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Fast multiclass dictionaries learning with geometrical directions in MRI reconstruction
Objective: Improve the reconstructed image with fast and multiclass dictionaries learning
when magnetic resonance imaging is accelerated by undersampling the k-space data …
when magnetic resonance imaging is accelerated by undersampling the k-space data …
Image reconstruction of compressed sensing MRI using graph-based redundant wavelet transform
Compressed sensing magnetic resonance imaging has shown great capacity for
accelerating magnetic resonance imaging if an image can be sparsely represented. How the …
accelerating magnetic resonance imaging if an image can be sparsely represented. How the …
Fast multi-contrast MRI reconstruction
Multi-contrast magnetic resonance imaging (MRI) is a useful technique to aid clinical
diagnosis. This paper proposes an efficient algorithm to jointly reconstruct multiple T1/T2 …
diagnosis. This paper proposes an efficient algorithm to jointly reconstruct multiple T1/T2 …
Balanced sparse model for tight frames in compressed sensing magnetic resonance imaging
Compressed sensing has shown to be promising to accelerate magnetic resonance
imaging. In this new technology, magnetic resonance images are usually reconstructed by …
imaging. In this new technology, magnetic resonance images are usually reconstructed by …
MR image reconstruction using a combination of compressed sensing and partial Fourier acquisition: ESPReSSo
A Cartesian subsampling scheme is proposed incorporating the idea of PF acquisition and
variable-density Poisson Disc (vdPD) subsampling by redistributing the sampling space …
variable-density Poisson Disc (vdPD) subsampling by redistributing the sampling space …
WTDUN: Wavelet tree-structured sampling and deep unfolding network for image compressed sensing
Deep unfolding networks have gained increasing attention in the field of compressed
sensing (CS) owing to their theoretical interpretability and superior reconstruction …
sensing (CS) owing to their theoretical interpretability and superior reconstruction …
Iterative support detection-based split Bregman method for wavelet frame-based image inpainting
The wavelet frame systems have been extensively studied due to their capability of sparsely
approximating piecewise smooth functions, such as images, and the corresponding wavelet …
approximating piecewise smooth functions, such as images, and the corresponding wavelet …
Fast iteratively reweighted least squares algorithms for analysis-based sparse reconstruction
In this paper, we propose a novel algorithm for analysis-based sparsity reconstruction. It can
solve the generalized problem by structured sparsity regularization with an orthogonal basis …
solve the generalized problem by structured sparsity regularization with an orthogonal basis …
Energy preserved sampling for compressed sensing MRI
The sampling patterns, cost functions, and reconstruction algorithms play important roles in
optimizing compressed sensing magnetic resonance imaging (CS‐MRI). Simple random …
optimizing compressed sensing magnetic resonance imaging (CS‐MRI). Simple random …
Diffuse optical tomography enhanced by clustered sparsity for functional brain imaging
Diffuse optical tomography (DOT) is a noninvasive technique which measures
hemodynamic changes in the tissue with near infrared light, which has been increasingly …
hemodynamic changes in the tissue with near infrared light, which has been increasingly …