of similarity measures for use in 2-D-3-D medical image registration,” IEEE Transactions on Medical Imaging, vol. 17, no. 4, pp. 586–595, 1998.)

Marching cube algorithm
这个是个比较传统的3D建模算法,这是个标准的算法,网上有算式和算例,有兴趣可以去搜索一下,可以看到椭圆小球是用无数三角形拼接而成,每个边缘三角形都是defined block和实体的相交线构成
X-ray 2D和CT 3D图像配准流程和算法总结 Fluoro-image and CT 3D image registration flowwork and algorithm_第2张图片

Initial coordinate matching
用Laplace算子找到边缘后,进行边缘中心的查找
对于传统CT图像,还需要对特征进行筛选,图像进行处理
X-ray 2D和CT 3D图像配准流程和算法总结 Fluoro-image and CT 3D image registration flowwork and algorithm_第3张图片

Growing region algorithm
这个也比较容易实现,O(4*P) P:pixel; 比较容易理解,如果图品的分辨率越高,需要查找的特征越大,肯定需要计算的时间越长
X-ray 2D和CT 3D图像配准流程和算法总结 Fluoro-image and CT 3D image registration flowwork and algorithm_第4张图片

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