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Table of Contents
Intro
Quantitative Magnetic Resonance Imaging
Copyright
Dedication
Contents
Contributors
Section: Introduction
Quantitative MRI: Rationale and Challenges
1. The goal of radiological imaging
2. Using quantitative MRI to meet the goals of medical imaging
3. Why tackle quantitative MRI now?
4. Open needs in quantitative MRI
4.1. Technical needs
4.2. Clinical needs
4.3. The translational gap
5. Organization of the book
Acknowledgments
References
MRI Biomarkers
1. Introduction
2. Fundamentals of imaging biomarkers
2.1. What is a biomarker?
2.2. What is an imaging biomarker?
2.3. Where are imaging biomarkers used?
2.3.1. Imaging biomarkers in research or internal decision making
2.3.2. Imaging biomarkers in drug or device development
2.3.3. Imaging biomarkers in clinical practice
2.4. Regulatory aspects of biomarkers
2.4.1. Approval for use in research or internal decision making
2.4.2. Qualification for use in drug or device development
2.4.3. Approval for use in clinical practice
2.4.4. Approval for in-house clinical use
2.5. Metrology of imaging biomarkers
2.5.1. Reproducibility and traceability
2.5.2. Reference standards for imaging biomarkers
2.5.3. Standardization as a driver for innovation
3. Imaging biomarker translation
3.1. The imaging biomarker roadmap
3.2. Crossing the first translational gap: MR biomarkers used in medical research
3.2.1. Assay development
3.2.2. Technical validation
3.2.3. Biological validation
3.2.4. Clinical validation
3.2.5. Clinical utility
3.2.6. Cost-effectiveness
3.3. Crossing the second translational gap: MR biomarkers used in healthcare
3.3.1. Assay development
3.3.2. Technical validation
3.3.3. Biological and clinical validation
3.3.4. Clinical utility.
3.3.5. Cost-effectiveness
4. Specific challenges in the development of MRI-based QIBs
4.1. Technical validation
4.1.1. Specific challenges in technical validation of MRI-based QIBs
4.1.2. Specific solutions for technical validation of MRI QIBs
4.2. Biological validation
4.2.1. Specific challenges in biological validation of MRI-based QIBs
4.2.2. Specific solutions for biological validation of MRI QIBs
4.3. Assay development
4.3.1. MRI biomarker assays for medical research
4.3.2. MRI biomarker assays for clinical practice
5. Summary
Acknowledgments
Appendix A: A case study in biospecimen biomarker translation
A.1. Biological validation
A.2. Clinical validation
A.3. Technical validation
A.4. Clinical utility as a diagnostic
Appendix B: A case study in imaging biomarker translation
B.1. Discovery and proof of concept
B.2. Crossing the first translational gap
B.3. Crossing the second translational gap
References
Section 1: Relaxometry
Chapter 1: Biophysical and Physiological Principles of T1 and T2
1.1. Introduction
1.2. The biophysical basis of relaxation
1.2.1. T2 relaxation
1.2.2. T1 relaxation
1.2.3. Mathematical formulation of relaxation
1.3. Biophysical factors that influence relaxation
1.3.1. Multicomponent relaxometry
1.3.2. Microstructural orientation and magnetic susceptibility
1.3.2.1. Neuronal activity, hemodynamics, and T2
1.3.2.2. Blood flow and exchange
1.3.2.3. Magnetization transfer
1.4. Summary
References
Chapter 2: Quantitative T1 and T1ρ Mapping
2.1. Introduction
2.2. Inversion recovery
2.2.1. Signal modeling
2.2.2. Data fitting
2.2.3. Benefits and pitfalls
2.2.4. Other saturation recovery T1 mapping techniques
2.3. Variable flip angle
