Motion Correction in MR: Correction of Position, Motion, and Dynamic Field Changes

Front Cover
Motion Correction in MR: Correction of Position, Motion, and Dynamic Field Changes
Copyright
Contents
List of contributors
Preface
Part 1: Motion in MR scans
Chapter 1: Why do patients move?
1.1. Introduction
1.2. Types of motion
1.3. How motion affects MR acquisition
1.4. Why do patients move?
1.5. Cost of motion
1.6. Motion-mitigating solutions
1.6.1. Patient focused techniques
1.6.2. Protocol/image reconstruction techniques
1.6.2.1. Cartesian techniques
1.6.2.2. Modified radial acquisition techniques
1.6.2.3. Stack of stars
1.6.2.4. Gating
1.6.2.5. Scan time reduction
Reduced averages
Parallel imaging
Deep learning image reconstruction
Neural network specific motion mitigation
1.7. Conclusion
Acknowledgments
References
Chapter 2: Impact of motion on research studies
2.1. Why is motion a problem for research imaging studies?
2.2. Motion in structural research imaging
2.2.1. Why is motion a problem in structural imaging?
2.2.2. Characteristics of motion artifacts in structural imaging
2.2.3. Impact of motion on extracted morphometry parameters
2.3. Motion in single-shot EPI for functional MRI
2.3.1. Why is motion a problem in fMRI?
2.3.2. Characteristics of motion artifacts in fMRI
2.3.3. Impact of motion on extracted fMRI parameters
2.4. Motion in single-shot EPI for DWI
2.4.1. Why is motion a problem in DWI?
2.4.2. Characteristics of motion artifacts in DWI
2.4.3. Impact of motion on extracted diffusion parameters
2.5. Summary
Acknowledgments
References
Chapter 3: Cost economy of motion
3.1. Introduction
3.2. What affects costs in MR?
3.2.1. MR exam charges and collections
3.2.2. MR cost analysis for hospitals and practices
3.2.3. Impact of efficiency on MR sustainability
3.3. Frequency of undesired patient motion
3.4. Financial impact
3.4.1. Lost revenue attributable to repeat scanning
3.4.2. Costs of sedation
3.4.3. Other costs
3.5. Conclusion
References
Chapter 4: Physical and pharmacologic solutions
4.1. Introduction
4.2. Physical solution
4.2.1. Effective use of positioning aids in MR imaging
4.2.1.1. Inflatable pads
4.2.1.2. Straps
4.2.1.3. Positioning options
Elbow
Knee
Foot/ankle
Liver/abdomen
4.2.2. Pharmacologic solutions
4.2.3. Levels of sedation and monitored anesthesia care
4.2.3.1. Minimal sedation
4.2.3.2. Moderate sedation
4.2.3.3. Deep sedation
4.2.4. General anesthesia
4.2.5. Interdepartmental communication
4.3. Intravenous induction medications
4.4. Most commonly used medications for minimal and moderate sedation
4.4.1. Midazolam
4.4.2. Dexmedetomidine
4.4.3. Propofol
4.5. Medications used to produce deep sedation and general anesthesia
4.5.1. Etomidate
4.5.2. Ketamine
4.6. Other medications used in monitored anesthesia care or general anesthesia by class
4.6.1. Benzodiazepine medications
4.6.1.1. Lorazepam
4.6.1.2. Diazepam
4.6.1.3. Reversal medication: Flumazenil
4.6.2. Opioids medications
4.6.2.1. Remifentanil
4.6.2.2. Sufentanil
4.6.2.3. Fentanyl
4.6.2.4. Reversal medication: Naloxone
4.6.3. Nondepolarizing neuromuscular blocking agents
4.6.3.1. Reversal medications for nondepolarizing neuromuscular blocking agents
4.6.4. Depolarizing neuromuscular blocking agent
4.7. Special consideration for pediatric patients
4.7.1. Postoperative concerns in the pediatric patient
4.7.2. Nonpharmacologic alternatives in pediatric patients
4.7.3. Pediatric situations: Proceed with caution
