Muutke küpsiste eelistusi

Quantitative Magnetic Resonance Imaging, Volume 1 [Pehme köide]

Edited by (Associate Professor, Department of Radiology, University of Michigan, Ann Arbor, USA), Edited by , Edited by , Edited by , Edited by , Edited by (MRI Technology Program, Cardiovascular Branch, Division of Intramural Research,), Edited by (Department of Radiology, University of Michigan, Ann Arbor, USA)
Teised raamatud teemal:
Teised raamatud teemal:

Quantitative Magnetic Resonance Imaging is a ‘go-to’ reference for methods and applications of quantitative magnetic resonance imaging, with specific sections on Relaxometry, Perfusion, and Diffusion. Each section will start with an explanation of the basic techniques for mapping the tissue property in question, including a description of the challenges that arise when using these basic approaches. For properties which can be measured in multiple ways, each of these basic methods will be described in separate chapters. Following the basics, a chapter in each section presents more advanced and recently proposed techniques for quantitative tissue property mapping, with a concluding chapter on clinical applications.

The reader will learn:

  • The basic physics behind tissue property mapping
  • How to implement basic pulse sequences for the quantitative measurement of tissue properties
  • The strengths and limitations to the basic and more rapid methods for mapping the magnetic relaxation properties T1, T2, and T2*
  • The pros and cons for different approaches to mapping perfusion
  • The methods of Diffusion-weighted imaging and how this approach can be used to generate diffusion tensor
  • maps and more complex representations of diffusion
  • How flow, magneto-electric tissue property, fat fraction, exchange, elastography, and temperature mapping are performed
  • How fast imaging approaches including parallel imaging, compressed sensing, and Magnetic Resonance
  • Fingerprinting can be used to accelerate or improve tissue property mapping schemes
  • How tissue property mapping is used clinically in different organs
  • Structured to cater for MRI researchers and graduate students with a wide variety of backgrounds
  • Explains basic methods for quantitatively measuring tissue properties with MRI - including T1, T2, perfusion, diffusion, fat and iron fraction, elastography, flow, susceptibility - enabling the implementation of pulse sequences to perform measurements
  • Shows the limitations of the techniques and explains the challenges to the clinical adoption of these traditional methods, presenting the latest research in rapid quantitative imaging which has the possibility to tackle these challenges
  • Each section contains a chapter explaining the basics of novel ideas for quantitative mapping, such as compressed sensing and Magnetic Resonance Fingerprinting-based approaches
Contributors xxvii
Section Introduction xxxv
Quantitative MRI: Rationale and Challenges xxxvii
Vikas Gulani
Nicole Seiberlich
MRI Biomarkers liii
Paul Hockings
Nadeem Saeed
Roslyn Simms
Nadia Smith
Matt G. Hall
John C. Waterton
Steven Sourbron
SECTION 1 Relaxometry
1(266)
Chapter 1 Biophysical and Physiological Principles of T1 and T2
3(16)
Sean Deoni
1.1 Introduction
3(1)
1.2 The biophysical basis of relaxation
4(6)
1.2.1 T2 relaxation
6(1)
1.2.2 T1 relaxation
7(2)
1.2.3 Mathematical formulation of relaxation
9(1)
1.3 Biophysical factors that influence relaxation
10(5)
1.3.1 Multicomponent relaxometry
11(2)
1.3.2 Microstructural orientation and magnetic susceptibility
13(2)
1.4 Summary
15(1)
References
16(3)
Chapter 2 Quantitative T1 and T1p Mapping
19(28)
Mathieu Boudreau
Kathryn E. Keenan
Nikola Stikov
2.1 Introduction
19(1)
2.2 Inversion recovery
20(5)
2.2.1 Signal modeling
20(1)
2.2.2 Data fitting
21(2)
2.2.3 Benefits and pitfalls
23(2)
2.2.4 Other saturation recovery T1 mapping techniques
25(1)
2.3 Variable flip angle
25(7)
2.3.1 Signal modeling
26(3)
2.3.2 Data fitting
29(1)
2.3.3 Benefits and pitfalls
30(2)
2.4 MP2RAGE
32(4)
2.4.1 Signal modeling
33(1)
2.4.2 Data fitting
34(1)
2.4.3 Benefits and pitfalls
35(1)