2.3.1. Signal modeling
2.3.2. Data fitting.
2.3.3. Benefits and pitfalls
2.4. MP2RAGE
2.4.1. Signal modeling
2.4.2. Data fitting
2.4.3. Benefits and pitfalls
2.5. T1ρ mapping
2.5.1. Signal modeling
2.5.2. Data fitting
2.5.3. Benefits and pitfalls
2.6. Concluding remarks
References
Chapter 3: Quantitative T2 and T2* Mapping
3.1. Introduction
3.2. Spin-spin relaxation (T2) measurement sequences
3.2.1. Single spin echo sequences
3.2.2. Multiecho spin echo sequences
3.2.3. T2-prepared sequences
3.2.4. Unspoiled gradient echo sequences
3.2.5. Model-based reconstructions
3.3. Effective spin-spin relaxation (T2*) measurement sequences
3.3.1. Single and multiecho spoiled gradient echo sequences
3.3.2. Prospective correction of susceptibility-induced field gradients
3.3.3. Retrospective correction of susceptibility-induced field gradients
3.3.4. Asymmetric spin echo sequences
3.4. Simultaneous T2 and T2 measurement sequences
3.5. Approaches for estimating T2 and T2*
3.5.1. Single-exponential models
3.5.2. Multiexponential models
3.6. Summary
References
Chapter 4: Multiproperty Mapping Methods
4.1. Simultaneous quantification of multiple relaxometry parameters
4.2. Simultaneous quantification of T1 and T2
4.2.1. Inversion recovery-bSSFP (IR-bSSFP, IR TrueFISP)
4.2.2. Magnetic resonance fingerprinting
4.2.3. Magnetization-prepared dual echo steady-state
4.2.4. Triple-echo steady-state
4.2.5. Phase-cycled bSSFP
4.3. Simultaneous quantification of T1 and T2*
4.3.1. Absolute quantification
4.3.2. Quantification relative to baseline
4.4. Simultaneous quantification of T2 and T2*
4.5. Common challenges in simultaneous relaxation time measurements
4.6. Summary
References
Chapter 5: Specialized Mapping Methods in the Heart
5.1. Introduction.
5.2. Cardiac T1 and extracellular volume mapping
5.2.1. Inversion recovery-based T1 mapping
5.2.2. Saturation recovery-based T1 mapping
5.2.3. Combined inversion recovery and saturation recovery-based T1 mapping
5.2.4. Cardiac extracellular volume mapping
5.2.5. Novel developments in cardiac T1 mapping
5.3. Cardiac T2 and T2* mapping
5.3.1. T2-prepared T2 mapping
5.3.2. Gradient and spin echo T2 mapping
5.3.3. Comparison of T2 mapping techniques
5.3.4. Cardiac T2* mapping
5.3.5. Novel developments in cardiac T2 mapping
5.4. Beyond single parameter mapping in the heart
5.4.1. Joint T1-T2 mapping of the heart
5.4.2. Cardiac magnetic resonance fingerprinting
5.4.3. Cardiac Multitasking
5.5. Concluding remarks
References
Chapter 6: Advances in Signal Processing for Relaxometry
6.1. Introduction
6.2. Advanced signal models
6.2.1. T2 mapping using the slice-resolved Extended Phase Graph formalism
6.2.2. Bloch equation simulation-based signal models
6.2.3. Multi-GRE-based relaxation mapping
6.2.4. Confounding factors
6.2.4.1. Magnetization transfer (MT)
6.2.4.2. Subvoxel compartmentation
6.3. Advanced reconstruction of undersampled datasets
6.3.1. Non-Cartesian data sampling
6.3.2. Model-based reconstruction of undersampled relaxation mapping
6.3.3. Compressed sensing (CS) and sparsity-driven reconstruction
6.4. Identification of new signal motifs
6.4.1. Magnetic Resonance Fingerprinting
6.4.2. Subvoxel multicompartment relaxometry
6.5. Concluding remarks
References
Chapter 7: Relaxometry: Applications in the Brain
7.1. Introduction
7.2. Overview of the brain
7.3. T1 in brain
7.3.1. Measuring T1 in brain
7.3.2. Physiological influences of T1 in brain
7.3.3. Single or multiple T1 components?
