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HYR2PICS: Hybrid regularized reconstruction for combined parallel imaging and compressive sensing in MRI

Claire Boyer, Philippe Ciuciu, Pierre Weiss, Sebastien Meriaux
2012 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI)  
So far, first attempts to combine sensitivity encoding (SENSE) imaging in pMRI with CS have been proposed in the context of Cartesian trajectories.  ...  Both parallel Magnetic Resonance Imaging (pMRI) and Compressed Sensing (CS) are emerging techniques to accelerate conventional MRI by reducing the number of acquired data in the k-space.  ...  To this end, both parallel imaging (pMRI) and compressed sensing (CS) have been proposed. pMRI relies on a geometrical principle involving multiple receiver coils with complementary sensitivity profiles  ... 
doi:10.1109/isbi.2012.6235485 dblp:conf/isbi/BoyerCWM12 fatcat:z6ctdwrysnhireeuhnzaaqtmd4

Smoothly clipped absolute deviation (SCAD) regularization for compressed sensing MRI using an augmented Lagrangian scheme

A. Mehranian, H. Saligheh Rad, M. R. Ay, A. Rahmim, H. Zaidi
2012 2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC)  
Rahmim is with the  ...  The summation over the magnitude of the gradient at all voxels defines the TV regularizer, which is known to be edge preserving in image processing and sparsity-promoting in compressed sensing.  ...  Recent developments in compressed sensing (CS) have introduced sparsity regularization techniques, which have gained significant attention in MR image reconstruction from highly undersampled k-spaces.  ... 
doi:10.1109/nssmic.2012.6551838 fatcat:a6hsoaqh6rf3zc3itcu2lxjiae

An Adaptive Directional Haar Framelet-Based Reconstruction Algorithm for Parallel Magnetic Resonance Imaging

Yan-Ran Li, Raymond H. Chan, Lixin Shen, Yung-Chin Hsu, Wen-Yih Isaac Tseng
2016 SIAM Journal of Imaging Sciences  
The ℓ 1 -SPIRiT [34] is an algorithm for auto calibrating parallel imaging and permits an efficient implementation with clinically-feasible runtimes by using compressive sensing.  ...  Sparse dictionary learning with compressive sensing was proposed in [38] to reconstruct MR images from highly undersampled kspace data.  ...  We proposed a pMRI reconstructed model in real domain whose regularization term was formed from the DHF. The reconstruction model can be efficiently solved by a proximal algorithm.  ... 
doi:10.1137/15m1033964 fatcat:mwzurcvxxrft3bud5gqq76pbxu

Spatio-temporal wavelet regularization for parallel MRI reconstruction: application to functional MRI

Lotfi Chaari, Philippe Ciuciu, Sébastien Mériaux, Jean-Christophe Pesquet
2014 Magnetic Resonance Materials in Physics, Biology and Medicine  
In this paper, we first extend this approach to 3D-wavelet representations and 3D sparsity-promoting regularization term, in order to address reconstruction artifacts which propagate across adjacent slices  ...  Conclusion: We show that our algorithm outperforms the SENSE reconstruction at the subject and group levels (15 subjects) for different contrasts of interest (motor or computation tasks) and two parallel  ...  For doing so, we introduce 3D wavelet transform and 3D sparsity-promoting regularization term in the wavelet domain.  ... 
doi:10.1007/s10334-014-0436-5 pmid:24619431 fatcat:haimttj6urewhd5jo3vx456you

A bayesian method for accelerated magnetic resonance elastography of the liver

Christopher Ebersole, Rizwan Ahmad, Adam V. Rich, Lee C. Potter, Huiming Dong, Arunark Kolipaka
2018 Magnetic Resonance in Medicine  
Pseudorandom sampling of k-space promotes incoherent aliasing, which allows compressive recovery via enforcement of sparsity in wavelet domain.  ...  Liver fibrosis, a common feature of many chronic liver diseases, is associated with an increase in liver stiffness.  ...  Here, a prior is selected such that sparsity is promoted in each offset under a two-dimensional undecimated wavelet transform.  ... 
doi:10.1002/mrm.27083 pmid:29334131 fatcat:igqrhp7h4faqzeg7knfmopjoo4

