A linear phase evolution model for reduction of temporal unwrapping and field estimation errors in multi-echo GRE

TL;DR

This paper introduces a linear phase evolution model for multi-echo GRE that reduces unwrapping and field estimation errors, improving accuracy especially in high field gradient scenarios.

Contribution

It proposes a novel optimization-based linear phase model and applies Itoh's algorithm, significantly enhancing phase unwrapping and field estimation accuracy in multi-echo GRE imaging.

Findings

80% reduction in mean absolute error compared to weighted least-square regression

66% reduction compared to penalized maximum likelihood

Performance improves with higher field gradients and echo spacing

Abstract

This article aims at developing a model based optimization for reduction of temporal unwrapping and field estimation errors in multi-echo acquisition of Gradient Echo sequence. Using the assumption that the phase is linear along the temporal dimension, the field estimation is performed by application of unity rank approximation to the Hankel matrix formed using the complex exponential of the channel combined phase at each echo time. For the purpose of maintaining consistency with the observed complex data, the linear phase evolution model is formulated as an optimization problem with a cost function that involves a fidelity term and a unity rank prior, implemented using alternating minimization. Itoh s algorithm applied to the multi-echo phase estimated from this linear phase evolution model is able to reduce the unwrapping errors as compared to the unwrapping when directly applied to the measured phase. Secondly, the improved accuracy of the frequency fit in comparison to estimation using weighted least-square regression and penalized maximum likelihood is demonstrated using numerical simulation of field perturbation due to magnetic susceptibility effect. It is shown that the field can be estimated with 80 percent reduction in mean absolute error in comparison to wLSR and 66 percent reduction with respect to penalized maximum likelihood. The improvement in performance becomes more pronounced with increasing strengths of field gradient magnitudes and echo spacing.