Efficient Regularized Field Map Estimation in 3D MRI

TL;DR

This paper introduces an efficient algorithm for 3D MRI field map estimation that handles phase wrapping, noise, and coil sensitivity, significantly reducing computational time compared to existing methods.

Contribution

The paper presents a preconditioned nonlinear conjugate gradient algorithm with incomplete Cholesky factorization for fast, regularized 3D MRI field map estimation, including multi-echo and water-fat imaging.

Findings

Demonstrates computational speedup over state-of-the-art methods

Handles phase wrapping and noise effectively in 3D MRI

Supports multi-echo and water-fat imaging scenarios

Abstract

Magnetic field inhomogeneity estimation is important in some types of magnetic resonance imaging (MRI), including field-corrected reconstruction for fast MRI with long readout times, and chemical shift based water-fat imaging. Regularized field map estimation methods that account for phase wrapping and noise involve nonconvex cost functions that require iterative algorithms. Most existing minimization techniques were computationally or memory intensive for 3D datasets, and are designed for single-coil MRI. This paper considers 3D MRI with optional consideration of coil sensitivity, and addresses the multi-echo field map estimation and water-fat imaging problem. Our efficient algorithm uses a preconditioned nonlinear conjugate gradient method based on an incomplete Cholesky factorization of the Hessian of the cost function, along with a monotonic line search. Numerical experiments show the computational advantage of the proposed algorithm over state-of-the-art methods with similar memory requirements.