Work

Uncertainty Estimation in Diffusion MRI Using the Nonlocal Bootstrap

Year: 2014

Type: article

Abstract: 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.

Source: IEEE Transactions on Medical Imaging

DOI: 10.1109/tmi.2014.2320947

Authors: Pew-Thian Yap, Hongyu An, Yasheng Chen, Dinggang Shen

Institutions: University of North Carolina at Chapel Hill, Korea University

Language: English

Topic: Advanced Neuroimaging Techniques and Applications

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

Open Access status: green

Funders: National Institute on Aging, National Institute of Mental Health, National Institute of Biomedical Imaging and Bioengineering