7.3.4. Interpreting T1 in the brain.
7.4. Clinical applications of T1 relaxation
7.4.1. Development and aging
7.4.2. Multiple sclerosis
7.4.3. Parkinson's disease
7.4.4. Brain cancer and radiation
7.4.5. Other applications
7.5. T2 in brain
7.5.1. Measuring multicomponent T2 in brain
7.5.2. Physiological influences of T2 in brain
7.5.3. Interpreting T2 in the brain
7.6. Clinical applications of T2 relaxation
7.6.1. Development and aging
7.6.2. Developmental and genetic disorders
7.6.3. Multiple sclerosis
7.6.4. Alzheimer's disease
7.6.5. Epilepsy
7.6.6. Cancer
7.6.7. Other diseases
7.7. T1ρ in brain
7.7.1. Measuring T1ρ in brain
7.7.2. Interpreting T1ρ in brain and clinical applications
7.8. T2* in brain
7.8.1. Measuring T2* in brain
7.8.2. Interpreting T2* in brain and clinical applications
7.9. Challenges with clinical application of relaxation
7.10. Concluding remarks
Acknowledgments
References
Chapter 8: Relaxometry: Applications in Musculoskeletal Systems
8.1. Introduction
8.2. MRI relaxometry of cartilage
8.2.1. Cartilage biochemistry and degeneration
8.2.2. Post-contrast T1 relaxation time mapping with delayed gadolinium-enhanced MRI of cartilage
8.2.3. T2 and T2* relaxation time mapping in cartilage
8.2.4. T1ρ relaxation time mapping of cartilage
8.3. MRI relaxometry to assess skeletal muscle
8.4. MRI relaxometry of menisci, tendons, and ligaments
8.5. MRI relaxometry of intervertebral discs
8.6. Outlook and Conclusion
Acknowledgments
References
Chapter 9: Relaxometry: Applications in the Body
9.1. Introduction
9.2. Liver
9.3. Spleen
9.4. Kidneys
9.5. Pancreas
9.6. Prostate
9.7. Breast
9.8. Challenges
References
Chapter 10: Relaxometry: Applications in the Heart
10.1. Introduction
10.2. Acute chest pain syndromes.
10.2.1. Myocarditis.
Quantitative Magnetic Resonance Imaging
Copyright
Dedication
Contents
Contributors
Section: Introduction
Quantitative MRI: Rationale and Challenges
1. The goal of radiological imaging
2. Using quantitative MRI to meet the goals of medical imaging
3. Why tackle quantitative MRI now?
4. Open needs in quantitative MRI
4.1. Technical needs
4.2. Clinical needs
4.3. The translational gap
5. Organization of the book
Acknowledgments
References
MRI Biomarkers
1. Introduction
2. Fundamentals of imaging biomarkers
2.1. What is a biomarker?
2.2. What is an imaging biomarker?
2.3. Where are imaging biomarkers used?
2.3.1. Imaging biomarkers in research or internal decision making
2.3.2. Imaging biomarkers in drug or device development
2.3.3. Imaging biomarkers in clinical practice
2.4. Regulatory aspects of biomarkers
2.4.1. Approval for use in research or internal decision making
2.4.2. Qualification for use in drug or device development
2.4.3. Approval for use in clinical practice
2.4.4. Approval for in-house clinical use
2.5. Metrology of imaging biomarkers
2.5.1. Reproducibility and traceability
2.5.2. Reference standards for imaging biomarkers
2.5.3. Standardization as a driver for innovation
3. Imaging biomarker translation
3.1. The imaging biomarker roadmap
3.2. Crossing the first translational gap: MR biomarkers used in medical research
3.2.1. Assay development
3.2.2. Technical validation
3.2.3. Biological validation
3.2.4. Clinical validation
3.2.5. Clinical utility
3.2.6. Cost-effectiveness
3.3. Crossing the second translational gap: MR biomarkers used in healthcare
3.3.1. Assay development
3.3.2. Technical validation
3.3.3. Biological and clinical validation
