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akcakaya m magnetic resonance image reconstruction theory app 2022

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Textbook in PDF format Magnetic Resonance Image Reconstruction: Theory, Methods and Applications presents the fundamental concepts of MR image reconstruction, including its formulation as an inverse problem, as well as the most common models and optimization methods for reconstructing MR images. The book discusses approaches for specific applications such as non-Cartesian imaging, under sampled reconstruction, motion correction, dynamic imaging and quantitative MRI. This unique resource is suitable for physicists, engineers, technologists and clinicians with an interest in medical image reconstruction and MRI. MRI reconstruction and its role in clinical practice Organization of the book Part Basics of MRI Reconstruction Brief Introduction to MRI Physics A brief history of MRI Nuclear magnetism Spin Net magnetization Magnetization dynamics NMR/MRI signal Signal creation and reception Radiofrequency pulses Signal detection Signal relaxation and decay Longitudinal relaxation Transverse relaxation Image formation Frequency encoding Phase encoding Slice selection Sequence diagram k-space formalism k-space trajectories Echo-planar imaging Non-Cartesian trajectories Pulse sequence types Spin echo Gradient echo Balanced steady-state free precession Components of an MRI scanner Magnet Gradient coils Radiofrequency coils Noise properties Suggested readings MRI Reconstruction as an Inverse Problem Inverse problems Discretization of the MR signal MR reconstruction as a linear inverse problem Solution of the MR reconstruction problem Regularizing the MR reconstruction problem Nonlinear inverse problems in MR Nonlinear parallel imaging Nonlinear motion estimation/correction Nonlinear parameter reconstruction Suggested readings Optimization Algorithms for MR Reconstruction Least squares reconstruction Model-based reconstruction Smooth optimization Nonsmooth optimization Stochastic gradient-based approaches Non-Cartesian MRI Reconstruction NFFT Gridding Iterative reconstruction Examples Spatial resolution and noise Extensions ``Early'' Constrained Reconstruction Methods Basic Fourier reconstruction Constrained reconstruction: historical perspective Support-constrained reconstruction Phase-constrained reconstruction Linear predictive reconstruction Rank-constrained reconstruction Sparsity-constrained reconstruction Reconstruction using side information Discussion Part Reconstruction of Undersampled MRI Data Parallel Imaging Fundamental techniques Advanced techniques D volumetric parallel imaging Dynamic parallel imaging Artifacts in parallel imaging Suggested readings Simultaneous Multislice Reconstruction Basics of SMS encoding Reconstruction of SMS using parallel imaging concepts SENSE Extended FOV methods SENSE-GRAPPA RO-SENSE-GRAPPA Kernel calibration Slice-GRAPPA Split-Slice-GRAPPA SMS with phase-encoding undersampling Reconstruction of SMS for EPI Blipped-wideband and blipped-CAIPI encoding Slice-GRAPPA with dual polarity SENSE-model for EPI Calibration and reference scans Calibration and reference scans for EPI Reconstruction metrics Noise amplification Residual aliasing Qualitative effect of slice leakage Extensions of SMS SMS and D imaging Non-Cartesian SMS Applications of SMS Exercise Content of tutorial Questions A Extended FOV methods for SMS PE-SENSE-GRAPPA Unbiased slice-GRAPPA Sparse Reconstruction Compressed sensing theory: a brief overview Sparsity and incoherence: a first look Compressed sensing reconstruction Conditions for compressed sensing reconstruction Compressed sensing MRI Sparsifying transform and transform sparsity Incoherent data acquisition Image reconstruction Combination of compressed sensing MRI with parallel imaging Why compressed sensing + parallel imaging? Representative compressed sensing + parallel imaging methods Clinical applications of compressed sensing MRI Challenges of compressed sensing MRI Tutorial A Conditions for a unique solution in compressed sensing Low-Rank Matrix and Tensor–Based Reconstruction Problem formulation Matrix-based approaches Global low-rank modeling Sampling Image reconstruction Explicit low-rank reconstruction Fixed-subspace reconstruction Alternating reconstruction Implicit low-rank reconstruction Local low-rank modeling Low-rank and sparse modeling Low-rank plus sparse modeling Multiscale low-rank modeling Tensor-based approaches Tensor definitions CP decomposition Tucker decomposition Tensor rank surrogates Reinterpreting dynamic images as tensors Coil modeling Patch similarity modeling Spatial separability Multidynamic tensors Tensor-based compressed sensing Multidynamic low-rank tensor modeling Explicit multidynamic low-rank tensor reconstruction Implicit multidynamic low-rank tensor reconstruction Additional multidynamic LRT models Dictionary, Structured Low-Rank, and Manifold Learning-Based Reconstruction Background Acquisition scheme Manifold models of signals Capitalization of redundancy using structured matrices Efficient matrix representation in terms of factors Dictionary learning and blind compressed sensing Subspace selection for each signal of interest using sparse representation Dictionary pre-learning Dictionary pre-learning, applied to static MRI Blind compressed sensing (BCS) Application of BCS to dynamic MRI Application of BCS to static imaging Structured low-rank methods Low-rank structure of patch matrices in k-space Low-rank relationships in multichannel MRI Low-rank structure