Department of Radiology and Medical Imaging, University of Virginia Charlottesville, VA, USA.
Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania Philadelphia, PA, USA.
Front Neuroinform. 2013 Dec 23;7:39. doi: 10.3389/fninf.2013.00039. eCollection 2013.
Diffeomorphic mappings are central to image registration due largely to their topological properties and success in providing biologically plausible solutions to deformation and morphological estimation problems. Popular diffeomorphic image registration algorithms include those characterized by time-varying and constant velocity fields, and symmetrical considerations. Prior information in the form of regularization is used to enforce transform plausibility taking the form of physics-based constraints or through some approximation thereof, e.g., Gaussian smoothing of the vector fields [a la Thirion's Demons (Thirion, 1998)]. In the context of the original Demons' framework, the so-called directly manipulated free-form deformation (DMFFD) (Tustison et al., 2009) can be viewed as a smoothing alternative in which explicit regularization is achieved through fast B-spline approximation. This characterization can be used to provide B-spline "flavored" diffeomorphic image registration solutions with several advantages. Implementation is open source and available through the Insight Toolkit and our Advanced Normalization Tools (ANTs) repository. A thorough comparative evaluation with the well-known SyN algorithm (Avants et al., 2008), implemented within the same framework, and its B-spline analog is performed using open labeled brain data and open source evaluation tools.
变形容映射在图像配准中至关重要，主要是因为它们的拓扑性质以及在为变形和形态估计问题提供生物学上合理的解决方案方面的成功。流行的变形容像配准算法包括那些具有时变和恒定速度场的算法，以及对称考虑的算法。以正则化为形式的先验信息用于强制变换的合理性，其形式为基于物理的约束或通过某种近似，例如，向量场的高斯平滑[Thirion 的 Demons（Thirion，1998）]。在原始 Demons 框架的上下文中，可以将所谓的直接操作自由形式变形（DMFFD）（Tustison 等人，2009）视为一种平滑替代方法，其中通过快速 B 样条逼近来实现显式正则化。这种特征可以用于提供 B 样条“风味”的变形容像配准解决方案，具有几个优点。实现是开源的，可通过 Insight Toolkit 和我们的高级归一化工具（ANTs）存储库获得。使用开源标记的脑数据和开源评估工具，在相同的框架内与知名的 Syn 算法（Avants 等人，2008）进行了全面的比较评估，及其 B 样条模拟。
