Advanced Neuro MR Techniques and Applications (Volume 4) (Advances in Magnetic Resonance Technology and Applications, Volume 4)

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
		15.3 Imaging the consequences of neural activity
			15.3.1 Imaging altered relaxation properties
			15.3.2 Key aspects of image formation
			15.3.3 Echo planar imaging (EPI)
				15.3.3.1 EPI artifacts
					Susceptibility-induced image distortion & signal dropout
					Phase-based trajectory correction
					Spatial specificity
		15.4 Applications
		15.5 Challenges and future directions
		15.6 Summary
		References
		Further reading
	16 Functional MRI analysis
		16.1 Types of fMRI
		16.2 Preprocessing
			16.2.1 Slice timing correction
			16.2.2 Motion correction
			16.2.3 Spatial smoothing
			16.2.4 Distortion correction
			16.2.5 Temporal filtering
			16.2.6 Physiological confounds
			16.2.7 Further data denoising
			16.2.8 Registration and normalization
			16.2.9 Quality control
		16.3 Statistical analysis
			16.3.1 Hypothesis vs data-driven analysis
			16.3.2 Univariate vs multivariate analysis
			16.3.3 Whole-brain vs regional analysis
			16.3.4 Subject-level vs group-level analysis
				16.3.4.1 Task fMRI
				16.3.4.2 Resting-state fMRI
		16.4 Communicating results
			16.4.1 Interpretations
			16.4.2 Visualization
			16.4.3 Open science
		References
		Further reading
	17 Neuroscience applications of functional MRI
		17.1 Introduction
		17.2 fMRI and neuroscience
			17.2.1 Historical perspective: brain damaged patients
		17.3 Functional localization
		17.4 Task-based fMRI
			17.4.1 Subtractive logic
			17.4.2 Parametric designs
			17.4.3 Adaptation studies
		17.5 Local vs focal
		17.6 Block vs event-related designs
			17.6.1 Block designs
			17.6.2 Event-related designs
		17.7 Resting-state fMRI
		17.8 Temporal resolution
		17.9 Ultra-high field (UHF) fMRI
		17.10 Conclusion
		References
	18 Clinical applications of functional MRI
		18.1 Introduction
		18.2 Surgical planning
			18.2.1 Non-lesional epilepsy
			18.2.2 Lesional pathologies
			18.2.3 Localizing seizure activity
		18.3 Non-neurosurgical applications
			18.3.1 Stroke outcome prediction
			18.3.2 Drug development
		18.4 Considerations for clinical fMRI
			18.4.1 Patient selection
			18.4.2 Sensitivity and task design
			18.4.3 Specificity: choosing the ``baseline\'\'
			18.4.4 To activate or not to activate… what is the question?
		18.5 Analyzing fMRI for clinical applications
			18.5.1 Single-subject analyses
			18.5.2 Impact of processing choices
			18.5.3 A note on laterality
			18.5.4 Validating fMRI
		18.6 Conclusion
		References
Part 6 The brain connectome
	19 The diffusion MRI connectome
		19.1 Introduction
		19.2 Mapping the structural connectome with diffusion MRI
		19.3 Inferring fiber orientations
		19.4 From fiber orientations to the connectome
		19.5 Quantifying connectivity strength
		19.6 Conclusions
		Acknowledgments
		References
	20 Functional MRI connectivity
		20.1 The promise of fMRI functional connectivity
			20.1.1 Defining functional connectivity
			20.1.2 Experimental approaches
			20.1.3 Interpreting functional connectivity
		20.2 Analysis and interpretation
			20.2.1 The functional connectivity processing pipeline
				20.2.1.1 Temporal and spatial filtering
				20.2.1.2 Motion correction
				20.2.1.3 Physiological noise regression
				20.2.1.4 Further denoising with ICA or global signal regression
