Multicontrast MRI Reconstruction with Structure-Guided Total Variation

DOI10.1137/15M1047325zbMath1346.47006arXiv1511.06631OpenAlexW3100694778MaRDI QIDQ3188220

Marta M. Betcke, Matthias J. Ehrhardt

Publication date: 17 August 2016

Published in: SIAM Journal on Imaging Sciences (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1511.06631

zbMATH Keywords

total variationregularizationmagnetic resonance imagingimage reconstructionstructural similarityMRIa priori information

Mathematics Subject Classification ID

Related Items (20)

Bilevel Training Schemes in Imaging for Total Variation--Type Functionals with Convex Integrands ⋮ Joint image reconstruction method with correlative multi-channel prior for x-ray spectral computed tomography ⋮ A function space framework for structural total variation regularization with applications in inverse problems ⋮ Dynamic MRI reconstruction from undersampled data with an anatomical prescan ⋮ Coupled regularization with multiple data discrepancies ⋮ Inexact first-order primal-dual algorithms ⋮ An inexact primal-dual method with correction step for a saddle point problem in image debluring ⋮ Higher-Order Total Directional Variation: Analysis ⋮ On and Beyond Total Variation Regularization in Imaging: The Role of Space Variance ⋮ A Flexible Space-Variant Anisotropic Regularization for Image Restoration with Automated Parameter Selection ⋮ A joint reconstruction and lambda tomography regularization technique for energy-resolved x-ray imaging ⋮ Joint reconstruction via coupled Bregman iterations with applications to PET-MR imaging ⋮ Higher-Order Total Directional Variation: Imaging Applications ⋮ Incorporating structural prior information and sparsity into EIT using parallel level sets ⋮ Blind image fusion for hyperspectral imaging with the directional total variation ⋮ Image reconstruction model for limited-angle CT based on prior image induced relative total variation ⋮ Review of Image Similarity Measures for Joint Image Reconstruction from Multiple Measurements ⋮ Denoising AMP for MRI Reconstruction: BM3D-AMP-MRI ⋮ Modern regularization methods for inverse problems ⋮ On a general smoothly truncated regularization for variational piecewise constant image restoration: construction and convergent algorithms

Uses Software

• DLMRI-Lab
• BrainWeb

Cites Work

• Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
• Nonlinear total variation based noise removal algorithms
• A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
• Anisotropic total variation filtering
• On the Douglas-Rachford splitting method and the proximal point algorithm for maximal monotone operators
• A Variational Approach for Sharpening High Dimensional Images
• Solution-Driven Adaptive Total Variation Regularization
• Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
• Inverse problems with structural prior information
• Vector-Valued Image Processing by Parallel Level Sets
• Adaptive Regularization With the Structure Tensor
• Joint reconstruction of PET-MRI by exploiting structural similarity
• Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems
• Fast Image Recovery Using Variable Splitting and Constrained Optimization
• Stable signal recovery from incomplete and inaccurate measurements
• Convex Analysis
• Compressed sensing
• Mathematical problems in image processing. Partial differential equations and the calculus of variations. Foreword by Olivier Faugeras

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