Advanced neuro MR techniques and applications /

Advanced Neuro MR Techniques and Applications gives detailed knowledge of emerging neuro MR techniques and their specific clinical and neuroscience applications, showing their pros and cons over conventional and currently available advanced techniques.

Bibliographic Details
Corporate Author:	ScienceDirect (Online service)
Other Authors:	Choi, In-Young, Jezzard, Peter
Format:	eBook
Language:	English
Published:	London ; San Diego, CA : Academic Press, an imprint of Elsevier, [2021]
Series:	Advances in magnetic resonance technology and applications ; 4.
Subjects:	Magnetic resonance imaging > Methodology. Brain > Imaging > Methodology. Imagerie par résonance magnétique > Méthodologie. Cerveau > Imagerie > Méthodologie. Electronic books.
Online Access:	Connect to the full text of this electronic book

Table of Contents:

Front Cover
Advanced Neuro MR Techniques and Applications
Copyright
Contents
List of contributors
Preface
Part 1 Fast and robust imaging
1 Recommendations for neuro MRI acquisition strategies
1.1 MRI hardware
1.2 From signals to biomarkers
1.3 Spatial encoding strategies
1.4 Large-scale population imaging
1.5 Example multi-purpose protocols
1.6 Acquisition of neuro MRI contrasts
1.6.1 Brain anatomy
1.6.2 Tissue microstructure
1.6.3 The brain at work and rest
1.6.4 Brain perfusion
1.6.5 Biophysical tissue properties
1.7 Conclusions and future prospects
References
2 Advanced reconstruction methods for fast MRI
2.1 Introduction to image reconstruction for fast MR imaging
2.2 Data acquisition for didactic example
2.3 Constrained reconstruction: partial Fourier acquisitions
2.3.1 Overview of partial Fourier imaging and the POCS algorithm
2.3.2 Didactic experiments for partial Fourier imaging
2.4 Parallel imaging
2.4.1 Overview of parallel imaging
2.4.2 Image space parallel imaging: SENSE
2.4.3 k-space parallel imaging: GRAPPA
2.4.4 Didactic experiments for parallel imaging
2.5 Compressed sensing and machine learning
2.5.1 Compressed sensing
2.5.2 Machine learning
2.6 Summary
Acknowledgments
References
3 Simultaneous multi-slice MRI
3.1 Historical overview
3.2 Implementation of SMS
3.2.1 Simultaneous slice excitation
3.2.2 Introducing relative spatial shifts
3.2.3 SMS image reconstruction
3.2.4 Coil sensitivity calibration
3.3 Current applications of SMS
3.4 Emerging applications and future outlook
Acknowledgments
References
Further reading
4 Motion artifacts and correction in neuro MRI
4.1 Introduction.
4.2 Establishing and maintaining a consistent brain anatomical coordinate system throughout a scan session
4.3 Impact of motion on MRI scans
4.3.1 Clinical impact
4.3.2 Research impact
4.3.3 Mitigating motion
4.4 Data quality and motion metrics
4.5 Retrospective correction methods
4.5.1 Classical approaches
4.5.2 Machine learning approaches
4.6 Methods of detecting motion and associated field changes in real time
4.6.1 Camera-based external motion trackers
4.6.2 Marker-based systems without cameras
4.6.3 Field cameras and probes
4.6.4 Navigators
4.6.4.1 Self-navigation
4.6.4.2 K-space navigators
4.6.4.3 Object-space navigators
4.6.4.4 Coil-space navigators
4.7 Prospective correction
4.8 Conclusion
References
Part 2 Classical and deep learning approaches to neuro image analysis
5 Statistical approaches to neuroimaging analysis
5.1 Linear model overview
5.1.1 Linear model: prediction compared to explanation
5.2 Estimating the parameters of the linear model
5.2.1 Bias and variance
5.2.2 Collinearity
5.3 Topics related to explanation
5.3.1 Contrast estimates
5.3.2 Inference
5.3.3 Multiple comparisons
5.3.4 Power
5.3.5 Efficiency
5.4 Topics related to prediction
5.4.1 Cross validation
5.4.2 Regularization
5.4.3 More advanced prediction models
References
6 Image registration
6.1 Introduction
6.2 Applications
6.3 Structure of image registration algorithms
6.4 Taxonomy of image registration algorithms
6.4.1 Classification based on transformation space
6.4.2 Classification based on similarity measure
6.4.3 Classification based on search strategy
6.5 Image registration with deep learning
References
7 Image segmentation
7.1 Introduction
7.2 Segmentation contexts: need, challenges and further application.
7.2.1 Total intracranial volume and brain segmentation
7.2.2 Tissue segmentation
7.2.3 Structure segmentation
7.2.4 Pathology segmentation
7.3 Approaches to automated segmentation
7.3.1 Thresholding methods
7.3.2 Atlas-based segmentation and label fusion
7.3.3 Edge-based methods
7.3.4 Clustering segmentation methods: mixture models, k-means and fuzzy clustering
7.3.5 Region-based methods
7.3.6 Feature-based methods
7.3.7 Hybrid methods / multi-sequence or multi-modal approaches