Regularized parallel mri reconstruction using an alternating direction method of multipliers

Sathish Ramani, Jeffrey A. Fessler
2011 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro  
Using sparsity-based regularization to improve magnetic resonance image (MRI) reconstruction quality demands computation-intensive nonlinear optimization.  ...  In this paper, we develop an iterative algorithm based on the method of multipliers-augmented Lagrangian (AL) formalism-for reconstruction from sensitivity encoded data using sparsity-based regularization  ...  As an attractive means of restoring stability in the reconstruction mechanism, sparsity-promoting regularization criteria have gained popularity in MRI [3, 4] due to advances in compressed sensing (CS  ... 
doi:10.1109/isbi.2011.5872429 dblp:conf/isbi/RamaniF11 fatcat:bytyzbutzbbdvoeszbg6iouupq

Preface

Zai-Wen Wen, Wo-Tao Yin, Xiao-Ming Yuan
2015 Journal of the Operations Research Society of China  
Owing much to the studies of signal representation, compressive sensing, and regularized regression, sparse and low-rank optimization has been recognized as a computational tool that plays central roles  ...  theoretical and numerical advances in sparse and low-rank optimization, including first-order methods such as the alternating direction method of multipliers (ADMM), distributed consensus optimization, image reconstruction  ...  Experimental results show that the proposed approach performs better than other state-of-the-art joint color and depth image reconstruction approaches.  ... 
doi:10.1007/s40305-015-0081-3 fatcat:ocrikymuhzenzpiy77sz637uxi

Spatio-temporal wavelet regularization for parallel MRI reconstruction: application to functional MRI [article]

Lotfi Chaari, Sébastien Mériaux, Jean-Christophe Pesquet and Philippe Ciuciu
2013 arXiv   pre-print
The resulting 3D-UWR-SENSE and 4D-UWR-SENSE reconstruction schemes are fully unsupervised in the sense that all regularization parameters are estimated in the maximum likelihood sense on a reference scan  ...  The gain induced by such extension (3D-Unconstrained Wavelet Regularized -SENSE: 3D-UWR-SENSE) is validated on anatomical image reconstruction where no temporal acquisition is considered.  ...  the full FOV image [3] [4] [5] . • Compressed Sensing (CS) MRI that exploits the implicit sparsity in MR images to significantly undersample the k-space and randomly select incoherent samples regarding  ... 
arXiv:1201.0022v3 fatcat:fehtnuuwdjcldmf4wojuytiy34

4D Wavelet-Based Regularization for Parallel MRI Reconstruction: Impact on Subject and Group-Levels Statistical Sensitivity in fMRI [article]

Lotfi Chaari, Sébastien Mériaux, Solveig Badillo, Jean-Christophe Pesquet, Philippe Ciuciu
2011 arXiv   pre-print
It relies on k-space undersampling and multiple receiver coils with complementary sensitivity profiles in order to reconstruct a full Field-Of-View (FOV) image.  ...  To improve the performance of the widely used SENSE algorithm, 2D- or slice-specific regularization in the wavelet domain has been efficiently investigated.  ...  the full FOV image [34, 18] . ii) compressed sensing MRI that exploits the implicit sparsity in MR images to significantly undersample the k-space and randomly select incoherent (or complementary) samples  ... 
arXiv:1103.3532v1 fatcat:kg3y3f6pmjdl5n7t4rtdiw2xci

Optimization-Based Deep learning methods for Magnetic Resonance Imaging Reconstruction and Synthesis

Wanyu Bian, Yunmei Chen
2022 Zenodo  
training algorithms that improve the accuracy and robustness of the optimization-based deep learning methods for compressed sensing MRI reconstruction and synthesis.  ...  This dissertation is devoted to provide advanced nonconvex nonsmooth variational models of (Magnetic Resonance Image) MRI reconstruction, efficient learnable image reconstruction algorithms and parameter  ...  LINDBERG [190] explored calibration-free pMRI technique which uses adaptive sparse coding to obtain joint-sparse representation precisely by equipping a joint sparsity regularization to extract desirable  ... 
doi:10.5281/zenodo.6863816 fatcat:3tehpr5dwzflvo4x6prxqm4yce