			20.2.2 Representing functional connectivity
				20.2.2.1 Spatial representation
				20.2.2.2 Functional connectivity summary measures
				20.2.2.3 FC representation and statistical analysis
				20.2.2.4 Task manipulations
				20.2.2.5 Analyses derived from and extending functional connectivity
		20.3 Review of the functional connectome and its applications
			20.3.1 Structure of functional correlations
				20.3.1.1 Spontaneous activity and resting state networks
				20.3.1.2 Variation in FC within individuals
				20.3.1.3 Variability in FC across the population
				20.3.1.4 FC as a biomarker for disease-related brain changes
		20.4 Future directions
		Acknowledgments
		References
	21 Applications of MRI connectomics
		21.1 Introduction
		21.2 Impact of the connectome on cognitive processes and behavior
			21.2.1 Association of connectome features with cognitive abilities across subjects
				21.2.1.1 Structural connectome across individuals
				21.2.1.2 Phenotyping individuals based on their static functional connectome
				21.2.1.3 Time-varying dynamics of the functional connectome as traits
			21.2.2 Association of connectome features with cognitive states within subjects
				21.2.2.1 Structural connectome and learning
				21.2.2.2 Static functional connectome and cognitive states
				21.2.2.3 Time-varying dynamics of the functional connectome and behavioral variability
		21.3 The connectome across the lifespan
			21.3.1 Age-related within- and between-network connectivity changes
			21.3.2 Typical brain aging informs identification of pathological brain aging
		21.4 Clinical research applications of connectomics
			21.4.1 Connectomics reflects biology and therefore probably disease pathways
			21.4.2 Connectomics as tool for (differential) diagnosis
			21.4.3 Connectomics for prognosis and relationship with clinical scales
			21.4.4 Connectomics for treatment planning and response prediction
		21.5 Limitations for research and clinical translation
		21.6 Concluding remarks
		References
Part 7 Susceptibility MRI
	22 Principles of susceptibility-weighted MRI
		22.1 Introduction
		22.2 What is magnetic susceptibility?
		22.3 SWI pulse sequence considerations
		22.4 Phase information
		22.5 Phase aliasing and background fields
			22.5.1 Homodyne high-pass filter
		22.6 Phase mask and SWI processing
		22.7 Imaging parameters and acquisition time
		22.8 Non-contrast SWI vs MICRO SWI
		22.9 Pitfalls of SWI
		22.10 Differentiating calcium from iron
		22.11 High field SWI
		22.12 New approaches to SWI
		22.13 Quantitative susceptibility mapping (QSM)
		22.14 Conclusions
		References
	23 Applications of susceptibility-weighted imaging and mapping
		23.1 Introduction
		23.2 Applications of susceptibility-weighted imaging
			23.2.1 Microbleeds (MBs)
			23.2.2 Cerebral amyloid angiopathy (CAA)
			23.2.3 Cavernomas
			23.2.4 Traumatic brain injury (TBI)
			23.2.5 Cerebral venous sinus thrombosis
			23.2.6 Acute stroke
			23.2.7 Tumors
			23.2.8 Central veins
			23.2.9 Iron rims
		23.3 Applications of quantitative susceptibility mapping (QSM)
			23.3.1 Iron mapping
			23.3.2 Multiple sclerosis (MS)
			23.3.3 Neurodegenerative diseases
			23.3.4 Mapping of oxygen saturation and extraction
			23.3.5 Perfusion imaging
		References
Part 8 Magnetization transfer approaches
	24 Magnetization transfer contrast MRI
