Statistically Segregated k-Space Sampling for Accelerating Multiple-Acquisition MRI

TL;DR

This paper introduces a statistically-segregated k-space sampling method for multi-acquisition MRI that adaptively minimizes overlap across patterns, enhancing reconstruction quality and scan efficiency.

Contribution

It proposes a novel sampling strategy that sequentially generates multiple patterns with reduced overlap, improving multi-acquisition MRI reconstructions.

Findings

Significantly improved reconstruction quality in simulations.

Enhanced scan efficiency with reduced k-space overlap.

Effective in both Fourier and compressed-sensing MRI reconstructions.

Abstract

A central limitation of multiple-acquisition magnetic resonance imaging (MRI) is the degradation in scan efficiency as the number of distinct datasets grows. Sparse recovery techniques can alleviate this limitation via randomly undersampled acquisitions. A frequent sampling strategy is to prescribe for each acquisition a different random pattern drawn from a common sampling density. However, naive random patterns often contain gaps or clusters across the acquisition dimension that in turn can degrade reconstruction quality or reduce scan efficiency. To address this problem, a statistically-segregated sampling method is proposed for multiple-acquisition MRI. This method generates multiple patterns sequentially, while adaptively modifying the sampling density to minimize k-space overlap across patterns. As a result, it improves incoherence across acquisitions while still maintaining similar sampling density across the radial dimension of k-space. Comprehensive simulations and in vivo results are presented for phase-cycled balanced steady-state free precession and multi-echo T$_2$-weighted imaging. Segregated sampling achieves significantly improved quality in both Fourier and compressed-sensing reconstructions of multiple-acquisition datasets.