Statistical deformable bone models for robust 3D surface extrapolation from sparse data
Statistical deformable bone models for robust 3D surface extrapolation from sparse data
复制标题
DOI：
10.1016/j.media.2006.05.001
复制
发表时间：
2007-04-01
影响因子：
10.9
通讯作者：
Ballester, Miguel A. Gonzalez
中科院分区：
文献类型：
--
作者：
Rajamani, Kumar T.;Styner, Martin A.;Ballester, Miguel A. Gonzalez
在计算机辅助骨科手术中，大多数术前计划和导航指导通常使用患者解剖的三维模型。这些模型提高了外科医生的能力，减少了手术过程的侵入性，提高了手术的准确性和安全性。一种常见的方法是使用计算机断层扫描（CT）或磁共振成像（MRI）。这些方法的缺点是昂贵和/或会对患者产生辐射。在本文中，我们提出了一种新的方法来构建患者特定的三维模型，提供适当的术中可视化，而无需术前或术中成像。通过将统计可变形模型拟合到最小的稀疏三维数据（由术中获得的数字化地标和表面点组成）来重建三维模型。利用主成分分析方法从训练对象中构建统计模型。我们的变形方案高效、准确地计算了可变形模型与三维数据的马氏距离加权最小二乘拟合。随着附加点的加入，放松马氏距离项使我们的方法能够有效地处理小型和大型数字化点集。将问题形式化为线性方程系统有助于我们向外科医生提供实时更新。结合基于m估计的数字化点加权使我们能够有效地拒绝异常值并计算稳定的模型。我们在这里提出了我们的评估结果，使用留一实验和扩展验证我们的方法在9干尸体骨头。(c) 2006 Elsevier B.V.版权所有
A majority of pre-operative planning and navigational guidance during computer assisted orthopaedic surgery routinely uses three-dimensional models of patient anatomy. These models enhance the surgeon's capability to decrease the invasiveness of surgical procedures and increase their accuracy and safety. A common approach for this is to use computed tomography (CT) or magnetic resonance imaging (MRI). These have the disadvantages that they are expensive and/or induce radiation to the patient. In this paper we propose a novel method to construct a patient-specific three-dimensional model that provides an appropriate intra-operative visualization without the need for a pre or intra-operative imaging. The 3D model is reconstructed by fitting a statistical deformable model to minimal sparse 3D data consisting of digitized landmarks and surface points that are obtained intra-operatively. The statistical model is constructed using Principal Component Analysis from training objects. Our deformation scheme efficiently and accurately computes a Mahalanobis distance weighted least square fit of the deformable model to the 3D data. Relaxing the Mahalanobis distance term as additional points are incorporated enables our method to handle small and large sets of digitized points efficiently. Formalizing the problem as a linear equation system helps us to provide real-time updates to the surgeons. Incorporation of M-estimator based weighting of the digitized points enables us to effectively reject outliers and compute stable models. We present here our evaluation results using leave-one-out experiments and extended validation of our method on nine dry cadaver bones. (c) 2006 Elsevier B.V. All rights reserved.