#parallel imaging

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…

PURPOSE: field inhomogeneity artifacts arising from remote locations in the FOV and encountered in accelerated 3D gradient-recalled echo (GRE) sequences at ultra-high field, and at providing mitigation strategies. METHODS: Measurements were conducted at 11.7T using a head-shaped phantom and an accelerated 3D GRE…

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…

Motivation: Existing open-source MR reconstruction tools often fail to efficiently utilize GPU resources and lack support for generalized GRAPPA implementations. Many tools are limited to 2D or 3D reconstruction, and few incorporate advanced techniques such as 2D-CAIPIRINHA, which enhances imaging capabilities.

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.