		24.1 Summary
		24.2 The magnetization transfer (MT) phenomenon and observations
			24.2.1 The MT experiment
			24.2.2 Biochemical origin of the MT effect
		24.3 Quantification of the MT effect
			24.3.1 Part 1: the MTR
			24.3.2 Quantification of the MT effect – part 2: quantitative MT (qMT)
				24.3.2.1 Pulsed MT
				24.3.2.2 Application of pulsed qMT
				24.3.2.3 Selective inversion recovery (SIR)
				24.3.2.4 Application of SIR qMT
		24.4 High field
			24.4.1 Pulsed MT
			24.4.2 Selective inversion recovery
		24.5 Conclusion
		References
	25 Chemical exchange saturation transfer (CEST) MRI as a tunable relaxation phenomenon
		25.1 Introduction and theoretical background
			25.1.1 Pulsed CEST
			25.1.2 CEST sequence scheme
		25.2 CEST effects in the human brain
			25.2.1 Quantitative model of CEST in the human brain
		25.3 CEST sequences and contrasts of the healthy and diseased human brain
			25.3.1 Readout
		25.4 Evaluation and artifacts – motion, normalization, B0, B1
			25.4.1 Motion correction & temporal SNR
			25.4.2 Normalization & reference values
			25.4.3 B0 and B1 correction
			25.4.4 CEST as the better MRS?
			25.4.5 Conclusion
		References
	26 Clinical application of magnetization transfer imaging
		26.1 Introduction
			26.1.1 Magnetization transfer-based MRI to improve pathological specificity
		26.2 Validation of MT imaging-derived metrics
			26.2.1 Histopathologic counterparts of MTR
				26.2.1.1 Animal models
				26.2.1.2 Postmortem human studies
				26.2.1.3 Summary remarks
			26.2.2 Histopathologic counterparts of the fraction of macromolecular protons and PSR
				26.2.2.1 Animal models
				26.2.2.2 Postmortem human studies
				26.2.2.3 Summary remarks
		26.3 MT imaging to understand and monitor neurological disease evolution
			26.3.1 Lessons learned from multiple sclerosis
				26.3.1.1 Summary remarks
			26.3.2 Lessons learned from normal brain development
				26.3.2.1 Summary remarks
			26.3.3 Exploiting the MT effect but not looking for myelin
				26.3.3.1 Summary remarks
		26.4 Why is MT imaging not part of routine clinical protocols?
		26.5 Conclusions
		Declaration of conflicts of interest
		Funding
		References
Part 9 Quantitative relaxometry and parameter mapping
	27 Quantitative relaxometry mapping
		27.1 Introduction
			27.1.1 Modeling
			27.1.2 Methods
		27.2 Modeling
			27.2.1 Transverse relaxation
			27.2.2 Longitudinal relaxation
		27.3 Methods
			27.3.1 T2 and T2*
			27.3.2 T1
		27.4 Conclusion and suggested readings
	28 MR fingerprinting: concepts, implementation and applications
		28.1 Introduction
		28.2 Basic framework of MRF
		28.3 Data acquisition
		28.4 Scan acceleration
		28.5 Dictionary generation
		28.6 Pattern recognition
		28.7 New promise for clinical translation
		28.8 Clinical applications
		28.9 New techniques and directions
		References
		Further reading
	29 Quantitative multi-parametric MRI measurements
		29.1 Introduction
		29.2 MRI sequences for multi-parametric brain mapping
			29.2.1 Spin-echo sequences
				29.2.1.1 Multi-parameter SE mapping
				29.2.1.2 Multiple T2 from spin-echoes
			29.2.2 Gradient-recalled echo sequences
				29.2.2.1 Multiple gradient-echoes
				29.2.2.2 Spoiled gradient-echoes
				29.2.2.3 Balanced steady-state free precession
				29.2.2.4 Combination of spoiled and balanced acquisition (DESPOT)
				29.2.2.5 Multi-parameter MP2RAGE
			29.2.3 Echo-planar imaging