3.3.4. Clinical utility.
3.3.5. Cost-effectiveness
4. Specific challenges in the development of MRI-based QIBs
4.1. Technical validation
4.1.1. Specific challenges in technical validation of MRI-based QIBs
4.1.2. Specific solutions for technical validation of MRI QIBs
4.2. Biological validation
4.2.1. Specific challenges in biological validation of MRI-based QIBs
4.2.2. Specific solutions for biological validation of MRI QIBs
4.3. Assay development
4.3.1. MRI biomarker assays for medical research
4.3.2. MRI biomarker assays for clinical practice
5. Summary
Acknowledgments
Appendix A: A case study in biospecimen biomarker translation
A.1. Biological validation
A.2. Clinical validation
A.3. Technical validation
A.4. Clinical utility as a diagnostic
Appendix B: A case study in imaging biomarker translation
B.1. Discovery and proof of concept
B.2. Crossing the first translational gap
B.3. Crossing the second translational gap
References
Section 1: Relaxometry
Chapter 1: Biophysical and Physiological Principles of T1 and T2
1.1. Introduction
1.2. The biophysical basis of relaxation
1.2.1. T2 relaxation
1.2.2. T1 relaxation
1.2.3. Mathematical formulation of relaxation
1.3. Biophysical factors that influence relaxation
1.3.1. Multicomponent relaxometry
1.3.2. Microstructural orientation and magnetic susceptibility
1.3.2.1. Neuronal activity, hemodynamics, and T2
1.3.2.2. Blood flow and exchange
1.3.2.3. Magnetization transfer
1.4. Summary
References
Chapter 2: Quantitative T1 and T1ρ Mapping
2.1. Introduction
2.2. Inversion recovery
2.2.1. Signal modeling
2.2.2. Data fitting
2.2.3. Benefits and pitfalls
2.2.4. Other saturation recovery T1 mapping techniques
2.3. Variable flip angle
2.3.1. Signal modeling
2.3.2. Data fitting.
2.3.3. Benefits and pitfalls
2.4. MP2RAGE
2.4.1. Signal modeling
2.4.2. Data fitting
2.4.3. Benefits and pitfalls
2.5. T1ρ mapping
2.5.1. Signal modeling
2.5.2. Data fitting
2.5.3. Benefits and pitfalls
2.6. Concluding remarks
References
Chapter 3: Quantitative T2 and T2* Mapping
3.1. Introduction
3.2. Spin-spin relaxation (T2) measurement sequences
3.2.1. Single spin echo sequences
3.2.2. Multiecho spin echo sequences
3.2.3. T2-prepared sequences
3.2.4. Unspoiled gradient echo sequences
3.2.5. Model-based reconstructions
3.3. Effective spin-spin relaxation (T2*) measurement sequences
3.3.1. Single and multiecho spoiled gradient echo sequences
3.3.2. Prospective correction of susceptibility-induced field gradients
3.3.3. Retrospective correction of susceptibility-induced field gradients
3.3.4. Asymmetric spin echo sequences
3.4. Simultaneous T2 and T2 measurement sequences
3.5. Approaches for estimating T2 and T2*
3.5.1. Single-exponential models
3.5.2. Multiexponential models
3.6. Summary
References
Chapter 4: Multiproperty Mapping Methods
4.1. Simultaneous quantification of multiple relaxometry parameters
4.2. Simultaneous quantification of T1 and T2
4.2.1. Inversion recovery-bSSFP (IR-bSSFP, IR TrueFISP)
4.2.2. Magnetic resonance fingerprinting
4.2.3. Magnetization-prepared dual echo steady-state
4.2.4. Triple-echo steady-state
4.2.5. Phase-cycled bSSFP
4.3. Simultaneous quantification of T1 and T2*
4.3.1. Absolute quantification
4.3.2. Quantification relative to baseline
4.4. Simultaneous quantification of T2 and T2*
4.5. Common challenges in simultaneous relaxation time measurements
4.6. Summary
References
Chapter 5: Specialized Mapping Methods in the Heart
5.1. Introduction.
