Yap Pew-Thian, An Hongyu, Chen Yasheng, Shen Dinggang
IEEE Trans Med Imaging. 2014 Aug;33(8):1627-40. doi: 10.1109/TMI.2014.2320947. Epub 2014 Apr 29.
In this paper, we propose a new bootstrap scheme, called the nonlocal bootstrap (NLB) for uncertainty estimation. In contrast to the residual bootstrap, which relies on a data model, or the repetition bootstrap, which requires repeated signal measurements, NLB is not restricted by the data structure imposed by a data model and obviates the need for time-consuming multiple acquisitions. NLB hinges on the observation that local imaging information recurs in an image. This self-similarity implies that imaging information coming from spatially distant (nonlocal) regions can be exploited for more effective estimation of statistics of interest. Evaluations using in silico data indicate that NLB produces distribution estimates that are in closer agreement with those generated using Monte Carlo simulations, compared with the conventional residual bootstrap. Evaluations using in vivo data demonstrate that NLB produces results that are in agreement with our knowledge on white matter architecture.
在本文中，我们提出了一种新的自举方案，称为非局部自举（NLB），用于不确定性估计。与依赖数据模型的残差自举或需要重复信号测量的重复自举不同，NLB不受数据模型所施加的数据结构的限制，并且无需进行耗时的多次采集。NLB基于这样的观察：局部成像信息会在图像中重复出现。这种自相似性意味着来自空间上遥远（非局部）区域的成像信息可用于更有效地估计感兴趣的统计量。使用计算机模拟数据进行的评估表明，与传统的残差自举相比，NLB产生的分布估计与使用蒙特卡罗模拟生成的估计更接近。使用体内数据进行的评估表明，NLB产生的结果与我们对白质结构的认识一致。