2.5 Tip mapping
36(4)
2.5.1 Signal modeling
37(2)
2.5.2 Data fitting
39(1)
2.5.3 Benefits and pitfalls
39(1)
2.6 Concluding remarks
40(1)
References
41(6)
Chapter 3 Quantitative T2 and T2* Mapping
47(18)
Richard D. Dortch
3.1 Introduction
47(1)
3.2 Spin-spin relaxation (T2) measurement sequences
48(8)
3.2.1 Single spin echo sequences
48(3)
3.2.2 Multiecho spin echo sequences
51(2)
3.2.3 T2-prepared sequences
53(1)
3.2.4 Unspoiled gradient echo sequences
54(1)
3.2.5 Model-based reconstructions
54(2)
3.3 Effective spin-spin relaxation (T2*) measurement sequences
56(2)
3.3.1 Single and multiecho spoiled gradient echo sequences
56(1)
3.3.2 Prospective correction of susceptibility-induced field gradients
56(1)
3.3.3 Retrospective correction of susceptibility-induced field gradients
57(1)
3.3.4 Asymmetric spin echo sequences
58(1)
3.4 Simultaneous T2 and T2 measurement sequences
58(2)
3.5 Approaches for estimating T2 and T2*
60(2)
3.5.1 Single-exponential models
60(1)
3.5.2 Multiexponential models
61(1)
3.6 Summary
62(1)
References
62(3)
Chapter 4 Multiproperty Mapping Methods
65(26)
Philipp Ehses
Rahel Heule
4.1 Simultaneous quantification of multiple relaxometry parameters
65(1)
4.2 Simultaneous quantification of T1, and T2
65(16)
4.2.1 Inversion recovery-bSSFP (IR-bSSFP, IR TrueFISP)
66(4)
4.2.2 Magnetic resonance fingerprinting
70(1)
4.2.3 Magnetization-prepared dual echo steady-state
70(2)
4.2.4 Triple-echo steady-state
72(4)
4.2.5 Phase-cycled bSSFP
76(5)
4.3 Simultaneous quantification of T1, and T2*
81(1)
4.3.1 Absolute quantification
81(1)
4.3.2 Quantification relative to baseline
81(1)
4.4 Simultaneous quantification of T2 and T2
82(3)
4.5 Common challenges in simultaneous relaxation time measurements
85(1)
4.6 Summary
85(2)
References
87(4)
Chapter 5 Specialized Mapping Methods in the Heart
91(32)
Gastao Cruz
Sebastien Roujol
Rene M. Botnar
Claudia Prieto
5.1 Introduction
91(1)
5.2 Cardiac T1, and extracellular volume mapping
92(9)
5.2.1 Inversion recovery-based T1 mapping
92(3)
5.2.2 Saturation recovery-based T1 mapping
95(2)
5.2.3 Combined inversion recovery and saturation recovery-based T1 mapping
97(1)
5.2.4 Cardiac extracellular volume mapping
98(1)
5.2.5 Novel developments in cardiac T1 mapping
99(2)
5.3 Cardiac T2 and T2 mapping
101(8)
5.3.1 T2 Vprepared T2 mapping
102(1)
5.3.2 Gradient and spin echo T2 mapping
103(1)
5.3.3 Comparison of T2 mapping techniques
104(2)
5.3.4 Cardiac T2* mapping
106(2)
5.3.5 Novel developments in cardiac T2 mapping
108(1)
5.4 Beyond single parameter mapping in the heart
109(6)
5.4.1 Joint T1-T2 mapping of the heart
110(1)
5.4.2 Cardiac magnetic resonance fingerprinting
111(3)
5.4.3 Cardiac Multitasking
114(1)
5.5 Concluding remarks
115(1)
References
116(7)
Chapter 6 Advances in Signal Processing for Relaxometry
123(26)
Noam Ben-Eliezer
6.1 Introduction
123(1)
6.2 Advanced signal models
124(7)
6.2.1 T2 mapping using the slice-resolved Extended Phase Graph formalism
124(2)
6.2.2 Bloch equation simulation-based signal models
126(1)
6.2.3 Multi-GRE-based relaxation mapping
127(1)
6.2.4 Confounding factors
128(3)
6.3 Advanced reconstruction of undersampled datasets
131(7)
6.3.1 Non-Cartesian data sampling
131(1)
6.3.2 Model-based reconstruction of undersampled relaxation mapping
132(1)
6.3.3 Compressed sensing (CS) and sparsity-driven reconstruction
133(5)
6.4 Identification of new signal motifs
138(5)
6.4.1 Magnetic Resonance Fingerprinting
139(2)
6.4.2 Subvoxel multicompartment relaxometry
141(2)
6.5 Concluding remarks
143(1)
References
144(5)
Chapter 7 Relaxometry: Applications in the Brain
149(36)
Alex L. MacKay
Cornelia Laule
7.1 Introduction
149(1)
7.2 Overview of the brain
149(1)
7.3 T1 in brain
150(3)
7.3.1 Measuring T1 in brain
150(1)
7.3.2 Physiological influences of T1 in brain
150(2)
7.3.3 Single or multiple T1, components?
152(1)
7.3.4 Interpreting T1 in the brain
153(1)
7.4 Clinical applications of T1 relaxation
153(4)
7.4.1 Development and aging
153(2)
7.4.2 Multiple sclerosis
155(1)
7.4.3 Parkinson's disease
156(1)
7.4.4 Brain cancer and radiation
156(1)
7.4.5 Other applications
157(1)
7.5 T2 in brain
