Fast Estimation of Diffusion Tensors under Rician noise by the EM algorithm

This paper presents a fast computational method, the Expectation Maximization algorithm, for Maximum Likelihood (ML) estimation in diffusion tensor imaging under the Rice noise model. We further extend the ML framework to the maximum a posterior (MAP) estimation and describe the numerical similarities of both ML and MAP estimators. This novel method is implemented and applied using both synthetic and real data in a wide range of b amplitudes. The comparison with other popular methods are made in accuracy, methodology and computation.

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