4.8. Challenges for the anesthesiologist in the magnetic resonance imaging suite
4.8.1. Inaccessibility of patient to resources
4.8.2. Magnetic fields and ferromagnetic objects
4.8.3. Electrical interference and magnetic field impact on ECG monitoring
4.9. Hospital/payer costs for nonoperating room anesthesia
References
Chapter 5: Psychosocial solutions
5.1. Introduction
5.2. The patient experience
5.2.1. The physical experience
5.2.2. The psychological experience
5.3. Psychological interventions to reduce anxiety and motion in MRI
5.3.1. Education and emotional support
5.3.2. Hypnosis and guided imagery
5.3.3. Cognitive strategies
5.3.4. Music
5.3.5. Technologist team training
5.3.6. Virtual reality
5.4. Step by step interventional guide for radiologists and radiology technologists
5.4.1. Guided imagery script
5.4.2. Cognitive restructuring
5.4.3. The 5 senses technique for panic
5.5. Conclusion
References
Part 2: Consistent anatomical selection
Chapter 6: Automatically detecting anatomy: Robust multiscale anatomy alignment for magnetic resonance imaging
6.1. Introduction
6.2. Background and related work
6.2.1. Anatomy localization in volumetric image data
6.2.2. Intelligent localization using deep reinforcement learning
6.2.3. Anatomy detection for automatic MR scan alignment
6.3. Proposed method
6.3.1. A discrete parametric scale space
6.3.2. Anatomy alignment as a multiscale navigation problem
6.3.3. Learning multiscale navigation strategies
6.4. Experiments and results
6.4.1. Dataset
6.4.2. System training and hyperparameters
6.4.3. Results
6.5. Conclusions
Disclaimer
References
Chapter 7: Anatomical coordinate systems in brain analysis
7.1. Introduction
7.2. Coordinate systems—From empiricism to theory
7.3. The Talairach coordinate system and other atlas-based approaches for lesion analysis in neurology and neuropsychology
7.4. Cortex-based coordinate systems
7.5. White matter organization
7.6. Scanner implementations
7.7. Scanner coordinate systems and image labeling
7.8. Clinical relevance of coordinate systems
7.9. Conclusion
Acknowledgments
References
Part 3: Scan quality and motion metrics
Chapter 8: Metrics for motion and MR quality assessment
8.1. Introduction
8.2. Quality assessment strategies
8.2.1. Full-reference assessment
8.2.2. Partial/reduced-reference assessment
8.2.3. Reference-free assessment
8.3. Describing motion
8.3.1. Parameterizing spatial variation in displacement
8.3.2. Parameterizing temporal variation in displacement
8.3.2.1. Regular motion
8.3.2.2. Irregular motion
8.3.3. Summary measures of motion
8.3.3.1. Qualitative descriptions of motion
8.4. Describing the quality of motion measurements
8.4.1. Describing differences between motion estimates
8.4.2. Describing uncertainty within motion estimates
8.4.3. Task-based evaluation of motion estimates
8.5. Describing quality of images
8.5.1. Describing differences between images
8.5.2. Objective, reference-free measures of image quality
8.5.3. Describing image quality relative to a task
8.6. Conclusions and future directions
References
Chapter 9: Digital and physical phantoms for motion and flow simulation
9.1. Introduction
9.2. Motion simulation purposes
9.2.1. Reproducibility
9.2.2. Motion qualification and quantification
9.2.2.1. Vascular structure simulations
9.2.2.2. Outcome prediction
9.2.3. Protocol development
9.2.4. Safety
9.2.5. Training
9.3. Motion simulation challenges
9.3.1. Motion complexity
9.3.2. Motion interactions with magnetic fields