157(3)
7.5.1 Measuring multicomponent T2 in brain
157(2)
7.5.2 Physiological influences of T2 in brain
159(1)
7.5.3 Interpreting T2 in the brain
160(1)
7.6 Clinical applications of T2 relaxation
160(7)
7.6.1 Development and aging
160(1)
7.6.2 Developmental and genetic disorders
161(2)
7.6.3 Multiple sclerosis
163(2)
7.6.4 Alzheimer's disease
165(1)
7.6.5 Epilepsy
165(1)
7.6.6 Cancer
165(1)
7.6.7 Other diseases
165(2)
7.7 Tip in brain
167(2)
7.7.1 Measuring T1p, in brain
167(2)
7.7.2 Interpreting T1p in brain and clinical applications
169(1)
7.8 T2* in brain
169(1)
7.8.1 Measuring T2 in brain
169(1)
7.8.2 Interpreting T2 in brain and clinical applications
169(1)
7.9 Challenges with clinical application of relaxation
170(1)
7.10 Concluding remarks
171(1)
Acknowledgments
171(1)
References
171(14)
Chapter 8 Relaxometry: Applications in Musculoskeletal Systems
185(30)
Xiaojuan Li
Carl S. Winalski
8.1 Introduction
185(1)
8.2 MRI relaxometry of cartilage
185(9)
8.2.1 Cartilage biochemistry and degeneration
185(2)
8.2.2 Post-contrast T1 relaxation time mapping with delayed gadolinium-enhanced MRI of cartilage
187(2)
8.2.3 T2 and T2* relaxation time mapping in cartilage
189(2)
8.2.4 T1p relaxation time mapping of cartilage
191(3)
8.3 MRI relaxometry to assess skeletal muscle
194(3)
8.4 MRI relaxometry of menisci, tendons, and ligaments
197(2)
8.5 MRI relaxometry of intervertebral discs
199(3)
8.6 Outlook and Conclusion
202(1)
Acknowledgments
203(1)
References
203(12)
Chapter 9 Relaxometry: Applications in the Body
215(24)
Jonathan R. Dillman
Andrew T. Trout
Jean A. Tkach
9.1 Introduction
215(1)
9.2 Liver
215(10)
9.3 Spleen
225(2)
9.4 Kidneys
227(1)
9.5 Pancreas
228(4)
9.6 Prostate
232(1)
9.7 Breast
232(3)
9.8 Challenges
235(1)
References
235(4)
Chapter 10 Relaxometry: Applications in the Heart
239(28)
Erica Dall `Armellina'
Arka Das
10.1 Introduction
239(2)
10.2 Acute chest pain syndromes
241(3)
10.2.1 Myocarditis
241(2)
10.2.2 Takostubo
243(1)
10.2.3 Ischemic chest pain and nonobstructive coronary artery disease
243(1)
10.3 Acute myocardial infarction
244(3)
10.3.1 ST-elevation myocardial infarction
244(2)
10.3.2 Non-ST elevation myocardial infarction (NSTEMI)
246(1)
10.4 Chronic stable coronary artery disease
247(1)
10.5 Cardiomyopathy
248(2)
10.5.1 Hypertrophic cardiomyopathy
248(1)
10.5.2 Amyloidosis
249(1)
10.5.3 Anderson-Fabry disease
249(1)
10.5.4 Dilated cardiomyopathy
250(1)
10.5.5 Iron overload cardiomyopathy
250(1)
10.6 Systemic inflammatory diseases
250(1)
10.7 Valve disease
251(1)
10.7.1 Aortic stenosis
251(1)
10.7.2 Mitral regurgitation
251(1)
10.8 Heart failure with preserved ejection fraction
252(1)
10.9 Heart transplant
252(1)
10.10 Conclusions
253(1)
References
253(14)
SECTION 2 Perfusion and Permeability
267(188)
Chapter 11 Physical and Physiological Principles of Perfusion and Permeability
269(26)
Stig P. Cramer
Mark B. Vestergaard
Ulrich Lindberg
Henrik B.W. Larsson
11.1 Introduction to perfusion and permeability
269(1)
11.2 Perfusion and vascular anatomy in different tissues and organs
270(2)
11.2.1 The brain
270(1)
11.2.2 The heart
271(1)
11.2.3 The liver
271(1)
11.2.4 The kidneys
271(1)
11.2.5 Perfusion and permeability in disease
271(1)
11.3 MRI signal and tracer agents
272(3)
11.3.1 Safety of MR contrast agents
272(1)
11.3.2 Relaxivity of contrast agents
273(1)
11.3.3 Measuring contrast agent concentration
274(1)
11.4 Basic tracer kinetics
275(5)
11.4.1 Linear and stationary tissues
276(1)
11.4.2 The transit time distribution
277(3)
11.4.3 The central volume theorem
280(1)
11.5 Compartment models
280(5)
11.5.1 One-compartment model
281(1)
11.5.2 Two-compartment exchange model
282(3)
11.5.3 The Patlak model
285(1)
11.6 Model-free perfusion quantification
285(7)
11.7 Conclusion
292(1)
References
292(3)
Chapter 12 Arterial Spin Labeling MRI: Basic Physics, Pulse Sequences, and Modeling
295(26)
Susan Francis
12.1 Introduction
295(1)
12.2 Basic physics
295(2)
12.3 ASL labeling schemes
297(5)
12.3.1 Pulsed ASL
297(2)
12.3.2 Continuous ASL
299(1)
12.3.3 Pseudo-continuous ASL
299(2)
12.3.4 Velocity selective ASL
301(1)
12.4 Sampling strategies
