Improving spreading projection algorithm for rapid k-space sampling trajectories through minimized off-resonance effects and gridding of low frequencies.

PURPOSE: Non-Cartesian MRI with long arbitrary readout directions are susceptible to off-resonance artifacts due to patient induced B 0 $$ {B}_0 $$ inhomogeneities. This results in degraded image quality with strong signal losses and blurring. Current solutions to address this issue involve correcting the off-resonance artifacts during image reconstruction or reducing inhomogeneities through improved shimming. THEORY: The recently developed SPARKLING algorithm is extended to drastically diminish off-resonance artifacts by generating temporally smooth k-space sampling patterns. For doing so, the cost function which is optimized in SPARKLING is modified using a temporal weighting factor. Additionally, oversampling of the center of k-space beyond the Nyquist criteria is prevented through the use of gridded sampling in the region, enforced with affine constraints. METHODS: Prospective k-space data was acquired at 3 T on new trajectories, and we show robustness to B 0 $$ {\mathrm{B}}_0 $$ inhomogeneities through in silico experiments by adding Δ B 0 $$ \Delta {\mathrm{B}}_0 $$  through artificial degradation of system B 0 $$ {\mathrm{B}}_0 $$ shimming. Later on, in vivo experiments were carried out to optimize parameters of the new improvements and benchmark the gain in performance. RESULTS: The improved trajectories allowed for the recovery of signal dropouts observed on original SPARKLING acquisitions at larger B 0 $$ {\mathrm{B}}_0 $$ field inhomogeneities. Furthermore, imposing gridded sampling at the center of k-space provided improved reconstructed image quality with limited artifacts. CONCLUSION: These advancements allowed us for nearly 4 . 62 × $$ 4.62\times $$ shorter scan time compared to GRAPPA-p4x1, allowing us to reach 600 µm isotropic resolution in 3D T 2 ∗ $$ {\mathrm{T}}_2^{\ast } $$ -w imaging in just 3.3 min at 3 T with negligible degradation in image quality.

Calibration-Less Multi-Coil Compressed Sensing Magnetic Resonance Image Reconstruction Based on OSCAR Regularization.

Over the last decade, the combination of compressed sensing (CS) with acquisition over multiple receiver coils in magnetic resonance imaging (MRI) has allowed the emergence of faster scans while maintaining a good signal-to-noise ratio (SNR). Self-calibrating techniques, such as ESPiRIT, have become the standard approach to estimating the coil sensitivity maps prior to the reconstruction stage. In this work, we proceed differently and introduce a new calibration-less multi-coil CS reconstruction method. Calibration-less techniques no longer require the prior extraction of sensitivity maps to perform multi-coil image reconstruction but usually alternate estimation sensitivity map estimation and image reconstruction. Here, to get rid of the nonconvexity of the latter approach we reconstruct as many MR images as the number of coils. To compensate for the ill-posedness of this inverse problem, we leverage structured sparsity of the multi-coil images in a wavelet transform domain while adapting to variations in SNR across coils owing to the OSCAR (octagonal shrinkage and clustering algorithm for regression) regularization. Coil-specific complex-valued MR images are thus obtained by minimizing a convex but nonsmooth objective function using the proximal primal-dual Condat-Vù algorithm. Comparison and validation on retrospective Cartesian and non-Cartesian studies based on the Brain fastMRI data set demonstrate that the proposed reconstruction method outperforms the state-of-the-art (ℓ1-ESPIRiT, calibration-less AC-LORAKS and CaLM methods) significantly on magnitude images for the T1 and FLAIR contrasts. Additionally, further validation operated on 8 to 20-fold prospectively accelerated high-resolution ex vivo human brain MRI data collected at 7 Tesla confirms the retrospective results. Overall, OSCAR-based regularization preserves phase information more accurately (both visually and quantitatively) compared to other approaches, an asset that can only be assessed on real prospective experiments.