7.4 Longitudinal segmentation: challenge and approaches
7.5 Segmentation evaluation
7.5.1 Evaluation strategies
7.5.2 Ground truth and comparison to reference
7.6 Conclusion
References
Part 3 Diffusion MRI
8 Diffusion MRI acquisition and reconstruction
8.1 Introduction
8.2 SS-EPI DWI
8.3 Parallel imaging for DWI
8.4 Multi-shot EPI DWI
8.5 Image reconstruction for MS-EPI DWI
8.6 DWI with multi-band acquisitions
8.7 Point spread function EPI
8.8 3D diffusion imaging
8.9 Non-EPI diffusion imaging
8.10 Summary
Acknowledgments
References
9 Diffusion MRI artifact correction
9.1 Introduction
9.2 Distortions
9.2.1 Why are echo-planar images distorted?
9.2.1.1 In-plane acceleration (parallel imaging)
9.2.2 Susceptibility-induced distortions
9.2.3 Eddy current-induced distortions
9.2.4 Distortions are back in vogue
9.3 Subject movement
9.3.1 Gross movement
9.3.1.1 Movement within a volume (deck of slices)
9.3.2 Movement-induced signal loss
9.3.2.1 Special considerations for multi-band/simultaneous multi-slice
9.3.3 Movement interacting with other factors
9.3.3.1 Susceptibility-induced field
9.3.3.2 Receive coil inhomogeneity
9.4 Gradient non-linearities
9.5 Correcting the distortions
9.5.1 Difficulties specific to diffusion-weighted images.
9.5.2 How to estimate the susceptibility-induced field
9.5.2.1 Dual echo-time fieldmaps
9.5.2.2 Blip-up-blip-down fieldmaps
9.5.2.3 Estimating susceptibility-by-movement interaction
9.5.3 How to estimate the eddy current-induced field
9.5.3.1 How to represent the field
9.5.3.2 How to estimate the field
9.5.3.3 How to make the predictions
9.5.3.4 How to combine the two fields
9.5.4 ``Causal'' modeling of the eddy currents
9.6 Correcting subject movement
9.6.1 Rotating ``b-vecs''
9.6.2 Correcting movement within a volume (deck of slices)
9.6.3 Correcting movement-induced signal loss
9.7 What matters?
9.8 What have we not corrected?
Acknowledgments
References
10 Diffusion MRI analysis methods
10.1 Introduction
10.2 Analysis methods
10.2.1 Histogram analysis
10.2.2 Region-of-interest analysis
10.2.3 Voxel-wise analysis
10.2.4 Fiber tractography: tract-based analysis
10.2.5 Along-the-tract analysis
10.2.6 Connectome-based analysis
10.2.7 Fixel-based analysis
10.2.8 Tract geometry analysis
10.3 Conclusion
References
11 Diffusion as a probe of tissue microstructure
11.1 Diffusion MRI: sensitivity vs specificity
11.2 Restricted diffusion
11.3 Applications in resolving complex fiber architecture
11.4 Application in plasticity and functional imaging
11.5 AxCaliber
11.6 Summary
References
Further reading
Part 4 Perfusion MRI
12 Non-contrast agent perfusion MRI methods
12.1 Introduction
12.2 Arterial spin labeling
12.2.1 Labeling variants
12.2.2 Pulsed ASL (PASL)
12.2.3 Continuous ASL (CASL) and pseudo-continuous ASL (pCASL)
12.2.4 Velocity, acceleration-selective ASL
12.2.5 Background suppression
12.2.6 Image acquisition
12.2.7 Efficient acquisition of multiple inflow times
12.2.8 Consensus on ASL variants.
12.2.9 Biophysical modeling
12.2.10 Comments on ASL post-processing
12.3 Other non-contrast perfusion methods
References
13 Contrast agent-based perfusion MRI methods
13.1 Introduction
13.2 Signal derivation in contrast-based perfusion MRI
13.2.1 Biophysical properties of perfusion imaging
13.2.2 MR signal derivation
13.2.3 Current gadolinium concerns and dosing recommendations
13.3 Quantification of perfusion and permeability parameters
13.3.1 Quantitative perfusion parameters (CBF/CBV/MTT)
13.3.1.1 Theory
13.3.1.2 Analysis
13.3.2 Quantitative permeability parameters (Ktrans/ve/vp)
13.3.2.1 Theory
13.3.2.2 Analysis
13.3.3 Special considerations
13.4 Acquisition strategies
13.4.1 DSC acquisitions
13.4.2 DCE acquisitions
13.4.3 Advanced acquisition methods
13.5 Emerging methods
13.6 Supplementary material
References
14 Perfusion MRI: clinical perspectives
14.1 Introduction
14.2 Cerebrovascular diseases
14.2.1 Acute ischemic stroke
14.2.1.1 Core and penumbra
14.2.1.2 Target mismatch
14.2.1.3 Computed tomography vs magnetic resonance
14.2.1.4 Pitfalls and caveats
14.2.2 Cerebrovascular reserve
14.3 Vascular malformations and other shunting lesions
14.4 Neoplasms
14.4.1 Tumor grading
14.4.2 Molecular markers
14.4.3 Treatment response assessment
14.4.3.1 Pseudoprogression
14.4.3.2 Pseudoresponse
14.4.3.3 Radiation necrosis
14.4.4 Other brain tumors
14.4.4.1 Metastases
14.4.4.2 Primary CNS lymphoma
14.5 Miscellaneous conditions
14.6 Conclusions
References
Part 5 Functional MRI
15 Functional MRI principles and acquisition strategies
15.1 Introduction
15.2 The effect of neural activity on MR properties
15.2.1 Cerebrovascular response to neural activity
15.2.2 Impact on relaxation properties.