302(2)
12.4.1 Single-TI and multi-TI sampling
302(1)
12.4.2 Multiphase or Look-Locker (LL) sampling strategy
302(1)
12.4.3 Time-encoded multi-PLD
302(2)
12.5 Readout scheme
304(1)
12.6 Improving the signal-to-noise ratio of ASL data
304(4)
12.6.1 Pre- and postsaturation schemes
304(2)
12.6.2 Background suppression
306(1)
12.6.3 Vascular crushing
306(2)
12.7 Preprocessing ASL data
308(1)
12.7.1 Subtraction methods
308(1)
12.7.2 Motion correction
308(1)
12.7.3 Outlier detection
309(1)
12.7.4 Partial volume effects
309(1)
12.8 Modeling the ASL signal
309(4)
12.8.1 Single compartment model using the modified Bloch equations
309(1)
12.8.2 General kinetic model
310(3)
12.9 Perfusion quantification
313(3)
12.9.1 Perfusion quantification using data collected at a single TI/PLD
313(1)
12.9.2 Perfusion quantification using multi-TI/PLD data
313(1)
12.9.3 Perfusion quantification using multiphase or Look-Locker sampling
314(1)
12.9.4 Comparison of methods of perfusion quantification
315(1)
12.10 Applications of ASL
316(1)
12.11 Summary
317(1)
References
317(4)
Chapter 13 Dynamic Contrast-Enhanced MRI: Basic Physics, Pulse Sequences, and Modeling
321(24)
Ye Tian
Ganesh Adluru
13.1 Introduction
321(1)
13.2 Contrast agent mechanism
321(4)
13.3 Data acquisition in DCE-MRI
325(5)
13.3.1 Requirements of DCE-MRI data
325(2)
13.3.2 DCE-MRI pulse sequences
327(2)
13.3.3 Sampling trajectories
329(1)
13.3.4 Arterial input function
330(1)
13.4 Image reconstruction in DCE-MRI
330(4)
13.4.1 Parallel imaging
330(2)
13.4.2 Constrained reconstruction
332(2)
13.5 Postprocessing for DCE-based perfusion mapping
334(5)
13.5.1 Motion compensation
334(1)
13.5.2 ROI-based and pixel-based methods
335(3)
13.5.3 Modeling
338(1)
13.6 Summary
339(1)
References
340(5)
Chapter 14 Dynamic Susceptibility Contrast MRI: Basic Physics, Pulse Sequences, and Modeling
345(24)
Endre Grovik
Atle Bjornerud
Kyrre Eeg Emblem
14.1 Introduction
345(1)
14.2 Biophysical foundations
346(2)
14.2.1 Dose-response in DSC-MRI
346(1)
14.2.2 Transverse relaxivity of contrast agents in vivo
347(1)
14.3 DSC-MRI data acquisition
348(5)
14.3.1 Current recommendations
348(2)
14.3.2 Susceptibility artifacts
350(2)
14.3.3 Non-GBCA tracers
352(1)
14.4 DSC-MRI data analysis for perfusion mapping
353(7)
14.4.1 Forward models
353(2)
14.4.2 Deconvolution
355(1)
14.4.3 Delay effects
356(1)
14.4.4 Contrast agent extravasation considerations
357(1)
14.4.5 Arterial input function determination
358(2)
14.5 Advanced DSC-MRI methods
360(1)
14.6 DSC-MRI beyond the brain
361(1)
14.7 Conclusion
361(1)
Acknowledgment
362(1)
References
362(7)
Chapter 15 Applications of Quantitative Perfusion and Permeability in the Brain
369(36)
Shalini Amukotuwa
Laura C. Bell
David L. Thomas
15.1 Introduction
369(1)
15.2 Applications of perfusion MRI in ischemic cerebrovascular disease
370(9)
15.2.1 Acute ischemic stroke
370(6)
15.2.2 Chronic steno-occlusive disease
376(3)
15.3 Applications of perfusion MRI in brain cancer
379(9)
15.3.1 Introduction
379(3)
15.3.2 Diagnosis and neurosurgery
382(4)
15.3.3 Evaluation of tumor progression and treatment response
386(2)
15.4 Applications of perfusion MRI in dementia
388(7)
15.4.1 Background
388(3)
15.4.2 Diagnosis
391(1)
15.4.3 Longitudinal monitoring of disease progression
391(1)
15.4.4 Limitations of ASL in neurodegenerative disease
391(4)
15.5 Summary
395(1)
References
396(9)
Chapter 16 Applications of Quantitative Perfusion and Permeability in the Liver
405(22)
Maxime Ronot
Florian Joly
Bernard E. Van Beers
16.1 Introduction
405(1)
16.2 The technical challenges of liver perfusion analysis
406(4)
16.2.1 Image acquisition
406(1)
16.2.2 Liver perfusion modeling
406(3)
16.2.3 Correcting for liver movement
409(1)
16.2.4 Reproducibility of liver perfusion measurements
409(1)
16.3 Clinical applications
410(5)
16.3.1 Liver oncology
410(3)
16.3.2 Chronic liver disease
413(2)
16.4 Perfusion mapping with hepatobiliary contrast agents
415(3)
16.5 Alternative methods to assess liver perfusion
418(1)
16.6 Summary
419(1)
Acknowledgment
420(1)
References
420(7)
Chapter 17 Applications of Quantitative Perfusion and Permeability in the Body
