UID:
almahu_9949551252302882
Format:
1 online resource (564 pages)
Edition:
1st ed.
ISBN:
0-323-95210-0
,
0-323-95209-7
Series Statement:
Advances in Magnetic Resonance Technology and Applications ; v. 11
Note:
Intro -- Quantitative Perfusion MRI: Techniques, Applications and Practical Considerations -- Copyright -- Contents -- List of contributors -- Preface -- Section 1: Basic principles -- Chapter 1: Basic principles for imaging blood flow -- 1.1. Introduction -- 1.2. Theory -- 1.2.1. Conservation of mass -- 1.2.2. The Fick principle -- 1.2.3. The central volume principle -- 1.2.4. Key perfusion parameters -- 1.2.4.1. Perfusion, perfusion rate, and blood flow -- 1.2.4.2. Blood volume -- 1.2.4.3. Mean transit time -- 1.3. MRI versus other modalities for imaging perfusion -- 1.3.1. Basics of MRI -- 1.3.2. Indicator: Injected contrast -- 1.3.2.1. Contrast-enhanced MR angiography -- 1.3.2.2. Late-gadolinium enhancement -- 1.3.2.3. Dynamic contrast-enhanced-MRI -- 1.3.2.4. Dynamic susceptibility contrast-MRI -- 1.3.3. Indicator: Moving blood -- 1.3.3.1. Time-of-flight angiography -- 1.3.3.2. 2D phase contrast and 4D flow -- 1.3.3.3. Arterial spin labeling -- 1.3.3.4. Intravoxel incoherent motion -- 1.3.4. Indicator: Blood oxygenation -- 1.3.4.1. Blood oxygen level dependent -- 1.3.4.2. Susceptibility-weighted imaging -- 1.4. Clinical relevance -- 1.4.1. Macrovascular -- 1.4.2. Microvascular -- 1.5. Summary -- References -- Chapter 2: Dynamic contrast-enhanced MRI -- 2.1. Introduction -- 2.2. Image acquisition -- 2.2.1. Imaging protocol -- 2.2.2. T1 mapping acquisition -- 2.2.3. DCE acquisition -- 2.2.4. Injection protocol -- 2.3. Quantitative DCE analysis -- 2.3.1. Preprocessing -- 2.3.2. Converting signal to contrast agent concentration -- 2.3.3. Measuring the AIF -- 2.3.4. Tracer kinetic modeling -- 2.3.4.1. Choice of model -- 2.3.5. Challenges for clinical adoption of quantitative DCE-MRI -- 2.3.5.1. Biomarker validation -- 2.3.5.2. Lack of standardized terminology -- 2.3.5.3. Lack of resources -- 2.4. Summary -- References.
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Chapter 3: Dynamic susceptibility contrast MRI -- 3.1. Introduction -- 3.2. Theory -- 3.2.1. Paramagnetic contrast agents, dipole-dipole interactions, and magnetic susceptibility effects -- 3.2.2. Contrast dose, injection, and risks -- 3.3. Acquisition of DSC-MRI data -- 3.3.1. The MR pulse sequence: Choice of acquisition readout -- 3.3.2. The image contrast: Gradient and spin echo -- 3.3.3. Pulse sequence: Parameter selection and acceleration methods -- 3.3.4. Data preprocessing for acquisition: Motion, noise, artifacts -- 3.4. Perfusion parameters -- 3.4.1. Conversion of MR signal to concentration-time curves -- 3.4.1.1. Single-echo DSC-MRI acquisition -- 3.4.1.2. Dual-echo DSC-MRI acquisition -- 3.4.1.3. SAGE and sSAGE DSC-MRI acquisition -- 3.4.2. DSC-MRI kinetic models -- 3.5. DSC-MRI data postprocessing possibilities -- 3.5.1. Multivendor postprocessing software applications -- 3.5.2. Magnetic field inhomogeneity correction -- 3.5.3. Deconvolution -- 3.5.4. Absolute or relative quantification -- 3.5.5. Leakage correction -- 3.5.6. Standardization efforts -- 3.6. Advanced biomarkers in DSC-MRI -- 3.6.1. Capillary transit time heterogeneity and oxygen extraction function -- 3.6.2. Vessel size and architecture imaging -- 3.7. Applications -- 3.7.1. DSC-MRI in brain tumors -- 3.7.2. DSC-MRI in acute stroke -- 3.7.3. DSC-MRI in aging -- 3.7.4. DSC-MRI in multiple sclerosis -- 3.8. Summary -- References -- Further reading -- Chapter 4: Arterial spin labeling MRI -- 4.1. Introduction -- 4.2. Acquisition -- 4.2.1. Labeling strategies -- 4.2.1.1. Labeling efficiency -- 4.2.1.2. Postlabeling delay -- 4.2.2. Readout -- 4.2.2.1. Background suppression -- 4.2.3. M0 acquisition -- 4.3. Analysis -- 4.3.1. Motion correction and outlier removal -- 4.3.2. Segmentation and registration -- 4.3.3. Quantification -- 4.3.4. Physiological variability and confounders.