K-space and Image Domain Collaborative Energy based Model for Parallel MRI Reconstruction [article]

Zongjiang Tu, Chen Jiang, Yu Guan, Shanshan Wang, Jijun Liu, Qiegen Liu, Dong Liang
2022 arXiv   pre-print
Experimental comparisons with the state-of-the-arts demonstrated that the proposed hybrid method has less error in reconstruction and is more stable under different acceleration factors.  ...  [31] explored CNN-based inversion in the sparsity-promoting denoising module to generalize the sparsity-enforcing operator.  ...  Among them, compressive sensing (CS) [5] has represented a widely used accelerating approach, which does not require MR hardware modification.  ... 
arXiv:2203.10776v2 fatcat:naf3ud5apbbozk3yjuyonegtpa

Accelerated Variance Based Joint Sparsity Recovery of Images from Fourier Data [article]

Theresa Scarnati, Anne Gelb
2019 arXiv   pre-print
The efficiency is due to the decoupling of the measurement vectors, with the increased accuracy resulting from the spatially varying weight.  ...  Image recovery techniques from MMVs aim to exploit the joint sparsity across the measurements in the sparse domain.  ...  The sparsity of g allows us to recover an approximation f * of f via 1 regularized inversion techniques, also commonly referred to as compressive sensing, [6, 7, 20] .  ... 
arXiv:1910.08391v1 fatcat:vwfvsydhevgblfj6hfbipj2f2i

Parallel MR Image Reconstruction Using Augmented Lagrangian Methods

S Ramani, J A Fessler
2011 IEEE Transactions on Medical Imaging  
The proposed methods are applicable to a general class of regularizers that includes popular edge-preserving (e.g., total-variation) and sparsity-promoting (e.g., 1 -norm of wavelet coefficients) criteria  ...  Magnetic resonance image (MRI) reconstruction using SENSitivity Encoding (SENSE) requires regularization to suppress noise and aliasing effects.  ...  We consider sparsity-promoting regularization for based on the field of compressed sensing for MRI-CS-MRI [10] , [11] .  ... 
doi:10.1109/tmi.2010.2093536 pmid:21095861 pmcid:PMC3081617 fatcat:35k62azexbaw7isdy45jo2v444

A Bayesian model for highly accelerated phase-contrast MRI

Adam Rich, Lee C. Potter, Ning Jin, Joshua Ash, Orlando P. Simonetti, Rizwan Ahmad
2015 Magnetic Resonance in Medicine  
We propose a new data processing approach called Reconstructing Velocity Encoded MRI with Approximate message passing aLgorithms (ReVEAL) that accelerates the acquisition by exploiting data structure unique  ...  Purpose-Phase-contrast magnetic resonance imaging (PC-MRI) is a noninvasive tool to assess cardiovascular disease by quantifying blood flow; however, low data acquisition efficiency limits the spatial  ...  We thank Daniel Kim (University of Utah), Li Feng (Cedars-Sinai Medical Center), and Hassan Haji-Valizadeh (University of Utah) for providing Matlab code for k-t SPARSE-SENSE.  ... 
doi:10.1002/mrm.25904 pmid:26444911 pmcid:PMC4824680 fatcat:afr35ov3zzfx3n3gti3iv6yjxq

Weighted Low-rank Tensor Recovery for Hyperspectral Image Restoration [article]

Yi Chang, Luxin Yan, Houzhang Fang, Sheng Zhong, Zhijun Zhang
2017 arXiv   pre-print
It is also possible to apply our WLRTR model for HSI compress sensing, unmixing and also the video applications. Fig. 1 : 1 The illustration of the HOSVD.  ...  The most popular sparsity promoting methods mainly include the sparse representation [1, 2, 16, 38, 46, 72] and the matrix factorization approach [22, 39, 42, 74, 82] .  ... 
arXiv:1709.00192v1 fatcat:ueremc3lzvhlna42pgngpgb3ka
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