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

TL;DR

This paper introduces a fast EM-based method for estimating diffusion tensors in MRI under Rician noise, improving accuracy and computational efficiency compared to existing techniques.

Contribution

It develops a novel EM algorithm for ML and MAP estimation in diffusion tensor imaging under Rician noise, with extensive validation on synthetic and real data.

Findings

The EM algorithm provides accurate tensor estimates under Rician noise.

The method outperforms existing techniques in speed and accuracy.

Both ML and MAP estimators show similar numerical properties.

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.