Corpus ID: 222133841

Off-the-grid data-driven optimization of sampling schemes in MRI

@article{Gossard2020OffthegridDO,
  title={Off-the-grid data-driven optimization of sampling schemes in MRI},
  author={Alban Gossard and Fr{\'e}d{\'e}ric de Gournay and Pierre Weiss},
  journal={ArXiv},
  year={2020},
  volume={abs/2010.01817}
}
We propose a novel learning based algorithm to generate efficient and physically plausible sampling patterns in MRI. This method has a few advantages compared to recent learning based approaches: i) it works off-the-grid and ii) allows to handle arbitrary physical constraints. These two features allow for much more versatility in the sampling patterns that can take advantage of all the degrees of freedom offered by an MRI scanner. The method consists in a high dimensional optimization of a cost… Expand

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References

SHOWING 1-10 OF 20 REFERENCES
Learning-Based Compressive MRI
TLDR
A learning-based framework for optimizing MRI subsampling patterns for a specific reconstruction rule and anatomy, considering both the noiseless and noisy settings is proposed and a novel parameter-free greedy mask selection method is presented. Expand
On the Generation of Sampling Schemes for Magnetic Resonance Imaging
TLDR
This paper proposes an original approach which consists of projecting a probability distribution onto a set of admissible measures and automatically generates efficient sampling patterns for MRI as shown on realistic simulations. Expand
Learning the Sampling Pattern for MRI
TLDR
This work considers the problem of learning a sparse sampling pattern that can be used to optimally balance acquisition time versus quality of the reconstructed image, using a supervised learning approach. Expand
Variable Density Sampling with Continuous Trajectories
TLDR
This paper discusses the choice of an optimal sampling subspace (smallest subset) allowing perfect reconstruction of sparse signals and shows that a mixed strategy involving partial deterministic sampling and independent drawings can help breaking the so-called "coherence barrier". Expand
Self-Supervised Deep Active Accelerated MRI
TLDR
This work proposes to simultaneously learn to sample and reconstruct magnetic resonance images (MRI) to maximize the reconstruction quality given a limited sample budget, in a self-supervised setup by considering both the data acquisition and the reconstruction process within a single deep-learning framework. Expand
Accelerating the Nonuniform Fast Fourier Transform
TLDR
This paper observes that one of the standard interpolation or "gridding" schemes, based on Gaussians, can be accelerated by a significant factor without precomputation and storage of the interpolation weights, of particular value in two- and three- dimensional settings. Expand
Compressed sensing with structured sparsity and structured acquisition
TLDR
New CS results for structured acquisitions and signal satisfying a prior structured sparsity are derived, which are RIPless, in the sense that they do not hold for any s-sparse vector, but for sparse vectors with a given support S. Expand
Bilevel Optimization with Nonsmooth Lower Level Problems
TLDR
This work proposes an alternative method based on differentiating the iterations of a nonlinear primal–dual algorithm that computes exact (sub)gradients and can be applied also in the nonsmooth setting. Expand
Learning a variational network for reconstruction of accelerated MRI data
To allow fast and high‐quality reconstruction of clinical accelerated multi‐coil MR data by learning a variational network that combines the mathematical structure of variational models with deepExpand
Python Non-Uniform Fast Fourier Transform (PyNUFFT): An Accelerated Non-Cartesian MRI Package on a Heterogeneous Platform (CPU/GPU)
TLDR
A Python non-uniform fast Fourier transform (PyNUFFT) package has been developed to accelerate multidimensional non-Cartesian image reconstruction on heterogeneous platforms and provides several solvers, including the conjugate gradient method, l1 total variation regularized ordinary least square (L1TV-OLS), and l1total variation regularization least absolute deviation (L 1TV-LAD). Expand
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