期刊
JOURNAL OF MAGNETIC RESONANCE
卷 197, 期 2, 页码 108-119出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jmr.2008.11.015
关键词
MR noise floor; Signal transformation; Rician; NonCentral Chi; Gaussian; MRI
资金
- Eunice Kennedy Shriver National Institute of Child Health and Human Development
- National Institute on Drug Abuse
- National Institute of Mental Health
- National Institute of Neurological Disorders and Stroke
- NIH Neuroscience Blueprint
A long-standing problem in magnetic resonance imaging (MRI) is the noise-induced bias in the magnitude signals. This problem is particularly pressing in diffusion MRI at high diffusion-weighting. In this paper, we present a three-stage scheme to solve this problem by transforming noisy nonCentral Chi signals to noisy Gaussian signals. A special case of nonCentral Chi distribution is the Rician distribution. In general, the Gaussian-distributed signals are of interest rather than the Gaussian-derived (e.g., Rayleigh, Rician, and nonCentral Chi) signals because the Gaussian-distributed signals are generally more amenable to statistical treatment through the principle of least squares. Monte Carlo simulations were used to validate the statistical properties of the proposed framework. This scheme opens up the possibility of investigating the low signal regime (or high diffusion-weighting regime in the case of diffusion MRI) that contains potentially important information about biophysical processes and structures of the brain. Published by Elsevier Inc.
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