SPARKLING: variable‐density k‐space filling curves for accelerated T₂^*‐weighted MRI

Abstract

Purpose To present a new optimition‐driven design of optimal k‐space trajectories in the context of compressed sensing: Spreading Projection Algorithm for Rapid K‐space sampLING (SPARKLING). TheoryThe SPARKLING algorithm is a versatile method inspired from stippling techniques that automatically generates optimized sampling patterns compatible with MR hardware constraints on maximum gradient amplitude and slew rate. These non‐Cartesian sampling curves are designed to comply with key criteria for optimal sampling: a controlled distribution of samples (e.g., variable density) and a locally uniform k‐space coverage. MethodsEx vivo and in vivo prospective T2*‐weighted acquisitions were performed on a 7‐Tesla scanner using the SPARKLING trajectories for various setups and target densities. Our method was compared to radial and variable‐density spiral trajectories for high‐resolution imaging. ResultsCombining sampling efficiency with compressed sensing, the proposed sampling patterns allowed up to 20‐fold reductions in MR scan time (compared to fully sampled Cartesian acquisitions) for two‐dimensional T2*‐weighted imaging without deterioration of image quality, as demonstrated by our experimental results at 7 Tesla on in vivo human brains for a high in‐plane resolution of 390 μm. In comparison to existing non‐Cartesian sampling strategies, the proposed technique also yielded superior image quality. ConclusionsThe proposed optimization‐driven design of k‐space trajectories is a versatile framework that is able to enhance MR sampling performance in the context of compressed sensing.

Perturbed spiral real‐time phase‐contrast MR with compressive sensing reconstruction for assessment of flow in children

“…This is because most MR systems cannot produce the sharp, nonsmooth changes in gradient moments needed for true random sampling. Several studies have been undertaken to identify MR data sampling patterns that are conducive to CS . It should be noted that gradient waveform design optimization for more complex trajectories has been done.…”

“…Several studies have been undertaken to identify MR data sampling patterns that are conducive to CS. 17,[32][33][34][35][36][37][38] It should be noted that gradient waveform design optimization for more complex trajectories has been done. However, these can be time-consuming algorithms with runtimes measured in minutes.…”

Correcting the Side Effects of ADC Filtering in MR Image Reconstruction

“…The proposed acquisition model and reconstruction schemes were tested on experimental data acquired from both spiral [27] and Sparkling [7,12,26] sampling patterns. In both cases, the forward operator of Section 4 is based on the filter estimation shown in Fig.…”

“…The recently introduced Sparkling trajectories are novel non-Cartesian trajectories that produce optimal variabledenisty sampling patterns by taking full advantage of the hardware capacity [7,12,26]. The trajectory used in our experiments consisted of 128 shots composed of 512 samples each for a target resolution of 512 × 512, corresponding to a subsampling factor of 4 which was chosen based on an empirical study of the maximum acceleration factors in MRI [25].…”

Online MR image reconstruction for compressed sensing acquisition in T2* imaging

“…The latest framework is known to achieve higher acceleration factors especially when combined with non-Cartesian sampling strategies such as radial, variable density spiral, 9 or the recently proposed Sparkling trajectories. 10 However, due to the presence of non-uniform 11 or non-equispaced 12 Fourier transform in the forward model, non-Cartesian CS imaging may lead to even longer image reconstruction times as compared to Cartesian CS imaging. Depending on spatial resolution and the number of receivers in the coil, such computational load may become incompatible with the online examination and fast quality check of MRI scans (e.g.…”