Least-Squares FIR Models of Low-Resolution MR data for Efficient Phase-Error Compensation with Simultaneous Artefact Removal

TL;DR

This paper introduces a least-squares FIR modeling approach for low-resolution MR data that efficiently compensates phase errors and removes artifacts, outperforming existing methods with fewer fractional k-space lines.

Contribution

It presents a novel signal space model and FIR filter estimation technique that improves phase-error correction and artifact removal in low-resolution MRI data.

Findings

More efficient phase correction with half the fractional lines.

Robust artifact-free reconstruction of low-resolution MR images.

Effective noise filtering without highpass filtering.

Abstract

Signal space models in both phase-encode, and frequency-encode directions are presented for extrapolation of 2D partial kspace. Using the boxcar representation of low-resolution spatial data, and a geometrical representation of signal space vectors in both positive and negative phase-encode directions, a robust predictor is constructed using a series of signal space projections. Compared to some of the existing phase-correction methods that require acquisition of a pre-determined set of fractional kspace lines, the proposed predictor is found to be more efficient, due to its capability of exhibiting an equivalent degree of performance using only half the number of fractional lines. Robust filtering of noisy data is achieved using a second signal space model in the frequency-encode direction, bypassing the requirement of a prior highpass filtering operation. The signal space is constructed from Fourier Transformed samples of each row in the low-resolution image. A set of FIR filters are estimated by fitting a least squares model to this signal space. Partial kspace extrapolation using the FIR filters is shown to result in artifact-free reconstruction, particularly in respect of Gibbs ringing and streaking type artifacts.