9.3.3. Assumptions and simplifications
9.4. Digital phantoms for motion simulation
9.4.1. Digital phantom purposes
9.4.1.1. Digital flow simulations
9.4.1.2. Digital cardiac, respiratory, and other simulations
9.4.2. Digital phantom challenges
9.4.2.1. Computational burden
9.4.2.2. Assumptions and simplifications
9.4.2.3. Flow-specific simulation limitations
9.4.3. General image simulation approaches
9.4.4. MR image simulation approaches
9.4.4.1. Isochromat summation
9.4.4.2. k-space method
9.4.5. Rigid body numerical motion simulations
9.4.6. Nonrigid body numerical motion simulations
9.4.6.1. Shape-based methods
9.4.6.2. Data-based and hybrid methods
9.4.7. Numerical flow simulations
9.4.7.1. k-space methods
9.4.7.2. Lagrangian methods
9.4.7.3. Eulerian methods
9.4.7.4. Hybrid methods
9.4.7.5. MRI-based flow simulations
9.5. Physical phantoms for motion simulation
9.5.1. Physical phantom purposes
9.5.1.1. Testing and quality assurance
9.5.1.2. Anatomical and motion reproducibility
9.5.1.3. Clinical translation of physical motion phantoms
9.5.2. Physical phantom challenges
9.5.2.1. Difficulty of in vivo modeling
9.5.2.2. Material concerns
9.5.2.3. Imaging concerns
9.5.3. Rigid-body physical motion phantoms
9.5.4. Nonrigid-body physical motion phantoms
9.5.5. Physical flow phantoms
9.6. Motion simulation phantoms summary
References
Chapter 10: Operational analytics using modality log files
10.1. Introduction
10.1.1. The need for accurate operational data
10.1.2. Sources of operational data
10.2. Modality log files
10.2.1. Reconstructing the imaging workflow
10.2.2. Technical considerations
10.3. Imaging workflow analytics
10.3.1. The prevalence of aborted and repeated sequences
10.3.2. The impact of motion on aborted and repeated sequences
10.3.3. The impact of motion on the radiology workflow
10.4. Conclusions and outlook
References
Part 4: Dynamic effects that compromise scan quality in MRI
Chapter 11: Types of motion
11.1. Introduction
11.2. Brief review of MR physics
11.3. Defining and characterizing motion and motion artifacts
11.4. Special considerations, an organ-based approach
11.4.1. Head and neck
11.4.1.1. CSF Pulsation
11.4.1.2. Echo-planar imaging
11.4.1.3. Head motion in fMRI
11.4.1.4. Tongue motion and swallowing
11.4.1.5. Arterial spin labeling
11.4.2. Cardiothoracic
11.4.2.1. Cardiac motion
11.4.2.2. Respiratory motion
11.4.2.3. Blood flow
11.4.3. Abdomen and pelvis
11.4.3.1. Abdominal motion
11.4.3.2. Pelvic motion
11.4.4. Musculoskeletal
11.5. Conclusion
References
Chapter 12: Other dynamic changes
12.1. Initial scanner adjustments
12.1.1. Frequency adjustments
12.1.2. Transmitter gain adjustment
12.1.3. Initial shim
12.2. Subsequent changes caused by physiology
12.2.1. Blood flow pulsatility
12.2.2. CSF pulsation
12.2.3. Physiological brain motion
12.2.4. Correction strategies
12.3. Changes in B0
12.3.1. 0th spatial order B0 changes
12.3.2. Higher spatial order B0 changes
12.3.2.1. Origins of B0 variations in the head
12.3.2.2. Respiration-induced fieldmap changes
12.3.2.3. Changes related to the heartbeat
12.3.2.4. Movement of the head in the field
12.3.3. Conclusion regarding B0 effects
12.4. B1 changes associated with motion
12.4.1. Dynamic B1 transmit changes
12.4.2. Conclusion regarding B1 effects
References
Part 5: Methods of detecting motion and associated field changes in real time
Chapter 13: External tracking systems
13.1. General concepts
13.2. Types of external tracking system
13.2.1. Attached sensors
