Fast Estimation of Diffusion Tensors under Rician noise by the EM algorithm
Computation
2015-01-09 v1 Applications
Methodology
Abstract
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.
Keywords
Cite
@article{arxiv.1501.01898,
title = {Fast Estimation of Diffusion Tensors under Rician noise by the EM algorithm},
author = {Jia Liu and Dario Gasbarra and Juha Railavo},
journal= {arXiv preprint arXiv:1501.01898},
year = {2015}
}