Simple Auto-Calibrated Gradient Delay Estimation From Few Spokes Using Radial Intersections (RING)

TL;DR

The paper introduces RING, a simple and robust method for estimating gradient delays in highly undersampled radial MRI data using few spokes, improving artifact correction.

Contribution

The novel RING method accurately estimates gradient delays from minimal radial spokes, enhancing undersampled radial imaging correction techniques.

Findings

RING accurately estimates gradient delays with as few as three spokes.

It outperforms correlation-based methods in robustness and simplicity.

Validated across simulations, phantom, and in vivo data.

Abstract

Purpose: To develop a simple and robust tool for the estimation of gradient delays from highly undersampled radial k-space data. Theory: In radial imaging gradient delays induce parallel and orthogonal trajectory shifts, which can be described using an ellipse model. The intersection points of the radial spokes, which can be estimated by spoke-by-spoke comparison of k-space samples, distinctly determine the parameters of the ellipse. Using the proposed method (RING), these parameters can be obtained using a least-squares fit and utilized for the correction of gradient delays. Methods: The functionality and accuracy of the proposed RING method is validated and compared to correlation-based gradient-delay estimation from opposing spokes using numerical simulations, phantom and in vivo heart measurements. Results: In all experiments, RING robustly provides accurate gradient delay estimations even for as few as three radial spokes. Conclusion: The simple and straightforward to implement RING method provides accurate gradient delay estimation for highly undersampled radial imaging. Keywords: trajectory correction, radial imaging, gradient delay, artifacts, system imperfections, RING