• Corpus ID: 125112670

Sparse MRI and CT Reconstruction

@inproceedings{Kermani2017SparseMA,
  title={Sparse MRI and CT Reconstruction},
  author={Ali Kermani},
  year={2017}
}
.............................................................................................................................. iii DEDICATION ............................................................................................................................ v TABLE OF CONTENTS ........................................................................................................... vi LIST OF TABLES… 

References

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Sparse-View CT Reconstruction Using Curvelet and TV-Based Regularization
TLDR
A new regularization model for CT reconstruction by combining regularization methods based on TV and the curvelet transform is proposed and results show that there are benefits in using the proposed combined curvelet and TV regularizer in the sparse view CT reconstruction.
Iterative CT Reconstruction Using Shearlet-Based Regularization
TLDR
This work investigates the use of another regularization approach in the context of medical images based on multiresolution transformations, and shows that there are benefits in using shearlets in CT imaging: texture is reconstructed more accurately compared to when TV is used, without biasing the image towards a piecewise constant image model.
MR image reconstruction based on framelets and nonlocal total variation using split Bregman method
TLDR
The proposed algorithm effectively solves a hybrid regularizer based on framelet sparsity and NLTV using the split Bregman method and makes the recovered image quality sharper by preserving the edges or boundaries more accurately, and framelets often improve image quality.
MR Image Reconstruction from Sparse Radial Samples Using Bregman Iteration
2 1 || ) ( || || ) ( || || || L k L BV v y f NFFT f f − − + Ψ + λ υ , where vk=y+ vk-1-NFFT(fk) with the convention v0=0, to obtain an improved reconstructed image fk+1. This procedure goes on until
Iterative CT Reconstruction Using Curvelet-Based Regularization
TLDR
The algorithm was evaluated with one physical phantom dataset and one in vitro dataset and was compared against and two state-of-art approach, namely, wavelet-based regularization (WR) and total variation based regularization methods (TVR).
A New Detail-Preserving Regularization Scheme
TLDR
A novel regularization model is proposed that integrates two recently developed regularization tools: total generalized variation (TGV) by Bredies, Kunisch, and Pock; and shearlet transform by Labate, Lim, Kutyniok, and Weiss and “selectively regularizes” different image regions at different levels and thus largely avoids oil painting artifacts.
MR IMAGE RECONSTRUCTION BY USING THE ITERATIVE REFINEMENT METHOD AND NONLINEAR INVERSE SCALE SPACE METHODS
. Magnetic resonance imaging (MRI) reconstruction from sparsely sampled data has been a difficult problem in medical imaging field. We approach this problem by formulating a cost functional that
An efficient algorithm for compressed MR imaging using total variation and wavelets
TLDR
This work proposes an efficient algorithm that jointly minimizes the lscr1 norm, total variation, and a least squares measure, one of the most powerful models for compressive MR imaging, based upon an iterative operator-splitting framework.
Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization.
TLDR
An iterative algorithm, based on recent work in compressive sensing, that minimizes the total variation of the image subject to the constraint that the estimated projection data is within a specified tolerance of the available data and that the values of the volume image are non-negative is developed.
Few-View Projection Reconstruction With an Iterative Reconstruction-Reprojection Algorithm and TV Constraint
TLDR
Numerical simulations show that the IRR-TV algorithm is effective for the few-view problem of reconstructing sparse-gradient images and an improved weighting function is proposed for few-View short-scan projection reconstruction by the filtered backprojection (FBP) algorithm.
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