			29.2.4 MR fingerprinting
			29.2.5 Bias correction, calibration, and PD
		29.3 Applications
			29.3.1 Synthetic MRI
			29.3.2 Tissue classification
			29.3.3 Biophysical models of microstructure
				29.3.3.1 Compartments
				29.3.3.2 Modeling relaxation by empirical relaxivities
				29.3.3.3 g-Ratio estimation
				29.3.3.4 Directional dependence
			29.3.4 Post-mortem MRI
		29.4 Discussion
		References
Part 10 Neurovascular imaging
	30 Neurovascular magnetic resonance angiography
		30.1 Introduction
		30.2 Macrovasculature of the brain
			30.2.1 Anatomy
			30.2.2 Blood flow
		30.3 Contrast methods
			30.3.1 Effect of motion on imaging
			30.3.2 Phase contrast MRA
			30.3.3 Inflow-based MRA
			30.3.4 Contrast-enhanced MRA
		30.4 Comparisons of techniques
		30.5 Summary and outlook
		References
		Further reading
	31 Neurovascular vessel wall imaging: new techniques and clinical applications
		31.1 Introduction
		31.2 Imaging technology
			31.2.1 Vessel wall imaging sequences
			31.2.2 Vessel wall imaging acceleration
			31.2.3 Vessel wall imaging analysis
		31.3 Current applications
			31.3.1 The carotid artery
		31.4 Intracranial arteries
			31.4.1 Intracranial atherosclerosis
			31.4.2 Cerebral aneurysms and post-aneurysm rupture
			31.4.3 Intracranial vasculopathy differentiation
		References
Part 11 Advanced magnetic resonance spectroscopy
	32 Single voxel magnetic resonance spectroscopy: principles and applications
		32.1 Introduction
		32.2 Acquisition techniques and calibration procedures
			32.2.1 Advanced localization techniques
				32.2.1.1 Ultra-short echo-time STEAM
				32.2.1.2 Short-echo, full intensity technique: semi-LASER
				32.2.1.3 Spectral editing techniques
			32.2.2 Water suppression techniques
				32.2.2.1 VAPOR water suppression
				32.2.2.2 1H MRS without water suppression
			32.2.3 Adjustment procedures and acquisition protocols
				32.2.3.1 Adjustment of B0 homogeneity
				32.2.3.2 Calibration of transmit B1+ field
				32.2.3.3 Adjustment of VAPOR power and timing
				32.2.3.4 1H MRS data acquisition
			32.2.4 Prospective motion correction
			32.2.5 Across-vendor standardization of sLASER
		32.3 Data processing and metabolite quantification
			32.3.1 MRS data preprocessing
			32.3.2 Spectral fitting and metabolite quantification
		32.4 Applications of advanced 1H MRS techniques
			32.4.1 Brain cancers
			32.4.2 Neurological and psychiatric diseases
			32.4.3 Metabolic diseases
			32.4.4 Functional MRS
		32.5 Conclusions
		Acknowledgments
		References
	33 Magnetic resonance spectroscopic imaging: principles and applications
		33.1 Introduction
		33.2 Principles & advanced techniques
			33.2.1 Acquisition & reconstruction
				33.2.1.1 Spatial localization
				33.2.1.2 MRSI encoding & decoding
			33.2.2 MRSI processing & quantification
			33.2.3 Advanced scanner hardware and ultra-high magnetic fields
			33.2.4 Multi-nuclear MRSI
			33.2.5 Spectrally-edited MRSI
			33.2.6 Emerging MRSI techniques
		33.3 Applications
			33.3.1 Clinical applications
				33.3.1.1 Brain tumors
				33.3.1.2 Epilepsy
				33.3.1.3 Demyelinating & neurodegenerative diseases
				33.3.1.4 Psychiatric disorders
			33.3.2 Advanced MRSI in neuroscience
		33.4 Conclusions & outlook
		References
	34 Non-Fourier-based magnetic resonance spectroscopy
		34.1 Introduction
		34.2 MRSI reconstruction using Fourier and non-Fourier approaches