13.2.2. Camera systems using markers
13.2.3. Camera systems without markers
13.2.4. Advantages and challenges
13.3. Future directions
References
Chapter 14: k-Space navigators
14.1. Introduction
14.2. Rigid-body motion in k-space
14.3. Specific navigator designs
14.3.1. Central k-space navigators
14.3.2. Linear k-space navigators
14.3.3. Orbital k-space navigators
14.3.4. Spherical k-space navigators
14.3.5. Cloverleaf k-space navigators
14.4. Summary
References
Chapter 15: Image-space navigators
15.1. Introduction
15.2. 1D navigators
15.3. 2D navigators
15.4. 2D/3D ``hybrid´´ navigators
15.5. 3D navigators
15.6. Self-navigation
15.7. Nonwater navigators
15.8. Image-based navigator considerations
15.9. Pros and cons of image-based approaches
15.10. Summary
References
Chapter 16: Navigators without gradients
16.1. Introduction
16.1.1. The free induction decay navigator module
16.1.2. Early applications of FID navigators
16.1.3. FIDnavs from multichannel coil arrays
16.2. Motion detection with FID navigators
16.2.1. Principles of FID navigator motion detection
16.2.2. Applications of FID navigator motion detection
16.3. Motion measurement with FID navigators
16.3.1. Decoding quantitative motion measurements from FID navigators
16.3.2. Principles of FID navigator motion measurement
16.3.2.1. FIDnav motion model
16.3.2.2. Modeling the CSPs
16.3.2.3. Accuracy of FIDnav motion measurements
16.3.3. Applications of FID navigator motion measurement
16.3.3.1. Retrospective motion correction
16.3.3.2. Prospective steering
16.3.3.3. Extension to other sequences
16.4. B0 field measurement with FID navigators
16.4.1. Principles of FID navigator B0 field measurement
16.4.1.1. SENSE shimming
16.4.1.2. FIDnav B0 field measurement model
16.4.1.3. Multichannel reference image
16.4.1.4. Accuracy of FIDnav field measurements
16.4.2. Applications of FID navigator B0 field measurement
16.4.2.1. Retrospective correction of anatomical and functional imaging
16.4.2.2. Real-time shimming
16.4.2.3. Scope of field correction
16.5. Discussion
16.5.1. Related works
16.5.1.1. Self-navigation signal
16.5.1.2. Pilot tone navigators
16.5.2. Spatial encoding from reference image data
16.5.3. Nonrigid motion
16.5.4. Simultaneous estimation of motion and field
16.6. Conclusion
References
Part 6: Retrospective correction
Chapter 17: Retrospective correction of motion in MR images
17.1. Retrospective correction
17.2. Applying corrections
17.2.1. Image domain corrections
17.2.2. k-space domain corrections
17.2.3. Matrix description of general motion correction
17.3. Determining motion
17.3.1. Motion parameterization
17.3.2. Autofocus
17.3.3. Consistency and joint optimization
17.3.4. Motion from data
17.4. Adoption of retrospective methods
17.5. Summary
References
Chapter 18: Effects of motion in sparsely sampled acquisitions
18.1. Introduction
18.2. Choice of k-space versus time sampling
18.2.1. Phantom simulation
18.2.2. Challenges associated with non-Cartesian sampling
18.3. Parallel imaging and generalized spatiotemporal models
18.3.1. Parallel imaging
18.3.2. Generalized spatiotemporal models
18.4. Explicit motion estimation and motion-compensated reconstruction
18.5. Implicit motion-compensated reconstruction
18.6. Manifold regularization
18.7. Need for validation studies
References
Chapter 19: Retrospective correction of dynamic B0 field variations
19.1. Introduction
19.2. Impact of B0 field variations
19.2.1. Dynamic DeltaB0 in Fourier encoding
