#sparkling

Purpose: T1-weighted MPRAGE remains a cornerstone of clinical anatomical imaging, yet its long acquisition times constrain routine use. Established acceleration techniques, namely Parallel Imaging (PI) and Compressed Sensing (CS), tend to introduce substantial noise and blurring when pushed to high acceleration…

While highly accelerated non-Cartesian acquisition protocols significantly reduce scan time, they often entail long reconstruction delays. Deep learning based reconstruction methods can alleviate this, but often lack stability and robustness to distribution shifts.

Motivation: Deep Neural Networks (DNNs) show promise in reconstructing undersampled MRI data but face challenges with non-Cartesian 3D multi-coil acquisitions due to high memory demands and training complexity. Goal(s): To address the research gap in validating DNNs performances for non-Cartesian data in 3D multi-coil…

Motivation: Well established clinically used parallel imaging techniques like GRAPPA and CAIPIRINHA are mostly limited to Cartesian sampling, and their extension to non-Cartesian sampling is not direct. Goal(s): We propose to extend the SPARKLING framework to ensure GRAPPA accelerated Cartesian sampling in k-space…

Non-Cartesian sampling patterns allow for highly accelerated MRI exams at the cost of exacerbated and more complex artifacts, each impacting image quality in unique ways. Patient-induced B0 field inhomogeneities can notably cause distortions and blurring, but some trajectories are by design more robust to them.

Cartesian sampling can acquire a given k-space region with minimum redundancy, while non-Cartesian sampling can help achieve a larger k-space coverage. Through generalized affine constraints in SPARKLING and an adapted target sampling density, for the first time non-Cartesian and Cartesian sampling are merged within…

The quest for higher spatial and/or temporal resolution in functional MRI (fMRI) while preserving a sufficient temporal signal-to-noise ratio (tSNR) has generated a tremendous amount of methodological contributions in the last decade ranging from Cartesian vs. non-Cartesian readouts, 2D vs.

The benefit of retrospective correction of static and dynamic B0 field imperfections was studied on non-Cartesian compressed sensing-based acquisition patterns by taking as an example 3D-SPARKLING encoding scheme. Long-TR dynamic acquisitions of T2*-weighted MRI (fMRI-like) volumes were acquired at 7T, retrospectively…

We augment the recently introduced SPARKLING algorithm and propose an improved mathematical formulation that takes the temporal dependence of the MR signal into account. This prevents the trajectories from sampling similar portions of k-space at different times, thereby reducing distortions and blurring induced by (B0…

For the first time, non-Cartesian 3D-SPARKLING (Spreading Projection Algorithm for Rapid K-space sampLING) encoding scheme was used for task-based fMRI (retinotopic mapping) at 7T. Additionally, this new acquisition technique was compared with 3D-EPI, considered as one of the reference acquisition schemes in…

Quantitative 23Na MRI provides useful information about brain tissue homeostasis. Most sequences use deterministic non-Cartesian 3D trajectories such as TPI.

Purpose Static and dynamic field imperfections are detrimental to functional MRI (fMRI) applications, especially at ultra‐high magnetic fields (UHF). In this work, a field camera is used to assess the benefits of retrospectively correcting field perturbations on Blood Oxygen Level Dependent (BOLD) sensitivity in…

Purpose Non‐Cartesian MRI with long arbitrary readout directions are susceptible to off‐resonance artifacts due to patient induced inhomogeneities. This results in degraded image quality with strong signal losses and blurring.

Journal article · Journal of Neuroradiology · 2023

Submitted to Magnetic Resonance in Medicine

Preprint (arXiv) · HAL (Le Centre pour la Communication Scientifique Directe) · 2022

International audience

Journal article · 2022

The SPARKLING algorithm was originally developed for accelerated 2D magnetic resonance imaging (MRI) in the compressed sensing (CS) context. It yields non-Cartesian sampling trajectories that jointly fulfill a target sampling density while each individual trajectory complies with MR hardware constraints.

The Spreading Projection Algorithm for Rapid K-space samplING, or SPARKLING, is an optimization-driven method that has been recently introduced for accelerated 2D T2*-w MRI using compressed sensing. It has then been extended to address 3D imaging using either stacks of 2D sampling patterns or a local 3D strategy that…

Journal article