基于模型的非刚性配准问题的混合公式,以提高准确性和鲁棒性。
Hybrid formulation of the model-based non-rigid registration problem to improve accuracy and robustness.
作者信息
Clatz Olivier, Delingette Hervé, Talos Ion-Florin, Golby Alexandra J, Kikinis Ron, Jolesz Ferenc A, Ayache Nicholas, Warfield Simon K
机构信息
Epidaure Research Project, INRIA Sophia Antipolis, France.
出版信息
Med Image Comput Comput Assist Interv. 2005;8(Pt 2):295-302. doi: 10.1007/11566489_37.
We present a new algorithm to register 3D pre-operative Magnetic Resonance (MR) images with intra-operative MR images of the brain. This algorithm relies on a robust estimation of the deformation from a sparse set of measured displacements. We propose a new framework to compute iteratively the displacement field starting from an approximation formulation (minimizing the sum of a regularization term and a data error term) and converging toward an interpolation formulation (least square minimization of the data error term). The robustness of the algorithm is achieved through the introduction of an outliers rejection step in this gradual registration process. We ensure the validity of the deformation by the use of a biomechanical model of the brain specific to the patient, discretized with the finite element method. The algorithm has been tested on six cases of brain tumor resection, presenting a brain shift up to 13 mm.
我们提出了一种新算法,用于将三维术前磁共振(MR)图像与大脑术中MR图像进行配准。该算法依赖于从一组稀疏的测量位移中对变形进行稳健估计。我们提出了一个新框架,从一个近似公式(最小化正则化项和数据误差项的总和)开始迭代计算位移场,并朝着插值公式(数据误差项的最小二乘最小化)收敛。该算法的稳健性是通过在这个渐进配准过程中引入一个异常值剔除步骤来实现的。我们通过使用针对患者的大脑生物力学模型(用有限元方法离散化)来确保变形的有效性。该算法已在6例脑肿瘤切除病例上进行了测试,脑移位高达13毫米。