19.2.1.1. Cartesian sampling
19.2.1.2. Echo planar imaging (EPI)
19.2.1.3. Non-Cartesian imaging
19.2.2. Excitation and spin history
19.3. Retrospective correction of dynamic B0 fields
19.3.1. Image reconstruction
19.3.1.1. Homogeneous DeltaB0
19.3.1.2. Linear DeltaB0
19.3.1.3. Higher-order DeltaB0
19.3.2. Image processing/analysis
19.3.3. MR spectroscopy
References
Chapter 20: Machine learning
20.1. Introduction
20.2. Image-based motion correction
20.2.1. Network architecture
20.2.2. Dataset generation
20.2.3. Results and challenges
20.3. Motion correction based on raw data
20.4. Outlook
References
Part 7: Prospective correction
Chapter 21: Prospective motion correction
21.1. Introduction
21.2. Theory of prospective motion correction
21.3. Implementation of prospective motion correction
21.3.1. Real-time measurement control considerations
21.3.2. Error propagation in prospective motion correction
21.3.3. Advanced topics on implementation of position and orientation updates
21.4. Challenges and advantages of prospective motion correction
21.5. Rejection and reacquisition
21.6. Conclusions and outlook
References
Chapter 22: Prospective B0 correction
22.1. Motion-induced fluctuations in the magnetic field
22.2. Advantages of prospective B0 field correction
22.3. Hardware for prospective B0 updating
22.4. Approaches for prospective updating
22.5. Prospective B0 updating in different sequences
22.5.1. T2*-weighted imaging
22.5.2. Phase-based imaging
22.5.3. MR spectroscopy
22.5.4. CEST
22.6. Conclusion
References
Part 8: Clinical applications beyond the brain
Chapter 23: Body imaging
23.1. Introduction
23.2. Multidirectional motion
23.2.1. Physical movement
23.2.2. Physiological motion: Peristalsis
23.3. Bidirectional motion
23.3.1. Respiratory and cardiovascular motion
23.3.1.1. Abdomen
23.3.1.2. Prone positioning
23.3.1.3. Supine positioning
23.3.1.4. Pelvis
23.3.2. Image domain-based motion compensation using gating methods
23.3.3. K-space domain-based motion compensation using radial sampling with gating
23.3.3.1. T2-weighted applications
23.3.3.2. T1-weighted applications
Nondynamic acquisitions
Dynamic acquisitions
23.4. Emerging techniques for motion correction
References
Chapter 24: MR motion correction in musculoskeletal imaging
24.1. Introduction
24.1.1. Why is motion a problem?
24.1.2. Types of motion in musculoskeletal (MSK) MR imaging
24.1.3. Possible mechanisms for data corruption
24.2. Motion artifact mitigation strategies
24.2.1. Motion prevention
24.2.2. Positioning strategies
24.2.2.1. Elbow
24.2.2.2. Knee
24.2.2.3. Foot/Ankle
24.2.3. Fast imaging
24.2.3.1. Parallel Imaging (PI)
24.2.3.2. Compressed Sensing (CS)
24.2.3.3. Simultaneous Multislice Acceleration (SMS)
24.2.3.4. Synthetic MR
24.2.3.5. Deep Learning (DL)
24.2.4. MR navigator methods
24.2.4.1. MR self-navigated methods
24.2.5. External tracking devices for prospective motion correction
24.2.6. Retrospective motion correction using deep learning
24.3. Dynamic MR in musculoskeletal imaging
Summary
References
Chapter 25: Cardiac imaging
25.1. Introduction
25.2. Motion minimization strategies
25.2.1. Cardiac-induced motion
25.2.2. Respiratory-induced motion
25.3. Coronary magnetic resonance angiography
25.4. Cine
25.5. Cardiac quantitative mapping
25.6. First-pass perfusion
25.7. Late gadolinium enhancement
25.8. Flow
25.9. Concluding remarks
References
Part 9: Technical applications by method
