Content tagged with "mri"
MRI-NUFFT is an open-source Python library that provides state-of-the-art non-Cartesian MRI tools: trajectories, data loading, fast and memory-efficient operators to be used on laptops, clusters, and MRI consoles.
Accelerated acquisition of fMRI enables enhanced detection of neurovascular (BOLD) activity in the brain, but image reconstruction becomes challenging with high k-space undersampling: Task-evoked BOLD signals are small in magnitude, which traditional anatomical MRI reconstruction methods fail to recover, as they favor…
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.
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: 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.
MRI-NUFFT is a python package that provides a universal interface to various Non-Uniform Fast Fourier Transform libraries running on CPU or GPU (gpuNUFFT, finufft, cufinufft, pyNFFT), adding compatibility with standard array library (NumPy, CuPy, PyTorch, TensorFlow, etc.) On top of these libraries it extends the…
Motivation: Non-Cartesian imaging remains complicated to use for MRI due to the high computational cost of the Non-Uniform Fourier Transform for image reconstruction. Goal(s): To provide a uniform interface for reconstructing magnetic resonance images from non-Cartesian k-space data and a collection of non-Cartesian…
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.
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…
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…
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.
Conception et apprentissage de trajectoires d'échantillonnage non cartésiennes dans l'espace k pour l'IRM 3D accélérée L'imagerie par résonance magnétique (IRM) est une technique d'imagerie médicale non invasive très répandue qui permet de comprendre les structures des tissus du corps humain. Cependant, sa portée et…
Journal article · Journal of Neuroradiology · 2023
Compressed sensing in magnetic resonance imaging essentially involves the optimization of (1) the sampling pattern in k-space under MR hardware constraints and (2) image reconstruction from undersampled k-space data. Recently, deep learning methods have allowed the community to address both problems simultaneously,…
Submitted to Magnetic Resonance in Medicine
Preprint (arXiv) · HAL (Le Centre pour la Communication Scientifique Directe) · 2022
We benchmark the current existing methods to jointly learn non-Cartesian k-space trajectory and reconstruction: PILOT, BJORK, and compare them with those obtained from the recently developed generalized hybrid learning (HybLearn) framework. We present the advantages of using projected gradient descent to enforce MR…
Deep Learning has become a very promising avenue for magnetic resonance image (MRI) reconstruction. In this work, we explore the potential of unrolled networks for non-Cartesian acquisition settings.
Compressed sensing (CS) in Magnetic resonance Imaging (MRI) essentially involves the optimization of 1) the sampling pattern in k-space under MR hardware constraints and 2) image reconstruction from the undersampled k-space data. Recently, deep learning methods have allowed the community to address both problems…
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…
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…
We present the open-source image processing software package PySAP (Python Sparse data Analysis Package) developed for the COmpressed Sensing for Magnetic resonance Imaging and Cosmology (COSMIC) project. This package provides a set of flexible tools that can be applied to a variety of compressed sensing and image…
Molecular dynamics simulation and in vitro nuclear magnetic resonance (NMR) studies on glutathione (GSH) indicated existence of closed and extended conformations. The present work in a multi-center research setting reports in-depth analysis of GSH conformers in vivo using a common magnetic resonance spectroscopy (MRS)…