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Found 15 papers, 9 papers with code
SNAKE-fMRI: A modular fMRI data simulator from the space-time domain to k-space and back
We propose a new, modular, open-source, Python-based 3D+time fMRI data simulation software, \emph{SNAKE-fMRI}, which stands for \emph{S}imulator from \emph{N}eurovascular coupling to \emph{A}cquisition of \emph{K}-space data for \emph{E}xploration of fMRI acquisition techniques. Unlike existing tools, the goal here is to simulate the complete chain of fMRI data acquisition, from the spatio-temporal design of evoked brain responses to various multi-coil k-space data 3D sampling strategies, with the possibility of extending the forward acquisition model to various noise and artifact sources while remaining memory-efficient. By using this \emph{in silico} setup, we are thus able to provide realistic and reproducible ground truth for fMRI reconstruction methods in 3D accelerated acquisition settings and explore the influence of critical parameters, such as the acceleration factor and signal-to-noise ratio~(SNR), on downstream tasks of image reconstruction and statistical analysis of evoked brain activity. We present three scenarios of increasing complexity to showcase the flexibility, versatility, and fidelity of \emph{SNAKE-fMRI}: From a temporally-fixed full 3D Cartesian to various 3D non-Cartesian sampling patterns, we can compare -- with reproducibility guarantees -- how experimental paradigms, acquisition strategies and reconstruction methods contribute and interact together, affecting the downstream statistical analysis.
Benchmarking learned non-Cartesian k-space trajectories and reconstruction networks
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.
Hybrid learning of Non-Cartesian k-space trajectory and MR image reconstruction networks
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.
Is good old GRAPPA dead?
We perform a qualitative analysis of performance of XPDNet, a state-of-the-art deep learning approach for MRI reconstruction, compared to GRAPPA, a classical approach.
SHINE: SHaring the INverse Estimate from the forward pass for bi-level optimization and implicit models
In Deep Equilibrium Models (DEQs), the training is performed as a bi-level problem, and its computational complexity is partially driven by the iterative inversion of a huge Jacobian matrix.
Learning the sampling density in 2D SPARKLING MRI acquisition for optimized image reconstruction
However, the two main limitations of SPARKLING are first that the optimal target sampling density is unknown and thus a user-defined parameter and second that this sampling pattern generation remains disconnected from MR image reconstruction thus from the optimization of image quality.
Density Compensated Unrolled Networks for Non-Cartesian MRI Reconstruction
Deep neural networks have recently been thoroughly investigated as a powerful tool for MRI reconstruction.
Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction
Accelerating MRI scans is one of the principal outstanding problems in the MRI research community.
Denoising Score-Matching for Uncertainty Quantification in Inverse Problems
Deep neural networks have proven extremely efficient at solving a wide rangeof inverse problems, but most often the uncertainty on the solution they provideis hard to quantify.
XPDNet for MRI Reconstruction: an application to the 2020 fastMRI challenge
We present a new neural network, the XPDNet, for MRI reconstruction from periodically under-sampled multi-coil data.
Ranked #2 on
MRI Reconstruction
on fastMRI Brain 8x
Benchmarking MRI Reconstruction Neural Networks on Large Public Datasets
Deep learning is starting to offer promising results for reconstruction in Magnetic Resonance Imaging (MRI).
Structured Sparse Principal Components Analysis with the TV-Elastic Net penalty
However, in neuroimaging, it is essential to uncover clinically interpretable phenotypic markers that would account for the main variability in the brain images of a population.
Data-driven HRF estimation for encoding and decoding models
We develop a method for the joint estimation of activation and HRF using a rank constraint causing the estimated HRF to be equal across events/conditions, yet permitting it to be different across voxels.
Variable density sampling with continuous trajectories. Application to MRI
Its standard design relies on the random drawing of independent measurements.
Applications
Spatio-temporal wavelet regularization for parallel MRI reconstruction: application to functional MRI
To improve the performance of the widely used SENSE algorithm, 2D- or slice-specific regularization in the wavelet domain has been deeply investigated.