Chapter 26: MR Spectroscopy (MRS), Chemical Exchange Saturation Transfer (CEST), and Magnetization Transfer (MT)
26.1. Introduction
26.2. Motion correction in MRS
26.2.1. Retrospective motion correction
26.2.2. Prospective motion correction
26.2.2.1. Internal navigator-based motion correction methods for MRS
26.2.2.1.1. Image-based navigators
26.2.2.1.2. k-Space navigators
26.2.2.1.3. FID and shim-only navigators
26.2.2.2. External tracking and hybrid motion correction methods for MRS
26.3. Motion correction in CEST MR
26.3.1. Retrospective motion correction
26.3.2. Prospective motion correction
26.4. Conclusions and future directions
References
Chapter 27: High-resolution structural brain imaging
27.1. Introduction
27.2. State-of-the-art
27.2.1. High-resolution MRI with navigators
27.2.2. High-resolution MRI with field probes
27.2.3. High-resolution MRI with optical tracking
27.2.4. Summary
27.3. Open challenges and limitations
27.3.1. SNR
27.3.2. Motion correction accuracy and effectiveness
27.3.3. Nonrigidity
References
Chapter 28: Amplified MRI and physiological brain tissue motion
28.1. Introduction
28.2. Methodology
28.2.1. aMRI sequence
28.2.2. Amplification algorithm
28.2.3. Performance of aMRI
28.2.3.1. Rigid-body motion
28.2.4. Importance of extracting amplified brain motion
28.3. Clinical applications of amplified brain motion
28.3.1. Intracranial pressure
28.3.2. Chiari malformation Type I (CM I)
28.3.3. Applications for hydrocephalus
28.3.4. Synergy between aMRI and MR elastography (MRE)
28.3.5. Mild traumatic brain injury (mTBI)
28.3.6. Aneurysms
28.4. Summary
References
Chapter 29: Diffusion imaging
29.1. Introduction
29.2. MRI with sensitivity to microscopic motion
29.3. Short-term subject motion
29.3.1. Effect of motion during diffusion encoding
29.3.2. Velocity compensation
29.3.3. Cardiac gating
29.3.4. Single-shot diffusion imaging
29.3.5. Multishot diffusion imaging with phase navigators
29.3.6. Developments in multishot diffusion imaging
29.4. Long-term subject motion
29.4.1. Reduction and prevention of the subject's motion
29.4.2. Retrospective correction of motion effects in diffusion MRI
29.4.3. Prospective reacquisition of image volumes to reduce the effect of signal drop-outs
29.4.4. Prospective motion correction to reduce bulk subject motion during the scan
29.4.4.1. Image-based approaches for prospective motion correction
29.4.4.2. Image-navigator-based approaches for prospective motion correction
29.4.4.3. Hybrid approaches for prospective motion correction
29.4.4.4. Optical tracking for prospective motion correction
29.5. Summary and future perspectives
References
Further reading
Chapter 30: Non-cartesian imaging
30.1. Introduction
30.2. Aliasing properties
30.3. Trajectories
30.3.1. 2D and 3D radial
30.3.2. 2D spiral
30.3.3. 3D cones
30.3.4. Low-coherence 3D trajectories
30.3.5. Hybrid methods
30.4. Reconstruction
30.4.1. Density compensation
30.4.2. Convolution interpolation
30.4.3. Correction of gradient imperfections
30.5. Motion compensation and synchronization
30.5.1. Self-gating capabilities
30.5.2. Resolving motion
30.6. Applications
30.6.1. Real-time cardiac imaging
30.6.2. N-D imaging
30.6.3. Hyperpolarization
30.7. Conclusion
References
Chapter 31: Functional MRI
31.1. Introduction
31.2. Kinds of brain motion
31.3. Identifying motion in fMRI
31.4. Influence of motion on fMRI signals
31.5. Consequences of motion for fMRI
31.6. Correlates of motion in fMRI
31.7. Amelioration of motion artifact
31.8. Prospective motion correction in fMRI
