Suppr超能文献

基于测地线射击的 PDE 约束 LDDMM 与使用增量伴随雅可比方程的不精确 Gauss-Newton-Krylov 优化。

PDE-constrained LDDMM via geodesic shooting and inexact Gauss-Newton-Krylov optimization using the incremental adjoint Jacobi equations.

机构信息

Department of Computer Science, Aragon Institute on Engineering Research (I3A), University of Zaragoza, Zaragoza, Spain.

出版信息

Phys Med Biol. 2019 Jan 7;64(2):025002. doi: 10.1088/1361-6560/aaf598.

Abstract

The class of non-rigid registration methods proposed in the framework of PDE-constrained large deformation diffeomorphic metric mapping is a particularly interesting family of physically meaningful diffeomorphic registration methods. Inexact Gauss-Newton-Krylov optimization has shown an excellent numerical accuracy and an extraordinarily fast convergence rate in this framework. However, the Galerkin representation of the non-stationary velocity fields does not provide proper geodesic paths. In this work, we propose a method for PDE-constrained LDDMM parameterized in the space of initial velocity fields under the EPDiff equation. The derivation of the gradient and the Hessian-vector products are performed on the final velocity field and transported backward using the adjoint and the incremental adjoint Jacobi equations. This way, we avoid the complex dependence on the initial velocity field in the computations. We also avoid the computation of the adjoint equation and its incremental counterpart that has been recently identified as a subtle problem in PDE-constrained LDDMM. The proposed method provides geodesics in the framework of PDE-constrained LDDMM, and it shows performance competing with benchmark PDE-constrained LDDMM and EPDiff-LDDMM methods.

摘要

在 PDE 约束的大变形仿射度量映射框架中提出的非刚性配准方法类是一类特别有趣的具有物理意义的仿射配准方法。非精确的 Gauss-Newton-Krylov 优化在该框架中显示出了极好的数值精度和非常快的收敛速度。然而,非定常速度场的 Galerkin 表示并不能提供适当的测地线路径。在这项工作中,我们提出了一种在 EPDiff 方程下初始速度场空间中参数化的 PDE 约束 LDDMM 的方法。梯度和海森向量积的推导是在最终速度场中进行的,并使用伴随和增量伴随 Jacobi 方程向后传输。通过这种方式,我们避免了计算中对初始速度场的复杂依赖。我们还避免了最近在 PDE 约束 LDDMM 中被确定为一个微妙问题的伴随方程及其增量对应物的计算。所提出的方法在 PDE 约束 LDDMM 框架中提供了测地线,并且它的性能与基准 PDE 约束 LDDMM 和 EPDiff-LDDMM 方法相媲美。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验