5.2. Cardiac T1 and extracellular volume mapping
5.2.1. Inversion recovery-based T1 mapping
5.2.2. Saturation recovery-based T1 mapping
5.2.3. Combined inversion recovery and saturation recovery-based T1 mapping
5.2.4. Cardiac extracellular volume mapping
5.2.5. Novel developments in cardiac T1 mapping
5.3. Cardiac T2 and T2* mapping
5.3.1. T2-prepared T2 mapping
5.3.2. Gradient and spin echo T2 mapping
5.3.3. Comparison of T2 mapping techniques
5.3.4. Cardiac T2* mapping
5.3.5. Novel developments in cardiac T2 mapping
5.4. Beyond single parameter mapping in the heart
5.4.1. Joint T1-T2 mapping of the heart
5.4.2. Cardiac magnetic resonance fingerprinting
5.4.3. Cardiac Multitasking
5.5. Concluding remarks
References
Chapter 6: Advances in Signal Processing for Relaxometry
6.1. Introduction
6.2. Advanced signal models
6.2.1. T2 mapping using the slice-resolved Extended Phase Graph formalism
6.2.2. Bloch equation simulation-based signal models
6.2.3. Multi-GRE-based relaxation mapping
6.2.4. Confounding factors
6.2.4.1. Magnetization transfer (MT)
6.2.4.2. Subvoxel compartmentation
6.3. Advanced reconstruction of undersampled datasets
6.3.1. Non-Cartesian data sampling
6.3.2. Model-based reconstruction of undersampled relaxation mapping
6.3.3. Compressed sensing (CS) and sparsity-driven reconstruction
6.4. Identification of new signal motifs
6.4.1. Magnetic Resonance Fingerprinting
6.4.2. Subvoxel multicompartment relaxometry
6.5. Concluding remarks
References
Chapter 7: Relaxometry: Applications in the Brain
7.1. Introduction
7.2. Overview of the brain
7.3. T1 in brain
7.3.1. Measuring T1 in brain
7.3.2. Physiological influences of T1 in brain
7.3.3. Single or multiple T1 components?
7.3.4. Interpreting T1 in the brain.
7.4. Clinical applications of T1 relaxation
7.4.1. Development and aging
7.4.2. Multiple sclerosis
7.4.3. Parkinson's disease
7.4.4. Brain cancer and radiation
7.4.5. Other applications
7.5. T2 in brain
7.5.1. Measuring multicomponent T2 in brain
7.5.2. Physiological influences of T2 in brain
7.5.3. Interpreting T2 in the brain
7.6. Clinical applications of T2 relaxation
7.6.1. Development and aging
7.6.2. Developmental and genetic disorders
7.6.3. Multiple sclerosis
7.6.4. Alzheimer's disease
7.6.5. Epilepsy
7.6.6. Cancer
7.6.7. Other diseases
7.7. T1ρ in brain
7.7.1. Measuring T1ρ in brain
7.7.2. Interpreting T1ρ in brain and clinical applications
7.8. T2* in brain
7.8.1. Measuring T2* in brain
7.8.2. Interpreting T2* in brain and clinical applications
7.9. Challenges with clinical application of relaxation
7.10. Concluding remarks
Acknowledgments
References
Chapter 8: Relaxometry: Applications in Musculoskeletal Systems
8.1. Introduction
8.2. MRI relaxometry of cartilage
8.2.1. Cartilage biochemistry and degeneration
8.2.2. Post-contrast T1 relaxation time mapping with delayed gadolinium-enhanced MRI of cartilage
8.2.3. T2 and T2* relaxation time mapping in cartilage
8.2.4. T1ρ relaxation time mapping of cartilage
8.3. MRI relaxometry to assess skeletal muscle
8.4. MRI relaxometry of menisci, tendons, and ligaments
8.5. MRI relaxometry of intervertebral discs
8.6. Outlook and Conclusion
Acknowledgments
References
Chapter 9: Relaxometry: Applications in the Body
9.1. Introduction
9.2. Liver
9.3. Spleen
9.4. Kidneys
9.5. Pancreas
9.6. Prostate
9.7. Breast
9.8. Challenges
References
Chapter 10: Relaxometry: Applications in the Heart
10.1. Introduction
10.2. Acute chest pain syndromes.
10.2.1. Myocarditis.