427(28)
Yong Chen
Muhummad Sohaib Nazir
Sebastian Kozerke
Sven Plein
Shivani Pahwa
17.1 Introduction
427(1)
17.2 Renal perfusion
428(5)
17.2.1 Background
428(1)
17.2.2 Technical developments
428(4)
17.2.3 Clinical applications
432(1)
17.3 Pancreatic perfusion
433(3)
17.3.1 Background
433(1)
17.3.2 Technical developments
433(2)
17.3.3 Clinical applications
435(1)
17.4 Prostate perfusion
436(2)
17.4.1 Background
436(1)
17.4.2 Technical developments
437(1)
17.4.3 Clinical applications
438(1)
17.5 Breast perfusion
438(3)
17.5.1 Background
438(1)
17.5.2 Technical developments
439(1)
17.5.3 Clinical applications
439(2)
17.6 Cardiac perfusion
441(5)
17.6.1 Background
441(1)
17.6.2 Technical developments
442(2)
17.6.3 Clinical applications
444(2)
17.7 Summary
446(1)
References
446(9)
SECTION 3 Diffusion
455(210)
Chapter 18 Physical and Physiological Principles of Diffusion
457(20)
Christopher D. Kroenke
18.1 Introduction
457(1)
18.2 Diffusion
458(3)
18.3 The Stejskal-Tanner pulse sequence: A magnetic resonance method for measuring diffusion
461(3)
18.4 Diffusion in biological tissue
464(1)
18.5 Intravoxel incoherent motion
465(1)
18.6 Using anisotropy in water diffusion to characterize cellular anatomy
466(3)
18.7 Beyond FA and MD
469(1)
18.8 Sensitivity of diffusion to cellular physiology and metabolism
470(3)
18.9 Conclusions
473(1)
References
474(3)
Chapter 19 Acquisition of Diffusion MRI Data
477(32)
Grant Yang
Jennifer A. McNab
19.1 Introduction
477(1)
19.2 Diffusion encoding strategies
477(7)
19.2.1 Single-pulsed gradients
478(1)
19.2.2 The diffusion propagator
479(1)
19.2.3 Oscillating gradients and time-dependent diffusion
480(1)
19.2.4 Multiple-pulsed gradients
481(1)
19.2.5 Generalized diffusion waveforms
482(2)
19.3 Refocusing mechanisms
484(3)
19.4 Image encoding
487(6)
19.4.1 Single-shot 2D echo-planar imaging
488(2)
19.4.2 Multishot EPI
490(1)
19.4.3 Alternatives to EPI
491(1)
19.4.4 Acceleration methods
492(1)
19.5 Hardware considerations and system limitations
493(2)
19.6 Diffusion mapping outside the brain
495(2)
19.6.1 Cardiac imaging
495(1)
19.6.2 Spinal cord
496(1)
19.6.3 Peripheral nervous system
496(1)
19.6.4 Breast
496(1)
19.6.5 Musculoskeletal
497(1)
19.7 Summary
497(1)
Acknowledgments
497(1)
References
497(12)
Chapter 20 Modeling Fiber Orientations Using Diffusion MRI
509(24)
Daan Christiaens
J. Donald Tournier
20.1 Introduction
509(2)
20.2 q-Space imaging
511(1)
20.3 Diffusion tensor imaging
511(3)
20.3.1 Geometric interpretation
512(1)
20.3.2 DTI metrics and DEC-FA
513(1)
20.4 Toward modeling crossing fibers: Higher-order signal representations
514(1)
20.4.1 Diffusion kurtosis imaging
514(1)
20.4.2 Multi-tensor models
515(1)
20.5 High-angular resolution diffusion imaging
515(4)
20.5.1 Spherical Harmonic decomposition
516(1)
20.5.2 g-ball imaging
517(2)
20.6 Multifascicle compartment models
519(1)
20.7 Spherical deconvolution
519(5)
20.7.1 The spherical convolution model
520(1)
20.7.2 Deconvolution and constraints
521(1)
20.7.3 Response function estimation
522(1)
20.7.4 Multi-tissue spherical deconvolution
523(1)
20.8 Validation of fiber orientation estimation
524(1)
20.9 Conclusions
525(1)
References
526(7)
Chapter 21 Diffusion MRI Fiber Tractography
533(38)
Robert Elton Smith
Alan Connelly
Fernando Calamante
21.1 Introduction
533(1)
21.2 Streamline tractography
533(15)
21.2.1 Method summary
533(10)
21.2.2 Deterministic vs probabilistic algorithms
543(3)
21.2.3 Targeted tracking/virtual dissection vs whole-brain fiber tracking
546(2)
21.3 Nonstreamline tractography
548(3)
21.3.1 Front evolution tractography
548(1)
21.3.2 Geodesic tractography
548(1)
21.3.3 Global tractography
548(2)
21.3.4 Voxel-constrained tractography algorithms
550(1)
21.4 Quantification
551(8)
21.4.1 Streamline-based connection densities
552(4)
21.4.2 Other quantitative methods involving tractography
556(3)
21.5 Conclusions
559(1)
References
559(12)
Chapter 22 Measuring Microstructural Features Using Diffusion MRI
571(34)
Noam Shemesh
22.1 Introduction
571(2)
22.1.1 What is the motivation for measuring microstructure using diffusion MRI?
571(1)