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4.3.5. Image processing software -- 4.4. Clinical applications -- 4.4.1. Cerebrovascular disease -- 4.4.2. Brain tumors -- 4.4.3. Neurodegenerative disease -- 4.4.4. Epilepsy -- 4.4.5. Pediatric diseases -- 4.5. Advanced techniques and future directions -- 4.5.1. More robust ASL techniques -- 4.5.2. Additional hemodynamic information -- 4.6. Conclusion -- References -- Recommended further reading -- Chapter 5: Vasoreactivity MRI -- 5.1. Introduction -- 5.2. Theory -- 5.2.1. The physiology of vasomotor control -- 5.2.2. Vasoactive stimuli -- 5.2.2.1. Pharmacological agents -- 5.2.2.2. Flow-mediated dilation -- 5.2.2.3. Exercise -- 5.2.2.4. Respiratory gas challenge -- 5.2.3. MRI of vasoreactivity -- 5.2.3.1. BOLD-MRI -- 5.2.3.2. Arterial spin labeling -- 5.2.3.3. Blood-pool steady-state T1 imaging -- 5.3. Applications -- 5.3.1. Cerebral steno-occlusive disease -- 5.3.2. Dementia and cognitive impairment -- 5.3.3. Small vessel disease -- 5.3.4. Diabetes -- 5.3.5. Cardiovascular disease -- 5.4. Future directions -- 5.5. Summary -- References -- Further reading -- Section 2: Technical considerations -- Chapter 6: MR contrast agents for perfusion imaging -- 6.1. Introduction -- 6.2. Classification of MR contrast agents for perfusion imaging -- 6.2.1. Classification based on chemical composition -- 6.2.2. Classification based on magnetic properties -- 6.2.3. Classification based on biodistribution -- 6.3. Clinical applications -- 6.4. Safety issues -- 6.5. Learning and knowledge outcomes -- Disclaimer -- References -- Chapter 7: Protocol requirements for quantitation accuracy -- 7.1. Introduction -- 7.2. Dynamic acquisition: The ideal sequence -- 7.2.1. Measurement of tracer concentration -- 7.2.2. High temporal resolution and complete washout -- 7.2.3. High spatial resolution -- 7.2.4. High signal-to-noise ratio -- 7.2.5. Motion compensation.
