Bhagalia Roshni, Fessler Jeffrey A, Kim Boklye
Department of Electrical Engineering and ComputerScience, University of Michigan, Ann Arbor, MI 48109, USA.
IEEE Trans Med Imaging. 2009 Aug;28(8):1208-16. doi: 10.1109/TMI.2009.2013136. Epub 2009 Feb 10.
Nonrigid image registration methods using intensity-based similarity metrics are becoming increasingly common tools to estimate many types of deformations. Nonrigid warps can be very flexible with a large number of parameters and gradient optimization schemes are widely used to estimate them. However, for large datasets, the computation of the gradient of the similarity metric with respect to these many parameters becomes very time consuming. Using a small random subset of image voxels to approximate the gradient can reduce computation time. This work focuses on the use of importance sampling to reduce the variance of this gradient approximation. The proposed importance sampling framework is based on an edge-dependent adaptive sampling distribution designed for use with intensity-based registration algorithms. We compare the performance of registration based on stochastic approximations with and without importance sampling to that using deterministic gradient descent. Empirical results, on simulated magnetic resonance brain data and real computed tomography inhale-exhale lung data from eight subjects, show that a combination of stochastic approximation methods and importance sampling accelerates the registration process while preserving accuracy.
使用基于强度的相似性度量的非刚性图像配准方法正日益成为估计多种类型变形的常用工具。非刚性变形可以通过大量参数实现非常灵活的变换，并且梯度优化方案被广泛用于估计这些参数。然而，对于大型数据集，针对这些众多参数计算相似性度量的梯度变得非常耗时。使用图像体素的小随机子集来近似梯度可以减少计算时间。这项工作专注于使用重要性采样来减少这种梯度近似的方差。所提出的重要性采样框架基于一种为基于强度的配准算法设计的与边缘相关的自适应采样分布。我们将基于有和没有重要性采样的随机近似的配准性能与使用确定性梯度下降的配准性能进行比较。对来自八名受试者的模拟磁共振脑数据和真实计算机断层扫描吸气 - 呼气肺部数据的实证结果表明，随机近似方法和重要性采样的组合在保持准确性的同时加速了配准过程。