22.1.2 What is "microstructure"?
572(1)
22.2 Directly imaging intrinsic microstructural features
573(6)
22.2.1 The pore density function
573(4)
22.2.2 The averaged propagator
577(1)
22.2.3 The diffusion spectrum
577(2)
22.3 Strategies for indirect quantification of microstructure
579(15)
22.3.1 Single diffusion encoding signal representations
581(10)
22.3.2 Tissue models
591(1)
22.3.3 Biophysical models
592(2)
22.4 Frontiers of microstructural MRI
594(1)
22.5 Summary
595(1)
Acknowledgments
596(1)
References
596(9)
Chapter 23 Diffusion MRI: Applications in the Brain
605(32)
Marco Bozzali
Andrew W. Barritt
Laura Serra
23.1 Introduction
605(1)
23.2 Clinical applications of quantitative diffusion mapping
605(9)
23.3 Research applications of diffusion imaging
614(14)
23.3.1 Multiple sclerosis (MS)
614(3)
23.3.2 Amyotrophic lateral sclerosis (ALS)
617(3)
23.3.3 Dementia
620(5)
23.3.4 Psychiatric disorders
625(3)
23.4 Summary
628(1)
Conflict of interest
628(1)
Funding
628(1)
References
628(9)
Chapter 24 Diffusion MRI: Applications Outside the Brain
637(28)
Ricardo Donners
Mihaela Rata
Neil Peter Jerome
Matthew Orton
Matthew Blackledge
Christina Messiou
Dow-Mu Koh
24.1 Introduction
637(1)
24.2 Principles of diffusion imaging as applied in the body
637(5)
24.2.1 General considerations
637(1)
24.2.2 Technical considerations
638(4)
24.3 Quantitative diffusion mapping
642(4)
24.3.1 Quantitative measurements using the monoexponential model
642(2)
24.3.2 Nonmonoexponential quantitative measurements
644(2)
24.3.3 Whole-body quantitative diffusion measurements: Diffusion volume, global ADC
646(1)
24.4 Clinical applications
646(8)
24.4.1 Breast cancer
646(3)
24.4.2 Head and neck cancers
649(1)
24.4.3 Focal liver lesions
649(1)
24.4.4 Focal pancreatic lesions
650(1)
24.4.5 Renal masses and renal function
651(1)
24.4.6 Gynecological tumors
652(1)
24.4.7 Prostate cancer
653(1)
24.4.8 Bone marrow disease: Myeloma, bone metastases
653(1)
24.5 Future developments
654(1)
References
655(10)
SECTION 4 Fat and Iron Quantification
665(140)
Chapter 25 Physical and Physiological Properties of Fat
667(14)
Shigeki Sugii
S. Sendhil Velan
25.1 Introduction
667(1)
25.2 Molecular and cellular characteristics of adipose tissue
667(2)
25.3 Adipocyte functions
669(2)
25.4 Fat distribution in humans
671(2)
25.5 Pathophysiological association
673(1)
25.6 Magnetic resonance of adipose tissues
674(1)
25.7 Summary
675(1)
References
676(5)
Chapter 26 Physical and Physiological Properties of Iron
681(14)
Suraj D. Serai
Hansel J. Otero
Janet L. Kwiatkowski
26.1 Introduction
681(1)
26.2 The form and function of iron in the body
681(1)
26.3 Iron balance
682(3)
26.3.1 Absorption
684(1)
26.3.2 Excretion
685(1)
26.3.3 Deposition
685(1)
26.4 Iron deficiency
685(1)
26.5 Iron overload
686(1)
26.5.1 Causes
686(1)
26.5.2 Iron toxicity
686(1)
26.6 Iron accumulation in the liver
687(1)
26.7 Iron accumulation in the heart
688(1)
26.8 Iron accumulation in other organs
689(1)
26.8.1 Effect of iron overload in hormone-producing endocrine glands
689(1)
26.8.2 Effect of iron overload in the brain
689(1)
26.9 Management of patients with iron overload
690(1)
26.10 Summary
691(1)
References
691(4)
Chapter 27 Fat Quantification Techniques
695(40)
Tess Armstrong
Holden H. Wu
27.1 MR properties of fat
695(4)
27.1.1 T1, and T2 of fat
695(1)
27.1.2 Chemical shift of fat
696(2)
27.1.3 Diffusion of fat
698(1)
27.2 Quantifying fat using MR spectroscopy
699(4)
27.2.1 Signal model
699(1)
27.2.2 MRS pulse sequences
700(2)
27.2.3 Signal fitting
702(1)
27.2.4 Quantitative measurements
703(1)
27.3 Quantifying fat using chemical-shift-encoded MRI
703(14)
27.3.1 Fat-water separation using CSE-MRI
704(7)
27.3.2 CSE-MRI pulse sequences
711(1)
27.3.3 Fat quantification using CSE-MRI
711(6)
27.3.4 Standardized phantoms
717(1)
27.4 Emerging CSE-MRI techniques for fat quantification
717(9)
27.4.1 Free-breathing Cartesian CSE-MRI fat quantification techniques
717(4)
27.4.2 Free-breathing non-Cartesian CSE-MRI fat quantification techniques
721(3)
27.4.3 Advanced CSE-MRI reconstruction and signal modeling
724(2)
27.5 Summary and outlook
726(1)
References
726(9)
Chapter 28 Applications of Fat Mapping
735(44)
Hermien E. Kan
Dimitrios C. Karampinos
Jurgen Machann
28.1 Introduction
735(2)
28.2 Quantification of abdominal adipose tissue
737(10)
28.2.1 Subcutaneous and visceral adipose tissue
737(5)
28.2.2 Applications and trends
742(5)
28.3 Current applications of fat quantification in organs
747(9)
28.3.1 Liver
747(4)
28.3.2 Heart, pancreas, kidneys, and brown adipose tissue
751(5)
28.4 Current applications of fat quantification in skeletal muscle and bone marrow
756(7)
28.4.1 IMCL and EMCL in skeletal muscle
756(3)
28.4.2 Fat infiltration in skeletal muscle
759(3)
28.4.3 Bone marrow adipose tissue
762(1)
28.5 Future directions and unmet needs
763(1)
References
764(15)
Chapter 29 Iron Mapping Techniques and Applications
779(26)
Ralf B. Loeffler
Samir D. Sharma
Claudia M. Hillenbrand
29.1 Introduction
779(1)
29.1.1 Liver
779(1)
29.1.2 Other organs
780(1)
29.2 R2*-based iron quantification
780(8)
29.2.1 Liver
780(7)
29.2.2 Other organs
787(1)
29.3 R2-based iron quantification
788(1)
29.4 Quantitative susceptibility mapping
788(6)
29.4.1 Introduction
788(2)
29.4.2 Data acquisition
790(1)
29.4.3 Magnetic susceptibility reconstruction
790(3)
29.4.4 Postprocessing
793(1)
29.4.5 Applications of QSM for iron mapping
793(1)
29.5 Future directions
794(2)
29.6 Summary
796(1)
References
796(9)
SECTION 5 Quantification of Other MRI-Accessible Tissue Properties
805(174)
Chapter 30 Electrical Properties Mapping
807(12)
Ulrich Katscher
30.1 Electrical properties: Physical and physiological background
807(1)
30.2 Development of EPT
808(1)
30.3 Physical/mathematical background of EPT
809(1)
30.4 EPT measurement methods
810(1)
30.5 Reconstruction algorithms
811(3)
30.6 Limitations and challenges of EPT
814(1)
30.7 Clinical applications
814(1)
30.8 Summary
815(1)
Acknowledgments
815(1)
References
815(4)
Chapter 31 Quantitative Susceptibility Mapping
819(20)
Karin Shmueli
31.1 Physical principles of susceptibility and MRI phase
819(2)