31.9. Image reconstruction errors in fMRI data caused by motion
31.10. Conclusions
Conflict of interest
References
Part 10: Special applications
Chapter 32: Fetal and placental imaging
32.1. Introduction
32.2. Fetal imaging
32.2.1. Motion artifact minimization via patient preparation
32.2.2. Fast imaging acquisitions
32.2.3. Motion-resistant acquisitions and gating techniques
32.2.4. Approaches for auto-slice prescription
32.2.5. Motion correction for structural imaging
32.2.6. Motion correction for functional imaging
32.3. Placental imaging
32.3.1. Motion correction for structural imaging and 3D placental visualization
32.3.2. Motion correction for functional imaging
32.4. Summary and future directions
References
Chapter 33: Special considerations for unsedated MR in the young pediatric population
33.1. Introduction
33.2. Strategies for successful unsedated MR studies
33.2.1. Equipment and supplies
33.2.1.1. Mock MR scanners
33.2.1.2. Customized molds for immobilization and bore foam inserts
33.2.1.3. Additional supplies to consider
33.2.2. Child-friendly procedures
33.2.2.1. Child-friendly environment
33.2.2.2. Performing successful MR exams during natural sleep
33.2.2.3. Performing successful MR exams during wakefulness
33.2.2.4. Performing successful MR exams of children with neurodevelopmental disorders
33.2.3. Optimizing success of novel imaging strategies
33.2.3.1. Considerations related to motion detection approaches when scanning children
33.2.3.2. Ultra-fast MR acquisitions
33.2.3.3. Silent MRI acquisitions
33.3. Conclusions
Acknowledgments
Appendix
Referencess
Chapter 34: MR-assisted PET motion correction in PET/MR
34.1. Introduction
34.2. Approaches to use MR-derived motion estimates for PET data motion correction
34.2.1. Before image reconstruction
34.2.2. During image reconstruction
34.2.3. After image reconstruction
34.3. Proof-of-principle studies that demonstrated technical feasibility
34.3.1. Head motion
34.3.2. Respiratory motion
34.3.3. Cardiac contractile motion
34.3.4. Cardiorespiratory motion of the heart
34.4. Potential benefits of motion correction in research and clinical PET/MR studies
34.4.1. Dynamic imaging for research applications
34.4.2. Imaging of subjects predisposed to head motion
34.4.3. Imaging of primary and metastatic thoracic lesions
34.4.4. Imaging of primary and metastatic lesions in the upper abdomen
34.4.5. Imaging of the coronary arteries
34.5. Conclusion
References
Chapter 35: Small animal imaging
35.1. Introduction
35.2. Animal handling
35.2.1. General considerations
35.2.2. Anesthesia protocols
35.2.3. Physiological parameters and physiological monitoring
35.3. Hardware
35.3.1. System /HW overview
35.3.1.1. Main magnet
35.3.1.2. Gradient coils
35.3.1.3. Shim coils
35.3.1.4. RF coils
35.4. Motion synchronization/compensation
35.4.1. Prospective and retrospective gating
35.4.2. Sensor-based approaches
35.4.2.1. Respiratory gating sensors
35.4.2.2. Cardiac gating sensors
35.4.2.3. Synchronization, challenges, and limitations
35.4.3. Self-gating (SG)
35.4.3.1. Gating strategies
35.4.3.2. Advantages, challenges, and limitations
35.4.3.3. Signal extraction
35.4.3.4. Trigger detection
35.5. Reconstruction approaches and sequences
35.5.1. Common trajectories
35.5.2. Image reconstruction
35.5.2.1. Single channel coil reconstruction
35.5.2.2. Multichannel coil reconstruction
35.5.2.3. Sparse reconstruction
35.6. Applications
35.7. Conclusion
References
Index
Back Cover