CORE‐PI: Non‐iterative convolution‐based reconstruction for parallel MRI in the wavelet domain

@article{Shimron2019COREPINC,
  title={CORE‐PI: Non‐iterative convolution‐based reconstruction for parallel MRI in the wavelet domain},
  author={Efrat Shimron and Andrew G. Webb and Haim Azhari},
  journal={Medical Physics},
  year={2019},
  volume={46},
  pages={199–214}
}
PURPOSE To develop and test a novel parameter-free non-iterative wavelet domain method for reconstruction of undersampled multicoil MR data. THEORY AND METHODS A linear parallel MRI method that operates in the Stationary Wavelet Transform (SWT) domain is proposed. The method is coined COnvolution-based REconstruction for Parallel MRI (CORE-PI). This method computes the SWT of the unknown MR image directly from subsampled k-space measurements, without modifying the RF excitation pulse. It then… 
4 Citations

Figures and Tables from this paper

A dictionary‐based graph‐cut algorithm for MRI reconstruction
TLDR
An efficient, discrete optimization formulation is proposed, which works not only on arbitrary Lp‐norm priors as some non‐convex CS methods do, but also on highly non‐ Convex truncated penalty functions, resulting in a specific type of edge‐preserving prior.
Magnetic Resonance Imaging Based on Wavelet Algorithm in the Diagnosis andTreatment ofTibialOsteomyelitisWound Infection
TLDR
The results of the enhanced and improved MRI images were relatively more accurate and the treatment methods adopted were more symptomatic, resulting in more effective treatment.
Magnetic Resonance Imaging Based on Wavelet Algorithm in the Diagnosis and Treatment of Tibial Osteomyelitis Wound Infection
TLDR
The enhanced and improved MRI images were relatively more accurate and the treatment methods adopted were more symptomatic, resulting in more effective treatment and the wavelet algorithm had certain application value in the enhancement processing of medical images and showed a good development prospect.

References

SHOWING 1-10 OF 52 REFERENCES
An adaptive undersampling scheme of wavelet-encoded parallel MR imaging for more efficient MR data acquisition
TLDR
An undersampling scheme named significance map for sparse wavelet-encoded k-space to speed up data acquisition as well as allowing for various adaptive imaging strategies is introduced to compensate for those disadvantages of wavelet encoded MRI.
SPIRiT: Iterative self‐consistent parallel imaging reconstruction from arbitrary k‐space
TLDR
A new approach to autocalibrating, coil‐by‐coil parallel imaging reconstruction, is presented, a generalized reconstruction framework based on self‐consistency that can accurately reconstruct images from arbitrary k‐space sampling patterns.
A Fast Wavelet-Based Reconstruction Method for Magnetic Resonance Imaging
TLDR
This work exploits the fact that wavelets can represent magnetic resonance images well, with relatively few coefficients, to improve magnetic resonance imaging (MRI) reconstructions from undersampled data with arbitrary k-space trajectories and proposes a variant that combines recent improvements in convex optimization and that can be tuned to a given specific k- space trajectory.
Advances in sensitivity encoding with arbitrary k‐space trajectories
TLDR
Using the proposed method, SENSE becomes practical with nonstandard k‐space trajectories, enabling considerable scan time reduction with respect to mere gradient encoding, and the in vivo feasibility of non‐Cartesian SENSE imaging with iterative reconstruction is demonstrated.
Sparse MRI: The application of compressed sensing for rapid MR imaging
TLDR
Practical incoherent undersampling schemes are developed and analyzed by means of their aliasing interference and demonstrate improved spatial resolution and accelerated acquisition for multislice fast spin‐echo brain imaging and 3D contrast enhanced angiography.
SENSE: Sensitivity encoding for fast MRI
TLDR
The problem of image reconstruction from sensitivity encoded data is formulated in a general fashion and solved for arbitrary coil configurations and k‐space sampling patterns and special attention is given to the currently most practical case, namely, sampling a common Cartesian grid with reduced density.
Efficient, Convergent SENSE MRI Reconstruction for Nonperiodic Boundary Conditions via Tridiagonal Solvers
  • Mai Le, J. Fessler
  • Computer Science
    IEEE Transactions on Computational Imaging
  • 2017
TLDR
Two methods for CS-SENSE-MRI that use regularization with nonperiodic boundary conditions to prevent wrap-around artifacts are proposed and the tridiagonal VS approach is applied to a simple image inpainting problem.
Non‐Cartesian parallel imaging reconstruction
TLDR
This review will begin with an overview of non‐Cartesian k‐space trajectories and their sampling properties, followed by an in‐depth discussion of several selected non‐ Cartesian parallel imaging algorithms.
A versatile wavelet domain noise filtration technique for medical imaging
We propose a robust wavelet domain method for noise filtering in medical images. The proposed method adapts itself to various types of image noise as well as to the preference of the medical expert;
...
...