31.1.1 Susceptibility: What and why?
819(1)
31.1.2 The relationship between magnetic susceptibility and MRI phase
819(2)
31.2 Imaging methodology and image processing pipeline
821(4)
31.3 Practical considerations and limitations of QSM techniques
825(1)
31.4 Sources of susceptibility contrast and clinical applications of QSM
826(3)
31.4.1 Tissue iron--Deep-brain structures and dementia
827(1)
31.4.2 Deoxyhemoglobin and blood iron--Brain oxygenation and microvascular disease
828(1)
31.4.3 Myelin--Demyelination and microstructure
828(1)
31.4.4 Other applications
828(1)
Acknowledgment
829(1)
References
829(10)
Chapter 32 Magnetization Transfer
839(18)
Tobias C. Wood
Shaihan J. Malik
32.1 Introduction
839(1)
32.2 Modeling MT effects
840(3)
32.3 Effects of MT on relaxometry
843(5)
32.3.1 Inversion recovery measurement of Rt
844(1)
32.3.2 Spin echo measurement of R2
845(1)
32.3.3 Steady-state measurements of R1 and R2
845(3)
32.3.4 Outlook: Relaxometry in the presence of MT effects
848(1)
32.4 Quantitative MT approaches
848(3)
32.4.1 Continuous wave Z-spectrum measurements
849(1)
32.4.2 Pulsed measurements
849(2)
32.4.3 Inversion or saturation recovery methods
851(1)
32.5 CEST
851(1)
32.6 Concluding remarks
852(1)
References
852(5)
Chapter 33 Chemical Exchange Mapping
857(28)
Zhongliang Zu
Moriel Vandsburger
Phillip Zhe Sun
33.1 Principles of chemical exchange saturation transfer
857(3)
33.1.1 Bloch-McConnell equations
858(1)
33.1.2 Spinlock theory
859(1)
33.1.3 Exchange metrics and parameters
859(1)
33.2 CEST MRI pulse sequences and data collection
860(1)
33.2.1 Basic CEST MRI sequence design
860(1)
33.2.2 Collection of CEST MRI data
861(1)
33.3 CEST data analysis
861(5)
33.3.1 MTR asymmetry analysis
861(3)
33.3.2 Lorentzian fitting
864(1)
33.3.3 R1w.-scaled inverse analysis
865(1)
33.4 Exchange rate mapping
866(3)
33.4.1 qCEST with saturation time and power dependency
866(1)
33.4.2 Omega plot and generalized omega plot analysis
867(1)
33.4.3 Ratiometric analysis
868(1)
33.5 CEST contrast agents
869(3)
33.5.1 Diamagnetic CEST agents
870(1)
33.5.2 paraCEST MRI
871(1)
33.6 Applications
872(3)
33.6.1 pH-sensitive APT imaging of acute stroke
872(3)
33.6.2 CEST imaging of tumor
875(1)
33.7 Practical limitations
875(2)
33.7.1 SNR efficiency
876(1)
33.7.2 Field inhomogeneity correction
876(1)
33.7.3 The specificity of CEST measurement
877(1)
33.8 Summary
877(1)
References
877(8)
Chapter 34 MR Thermometry
885(22)
Bruno Madore
34.1 Introduction
885(1)
34.2 MR thermometry: Theory
885(1)
34.3 Proton resonance frequency (PRF) thermometry
886(1)
34.4 Anatomy of traditional PRF thermometry pulse sequences
887(5)
34.5 Improving upon the traditional design
892(4)
34.6 TNR-optimum combination of multiple signals
896(1)
34.7 Dealing with motion and other confounders
897(1)
34.8 Limits of PRF thermometry
898(1)
34.9 Applications and future directions
899(2)
34.10 Conclusion
901(1)
Acknowledgments
901(1)
References
901(6)
Chapter 35 Motion Encoded MRI and Elastography
907(24)
