Maximum Likelihood estimation of diffusion parameters with a Rician noise model
نویسنده
چکیده
Introduction When estimating the parameters of the diffusion tensor it is common to choose those that minimise the sum of squared differences between the observed data and the predictions made by the model. This is equivalent to a Maximum Likelihood (ML) estimate given an assumption of normal distributed errors. In reality the errors have a Rician distribution, but when the SNR is sufficiently high they are well approximated by a normal distribution. For poor SNR, which could be caused by scanning with high resolution or by using high b-values, it has been shown that diffusion is underestimated [1].
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