			34.2.1 Basic concepts of MRSI reconstruction
			34.2.2 Reconstruction of MRSI using SLIM and SLOOP
			34.2.3 GSLIM solution for inhomogeneous compartments
			34.2.4 BASE-SLIM for B0 and B1 corrections
			34.2.5 Constrained and parameterized reconstruction concepts
			34.2.6 Spatiospectral correlation (SPICE)
		34.3 Conclusions
		References
Part 12 Ultra-high field neuro MR techniques
	35 Benefits, challenges, and applications of ultra-high field magnetic resonance
		35.1 Introduction
		35.2 Advantages and opportunities at ultra-high field
			35.2.1 Signal-to-noise ratio (SNR)
			35.2.2 Spectral resolution
				35.2.2.1 MR spectroscopy and spectroscopic imaging
				35.2.2.2 Chemical exchange saturation transfer (CEST)
			35.2.3 Changes in relaxation times and contrast
				35.2.3.1 T1-weighted imaging
				35.2.3.2 T2-weighted imaging
				35.2.3.3 T2*-weighted and phase imaging
		35.3 Challenges encountered at ultra-high field
			35.3.1 B0 inhomogeneity
			35.3.2 RF power and B1 inhomogeneity
				35.3.2.1 Challenges posed by SAR and heating
				35.3.2.2 Causes and challenges of B1 inhomogeneity
				35.3.2.3 Dealing with B1 inhomogeneity
					Non-pTx RF pulses
					Dielectric pads
					Parallel RF transmission (pTx)
					Static pTx - B1 shimming
					Dynamic pTx
					Calibration-free pTx
			35.3.3 Physiological effects
		35.4 Summary
		References
	36 Neuroscience applications of ultra-high-field magnetic resonance imaging: mesoscale functional imaging of the human brain
		36.1 Introduction
		36.2 Considerations for fMRI studies at UHF: imaging resolution and quality
		36.3 Relating functional MRI to neural activity: what is currently known?
		36.4 Prospects for ``mesoscale fMRI\'\' of cortical maps, columns, and layers
		36.5 Individual-focused neuroscience and single-subject fMRI at high fields
		36.6 What role should UHF fMRI play in modern neuroscience?
		36.7 Summary/conclusions
		Acknowledgments
		References
		Further reading
	37 Clinical applications of high field magnetic resonance
		37.1 Proton MRI/MRS at UHF
			37.1.1 High-resolution proton imaging at ultra-high field strength (>= 7 tesla)
				37.1.1.1 Clinical applications
				37.1.1.2 Brain anatomy
				37.1.1.3 Brain cancer
				37.1.1.4 Vascular imaging
				37.1.1.5 Neurodegenerative diseases
				37.1.1.6 Epilepsy
			37.1.2 Proton MRS
				37.1.2.1 Feasibility studies
				37.1.2.2 Brain tumors
				37.1.2.3 Other applications
			37.1.3 Quantitative exchange-label turnover (qELT) for measuring metabolic fluxes
			37.1.4 Functional MRI at ultra-high field strengths
			37.1.5 Chemical exchange saturation transfer (CEST)
				37.1.5.1 Clinical applications
			37.1.6 Dynamic glucose-enhanced (DGE) MRI
				37.1.6.1 Clinical applications
		37.2 X-nuclei imaging in metabolic and functional imaging
			37.2.1 Sodium-23 (23Na) MRI
				37.2.1.1 Multiple sclerosis
				37.2.1.2 Brain cancer
				37.2.1.3 Other applications in neuroimaging
			37.2.2 Dynamic oxygen-17 (17O) MRI
				37.2.2.1 Clinical applications
			37.2.3 Phosphorus-31 (31P) MR spectroscopy
				37.2.3.1 Clinical applications
			37.2.4 Other X-nuclei
				37.2.4.1 Exploring cellular homeostasis employing 35Cl and 39K MRI
				37.2.4.2 19F MRI for excellent specificity
				37.2.4.3 Perspectives for X-nuclei imaging
		37.3 Impact of UHF in clinical neuroimaging
		References
Index
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