Prashant P. Nair
Yogesh K. Mariappan
35.1 O Motion! Where art thou?
907(1)
35.2 Motion encoding
907(5)
35.2.1 Phase-contrast MRI
907(1)
35.2.2 Spatial modulation of magnetization
908(1)
35.2.3 Displacement encoding with stimulated echoes
909(1)
35.2.4 Strain encoding
910(2)
35.2.5 Applications
912(1)
35.3 Magnetic Resonance Elastography
912(13)
35.3.1 MRE Step 1: introduce shear vibrations into the tissue of interest
914(1)
35.3.2 MRE Step 2: imaging of shear wave propagation
915(1)
35.3.3 MRE Step 3: Quantification of mechanical parameters
916(2)
35.3.4 Applications of MR elastography
918(7)
35.4 Conclusion
925(1)
References
925(6)
Chapter 36 Flow Quantification with MRI
931(22)
Jacob A. Macdonald
Oliver Wieben
36.1 Introduction
931(1)
36.2 Principles of flow encoding
931(6)
36.2.1 One-directional flow encoding
931(3)
36.2.2 Two-directional & three-directional flow encoding
934(2)
36.2.3 4D flow MRI
936(1)
36.3 Phase contrast methodology
937(10)
36.3.1 Acquisition
937(3)
36.3.2 Reconstruction & visualization
940(2)
36.3.3 Hemodynamic analysis
942(2)
36.3.4 Confounding factors and artifacts
944(3)
36.4 Applications
947(1)
36.4.1 Cardiac
947(1)
36.4.2 Cranial
948(1)
36.4.3 Abdominal
948(1)
36.5 Summary
948(1)
References
948(5)
Chapter 37 Hyperpolarized Magnetic Resonance Spectroscopy and Imaging
953(26)
Thomas R. Eykyn
37.1 Introduction
953(1)
37.2 Principles of hyperpolarization--Basic concepts and sensitivity
954(3)
37.3 Hyperpolarization technologies
957(2)
37.4 System set-up for hyperpolarized 13C
959(1)
37.5 Imaging hyperpolarized substrates
960(5)
37.6 Quantifying temporal kinetics
965(3)
37.7 Conservation of mass
968(1)
37.8 Applications of hyperpolarized l3C MRI
968(3)
37.9 Application of hyperpolarized gases
971(1)
37.10 Summary
971(1)
Acknowledgments
971(1)
References
972(7)
Index 979
Dr. Nicole Seiberlich is an Associate Professor in the Department of Radiology at the University of Michigan in Ann Arbor, and the Director of the Michigan Institute for Imaging Technology and Translation (MIITT). She was previously the Elmer Lincoln Lindseth Associate Professor of Biomedical Engineering at Case Western Reserve University. Dr. Seiberlich received her BS in Chemistry from Yale University (New Haven, CT) and her PhD in Physics from the Universität Würzburg (Würzburg, Germany). Her research focuses on novel data acquisition and signal processing techniques for rapid and quantitative Magnetic Resonance Imaging, with applications in cardiac and abdominal imaging. Vikas Gulani is the Chair and Fred J. Hodges Professor of Radiology at the University of Michigan. As Chair, his primary goal is to build a compassionate workplace that strives towards excellence. He was previously the Joseph T. Wearn Professor in Radiology, Urology, and Biomedical Engineering and the Director of MRI at Case Western Reserve University and University Hospitals. Dr. Gulani is a physician-scientist interested in MR technology development and clinical translation. His clinical interests include prostate and liver MRI, MR angiography, and in-bore intervention. His scientific interests include relaxometry, diffusion imaging, perfusion, MR microscopy, parallel imaging, rapid acquisitions, and body MRI. His recent work has focused on development and translation of MR Fingerprinting. Dr. Adrienne Campbell-Washburn is Earl Stadtman Principal Investigator at the MRI Technology Program for the National Heart, Lung, and Blood Institute at the National Institutes of Health. She received her PhD in Medical Physics from University College London.Her research focuses on the development of MRI technology for cardiac, lung and interventional imaging applications. She works on low field MRI technology, advanced MRI acquisitions that leverage non-Cartesian sampling, and reconstruction methods using state-of-the-art computational resources in the clinical environment. Steven Sourbron holds a Chair in Medical Imaging Physics in the University of Sheffield, UK. He is a theoretical physicist by training, obtained a PhD on perfusion MRI from the Free University of Brussels (Belgium), and performed post-doctoral research in the Ludwig-Maximilian University of Munich (Germany) before taking up a lectureship in the University of Leeds (UK). His research focuses on developing and applying quantitative medical imaging techniques that provide more accurate and more biologically specific assessment of tissue perfusion, function and structure. Much of his current work involves clinical studies on non-invasive assessment of chronic kidney- and liver disease to determine if quantitative MRI can improve prognosis and prediction of treatment effects. Mariya Doneva is a senior scientist at Philips Research, Hamburg, Germany. She received her BSc and MSc degrees in Physics from the University of Oldenburg in 2006 and 2007, respectively and her PhD degree in Physics from the University of Luebeck in 2010. She was a Research Associate at Electrical Engineering and Computer Sciences department at UC Berkeley between 2015 and 2016. She is a recipient of the Junior Fellow award of the International Society for Magnetic Resonance in Medicine. Her research interests include methods for efficient data acquisition, image reconstruction and quantitative parameter mapping in the context of magnetic resonance imaging. Fernando Calamante studied Physics in Argentina, and obtained his PhD in MRI from University College London in 2000. He is Professor at the Faculty of Engineering, and Director of Sydney Imaging (the biomedical imaging Core Research Facility) at the University of Sydney. His main areas of research are Diffusion and Perfusion MRI, and their applications to neurology and neuroscience. His work on Perfusion MRI is highly cited and at the forefront of the field, and his Diffusion MRI methods for characterising structural connectivity are also widely used worldwide. Fernando will be President of the International Society for Magnetic Resonance in Medicine in 2021-2022. Houchun Harry Hu has been working in the domain of pediatric MRI over the last 15 years. He obtained his undergraduate degree in biochemical engineering at the University of Southern California, and his PhD in Medical Imaging from the Mayo Clinic. He has served as a Deputy Editor for Magnetic Resonance in Medicine, and an Associate Editor for Radiology and the Journal of Magnetic Resonance Imaging. Dr. Hu's main interests are in translational and clinical research. He has published over 100+ first-author and co-authored manuscripts.