Uncertainty estimation in diffusion MRI using the nonlocal bootstrap

Pew Thian Yap, Hongyu An, Yasheng Chen, Dinggang Shen

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)


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.

Original languageEnglish
Article number6807726
Pages (from-to)1627-1640
Number of pages14
JournalIEEE Transactions on Medical Imaging
Issue number8
Publication statusPublished - 2014 Aug


  • Bootstrap
  • diffusion magnetic resonance imaging (MRI)
  • estimator
  • nonlocal means
  • nonparametric kernel regression
  • sampling distribution
  • self-similarity
  • uncertainty

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering


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