This novel approach specifically addresses the joint likelihood of all observations given a tensor model (as opposed to the marginal likelihood of each ...
This paper presents a novel maximum likelihood approach to tensor estimation, denoted Diffusion Tensor Estimation by Maximizing Rician Likelihood (DTEMRL). In ...
Diffusion tensor imaging (DTI) is widely used to char- acterize white matter in health and disease. Previous ap- proaches to the estimation of diffusion ...
This paper presents a novel maximum likelihood approach to tensor estimation, denoted Diffusion Tensor Estimation by Maximizing Rician Likelihood (DTEMRL).
Introduction: Diffusion tensor imaging (DTI) is widely used to characterize white matter in health and disease. Previous approaches to the estimation of ...
Jan 15, 2016 · In this paper, we present a fast computational method for maximum likelihood estimation ... maximization at the Rician log-likelihood Qr. Detailed ...
Missing: Maximizing | Show results with:Maximizing
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An analytically exact method is proposed to extract the signal intensity and the noise variance simultaneously from noisy magnitude MR signals.
“Diffusion Tensor Estimation by Maximizing Rician Likelihood”, In Proceedings of the 2007 International Conference on Computer Vision Workshop ...
Constrained maximum likelihood estimation of the diffusion kurtosis ...
pubmed.ncbi.nlm.nih.gov › ...
Mar 17, 2011 · To guarantee physical relevance, we here suggest to estimate both diffusional tensors by maximizing the joint likelihood function of all Rician ...