Calibration-free B0 correction of EPI data using structured low rank matrix recovery

TL;DR

This paper presents a calibration-free method for correcting field inhomogeneity artifacts in EPI MRI data by leveraging structured low rank matrix recovery and exponential modeling of temporal profiles.

Contribution

It introduces a novel structured low rank algorithm that uses two EPI readouts and Toeplitz matrix completion for artifact correction without calibration.

Findings

Significant artifact reduction demonstrated on phantom and human data.

The method effectively estimates the signal subspace from undersampled data.

Potential application in dynamic MRI for time-varying field map correction.

Abstract

We introduce a structured low rank algorithm for the calibration-free compensation of field inhomogeneity artifacts in Echo Planar Imaging (EPI) MRI data. We acquire the data using two EPI readouts that differ in echo-time (TE). Using time segmentation, we reformulate the field inhomogeneity compensation problem as the recovery of an image time series from highly undersampled Fourier measurements. The temporal profile at each pixel is modeled as a single exponential, which is exploited to fill in the missing entries. We show that the exponential behavior at each pixel, along with the spatial smoothness of the exponential parameters, can be exploited to derive a 3D annihilation relation in the Fourier domain. This relation translates to a low rank property on a structured multi-fold Toeplitz matrix, whose entries correspond to the measured k-space samples. We introduce a fast two-step algorithm for the completion of the Toeplitz matrix from the available samples. In the first step, we estimate the null space vectors of the Toeplitz matrix using only its fully sampled rows. The null space is then used to estimate the signal subspace, which facilitates the efficient recovery of the time series of images. We finally demonstrate the proposed approach on spherical MR phantom data and human data and show that the artifacts are significantly reduced. The proposed approach could potentially be used to compensate for time varying field map variations in dynamic applications such as functional MRI.