McLaughlin Robert A, Hipwell John, Hawkes David J, Noble J Alison, Byrne James V, Cox Tim C
Wolfson Medical Vision Laboratory, Department of Engineering Science, University of Oxford, Oxford OX2 0BU, UK.
IEEE Trans Med Imaging. 2005 Aug;24(8):1058-66. doi: 10.1109/TMI.2005.852067.
Two-dimensional (2-D)-to-three-dimensional (3-D) registration can improve visualization which may aid minimally invasive neurointerventions. Using clinical and phantom studies, two state-of-the-art approaches to rigid registration are compared quantitatively: an intensity-based algorithm using the gradient difference similarity measure; and an iterative closest point (ICP)-based algorithm. The gradient difference approach was found to be more accurate, with an average registration accuracy of 1.7 mm for clinical data, compared to the ICP-based algorithm with an average accuracy of 2.8 mm. In phantom studies, the ICP-based algorithm proved more reliable, but with more complicated clinical data, the gradient difference algorithm was more robust. Average computation time for the ICP-based algorithm was 20 s per registration, compared with 14 min and 50 s for the gradient difference algorithm.
二维（2-D）到三维（3-D）配准可以改善可视化效果，这可能有助于微创神经介入手术。通过临床研究和模型研究，对两种最先进的刚性配准方法进行了定量比较：一种基于强度的算法，使用梯度差异相似性度量；另一种基于迭代最近点（ICP）的算法。结果发现，梯度差异方法更准确，临床数据的平均配准精度为1.7毫米，而基于ICP的算法平均精度为2.8毫米。在模型研究中，基于ICP的算法更可靠，但对于更复杂的临床数据，梯度差异算法更稳健。基于ICP的算法每次配准的平均计算时间为20秒，而梯度差异算法为14分50秒。