resulting from finite support and smoothly varying image phase Low-rank structure resulting from continuous domain sparsity Low-rank structure of piecewise smooth images Low-rank relations in parameter mapping Algorithms for k-space patch low-rank methods Iterative reweighted least square (IRLS) algorithm Algorithms that rely on calibration data Smooth manifold models Analysis manifold methods Relationship to factor models and binning based approaches Estimation of manifold Laplacian Image recovery assuming smooth patch manifold Application to dynamic MRI Software Machine Learning for MRI Reconstruction Organization of this chapter Machine learning definitions Learning models Types of learning Cost function, optimization and backpropagation Training, validation, and testing Database splitting Task definition for MR reconstruction Image enhancement Direct k-space to image mapping Physics-based reconstruction Core concepts: layers Convolution layer Dilated convolution Separable convolution Transposed convolution Normalization layer Activation layer Fully connected layer Down-sampling layer Up-sampling layer Dropout layer Merging layers Recursive layer Building blocks Data consistency layers Network architectures for MRI reconstruction How to build an ML model for MR reconstruction Checklist to build an ML model Database Database pipeline Frameworks Further resources and tutorials Exercises Hands-on examples A ML-specific notation B Complex calculus C Trainable parameters of separable convolutions Part Reconstruction Methods for Nonlinear Forward Models in MRI Imaging in the Presence of Magnetic Field Inhomogeneities Disruptions to the homogeneity of the magnetic field Field inhomogeneity effects on imaging Three types of effects disrupting the image and its information Field inhomogeneity and the signal equation Other basis expansions enable the modeling of additional artifacts Field inhomogeneity mitigation methods Image distortions and correction approaches Distortions depend on trajectory and sample timing Image correction: image warping approaches Image correction: conjugate phase Image correction: inverse problem approach Computational considerations Comparing performance of image correction approaches Phase and signal dephasing correction approaches Image reconstruction based approaches for within voxel dephasing K-space trajectory distortions Measuring the field map Motion-Corrected Reconstruction Theory Reconstruction with known motion: the particular case of translational motion Reconstruction with known motion: the general case Motion operators Forward acquisition model including motion operators Solving the inverse problem Conditioning of the system Joint reconstruction of image and motion Propagation of motion errors Alternating Gauss–Newton optimization Case of translational motion Case of a temporally constrained, nonrigid motion model Methods Strategies for motion sensing External sensor measurements Extracting motion from MR data Separate navigation signals Separate image navigation for D/D motion estimation Self-navigation signals Alternative MR navigation data Image registration Motion models Optimal k-space sampling for motion correction Motion correction to improve dynamic MRI Clinical application examples Brain Cardiovascular Body imaging (other than brain and heart) Current challenges and future directions Practical tutorial Chemical Shift Encoding-Based Water-Fat Separation Theory on chemical species separation The chemical shift property The chemical shift of fat Signal model for water–fat separation Solving the water–fat separation problem Parameter estimation in water–fat separation The field-map estimation problem Noise performance analysis Water–fat separation in non-Cartesian imaging Water–fat shift artifact Fat blurring in non-Cartesian acquisitions k-space-based water–fat separation Confounding factors in quantitative water–fat imaging Correction of hardware imperfections: gradient delays Correction of concomitant gradients Proton density fat-fraction determination Current challenges and future directions Further reading Model-Based Parametric Mapping Reconstruction MR mapping sequences Image-based mapping Reconstruction-based mapping Model-based acceleration of parameter mapping (MAP) Model-based optimization Clinical applications Current challenges and future directions Tutorial Image-based T mapping Problem description Provided material Questions Magnetic resonance fingerprinting Problem description Provided material Questions Quantitative Susceptibility-Mapping Reconstruction GRE data acquisition Phase pre-processing Dipole inversion COSMOS K-space reconstruction with closed-form solution Iterative reconstructions in image space Recent advances: single-step QSM and deep-learning-based QSM Summary and outlook A Tutorials Phase pre-processing Provided materials Exercises Dipole inversion Provided materials Exercises Total variation regularized single-step QSM (single-step TV) Provided materials Problem description Exercises A Linear Algebra Primer Vector spaces Linear independence Span Basis Normed space Inner product space Matrix theory Types of matrices Matrices with special structures Special matrix products Matrix decompositions Matrix norms Tensors Tensor properties Tensor products Tensor ranks Tensor decompositions Back Cover

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Akcakaya M. Magnetic Resonance Image Reconstruction. Theory,...App 2022.pdf 25.3 MB

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akcakaya m magnetic resonance image reconstruction theory app 2022