کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5406933 | 1393195 | 2009 | 12 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A signal transformational framework for breaking the noise floor and its applications in MRI
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موضوعات مرتبط
مهندسی و علوم پایه
شیمی
شیمی تئوریک و عملی
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چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Magnetic Resonance - Volume 197, Issue 2, April 2009, Pages 108-119
Journal: Journal of Magnetic Resonance - Volume 197, Issue 2, April 2009, Pages 108-119
نویسندگان
Cheng Guan Koay, Evren Ãzarslan, Peter J. Basser,