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7.3. Dynamic acquisition: Practical solutions -- 7.3.1. DCE-MRI -- 7.3.2. DSC-MRI -- 7.3.3. ASL -- 7.4. Additional considerations for accurate quantification -- 7.4.1. Structural imaging -- 7.4.2. T1 measurement -- 7.4.3. Water exchange -- 7.4.4. Practical considerations -- 7.5. Future directions -- 7.6. Summary -- References -- Chapter 8: Arterial input function: A friend or a foe? -- 8.1. Introduction -- 8.2. Pitfalls and possibilities -- 8.2.1. Acquisition of the AIF -- 8.2.1.1. Dynamic susceptibility contrast MRI -- 8.2.1.2. Dynamic contrast-enhanced MRI -- 8.2.1.3. Arterial spin labeling -- 8.2.2. Selection of the AIF -- 8.2.2.1. Dynamic susceptibility contrast MRI -- 8.2.2.2. Dynamic contrast-enhanced MRI -- 8.2.2.3. Arterial spin labeling -- 8.2.3. Calculation of AIF concentration -- 8.2.3.1. Dynamic susceptibility contrast MRI -- 8.2.3.2. Dynamic contrast-enhanced MRI -- 8.2.3.3. Arterial spin labeling -- 8.3. Summary -- References -- Chapter 9: Motion compensation strategies -- 9.1. Introduction -- 9.2. Acquisition strategies -- 9.3. Types of motion and related artifacts -- 9.4. Potential strategies to mitigate motion-related artifacts -- 9.4.1. Head motion -- 9.4.2. Respiratory motion for body imaging -- 9.4.3. Motion around the heart -- 9.5. New approaches and future directions -- 9.6. Summary -- References -- Chapter 10: Practical considerations for water exchange modeling in DCE-MRI -- 10.1. Introduction -- 10.2. Modeling water exchange in DCE-MRI -- 10.2.1. Four-compartment catenary model -- 10.3. Signal modeling in the presence of water exchange -- 10.3.1. Arterial input function -- 10.3.2. Compartment volumes -- 10.3.3. Pharmacokinetic parameters -- 10.3.4. Intrinsic relaxation rates -- 10.3.5. Contrast agent relaxivities -- 10.3.6. Measurement parameters -- 10.3.7. Water exchange rate constants.
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10.4. Simulation of water exchange effects in DCE-MRI -- 10.5. Model parameter estimation -- 10.5.1. Bootstrapping initial parameter guesses -- 10.5.2. Model parameter constraints and reparameterization -- 10.5.3. Identifiability of effects of finite water exchange -- 10.6. Methods of enhancing sensitivity to water exchange -- 10.6.1. Dual flip angle imaging -- 10.6.2. Multiagent imaging -- 10.7. Conclusions -- References -- Chapter 11: Acceleration methods for perfusion imaging -- 11.1. Introduction -- 11.2. General MRI acquisition and reconstruction -- 11.2.1. MRI signal equation -- 11.2.2. Acceleration of data acquisition in MRI -- 11.3. Accelerated MRI through reconstruction of undersampled data -- 11.3.1. Linear reconstruction methods -- 11.3.1.1. Methods exploiting temporal correlations -- View sharing -- 11.3.1.2. Methods exploiting spatial correlations -- Partial Fourier imaging -- Parallel imaging -- 11.3.1.3. Methods exploiting spatiotemporal correlations -- Temporal parallel imaging -- K-t acceleration methods -- 11.3.2. Nonlinear reconstruction techniques -- 11.3.2.1. Compressed sensing -- 11.3.2.2. Low-rank-based reconstruction -- Implicit low-rank reconstruction -- Explicit low-rank reconstruction -- 11.3.2.3. Model-based reconstruction -- 11.3.2.4. Deep learning-based reconstruction -- 11.4. Accelerated MRI through simultaneous multi-slice imaging -- 11.5. Accelerated MRI through reduced signal averages -- 11.6. Selection of acceleration methods for perfusion MRI -- 11.6.1. Acceleration methods for DCE-MRI -- 11.6.2. Acceleration methods for DSC-MRI -- 11.6.3. Acceleration methods for ASL-MRI -- 11.7. Conclusion -- Acknowledgment -- References -- Further reading -- Chapter 12: Artificial intelligence: The next frontier of perfusion imaging? -- 12.1. Introduction -- 12.2. Background -- 12.2.1. Deep learning -- 12.3. AI in perfusion MRI.
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12.3.1. Acquisition.
Language:
English
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