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E-grāmata: Introduction to Neuroimaging Analysis

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(Professor of Neuroimaging, Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford), (Associate Professor of Engineering Science, Institute of Biomedical Engineeri)
  • Formāts: 208 pages
  • Sērija : Oxford Neuroimaging Primers
  • Izdošanas datums: 15-Mar-2018
  • Izdevniecība: Oxford University Press
  • Valoda: eng
  • ISBN-13: 9780192548276
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Introduction to Neuroimaging AnalysisPalielināt
  • Formāts: 208 pages
  • Sērija : Oxford Neuroimaging Primers
  • Izdošanas datums: 15-Mar-2018
  • Izdevniecība: Oxford University Press
  • Valoda: eng
  • ISBN-13: 9780192548276
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MRI has emerged as a powerful way of studying in-vivo brain structure and function in both healthy and disease states. Whilst new researchers may be able to call upon advice and support for acquisition from operators, radiologists and technicians, it is more challenging to obtain an understanding of the principles of analysing neuroimaging data. This is crucial for choosing acquisition parameters, designing and performing appropriate experiments, and correctly interpreting the results.

This primer gives a general and accessible introduction to the wide array of MRI-based neuroimaging methods that are used in research. Supplemented with online datasets and examples to enable the reader to obtain hands-on experience working with real data, it provides a practical and approachable introduction for those new to the neuroimaging field. The text also covers the fundamentals of what different MRI modalities measure, what artifacts commonly occur, the essentials of the analysis, and common 'pipelines' including brain extraction, registration and segmentation.

As it does not require any background knowledge beyond high-school mathematics and physics, this primer is essential reading for anyone wanting to work in neuroimaging or grasp the results coming from this rapidly expanding field.

The Oxford Neuroimaging Primers are short texts aimed at new researchers or advanced undergraduates from the biological, medical or physical sciences. They are intended to provide a broad understanding of the ways in which neuroimaging data can be analyzed and how that relates to acquisition and interpretation. Each primer has been written so that it is a stand-alone introduction to a particular area of neuroimaging, and the primers also work together to provide a comprehensive foundation for this increasingly influential field.
1 Introduction
1(22)
1.1 Main MRI modalities and analysis techniques
2(7)
1.2 Walk-through of a typical study
9(6)
Example Box: Look at your data!
11(2)
Example Box: Keep looking at your data!
13(1)
Example Box: Think through your analysis early
14(1)
1.3 MR physics and scanner hardware
15(6)
Box 1.1 B0 field inhomogeneities
17(1)
Box 1.2 Resonance frequency
17(1)
Box 1.3 RF inhomogeneity and bias fields
18(1)
Box 1.4 Head coils and accelerated imaging
19(1)
Box 1.5 Gradient fields
20(1)
1.4 Overview
21(2)
Summary
22(1)
Further reading
22(1)
2 MRI Modalities for Neuroimaging
23(62)
2.1 Image fundamentals
23(4)
Box 2.1 Image formats
26(1)
2.2 Structural MRI
27(16)
Example Box: SNR, resolution, and acquisition time trade-offs
33(6)
Example Box: MRI artifacts
39(3)
Summary
42(1)
Further reading
43(1)
2.3 Diffusion MRI
43(13)
Example Box: Diffusion signal in different tissues
48(1)
Box 2.2 HARDI
49(1)
Example Box: Diffusion signal in different tissues (revisited)
50(1)
Example Box: Viewing diffusion images
51(4)
Summary
55(1)
Further reading
56(1)
2.4 Functional MRI
56(13)
Box 2.3 T2* Relaxation and BOLD
58(10)
Summary
68(1)
Further reading
68(1)
2.5 Perfusion MRI
69(7)
Summary
75(1)
Further reading
75(1)
2.6 Spectroscopy (MRS)
76(3)
Summary
78(1)
Further reading
79(1)
2.7 Complementary techniques
79(6)
Summary
83(1)
Further reading
83(2)
3 Overview of MRI Analysis
85(60)
Group Analysis Box
86(3)
3.1 Early stages
89(3)
Box 3.1 Voxel and world coordinates
91(1)
Summary
92(1)
3.2 Structural pipeline
92(13)
Example Box: Tissue-type segmentation
96(1)
Box 3.2 Segmentation of pathological tissue
97(2)
Example Box: Deep gray matter structure segmentation
99(5)
Summary
104(1)
Further reading
104(1)
3.3 Diffusion pipeline
105(11)
Box 3.3 Fieldmap acquisitions
106(1)
Example Box: Correcting for distortions and artifacts in diffusion images
107(2)
Box 3.4 Diffusion tensor model
109(1)
Example Box: DTI
110(2)
Example Box: TBSS
112(2)
Example Box: Tractography
114(1)
Summary
115(1)
Further reading
115(1)
3.4 Functional pipeline (task and resting state fMRI)
116(21)
Box 3.5 Motion artifacts
118(2)
Example Box: Slice timing effects and corrections
120(4)
Example Box: Aliasing of physiological signals
124(2)
Example Box: Physiological noise modeling
126(2)
Example Box: Independent component classification
128(5)
Example Box: Task fMRI data
133(1)
Example Box: Resting state fMRI networks
134(1)
Summary
135(1)
Further reading
136(1)
3.5 Perfusion pipeline
137(8)
Summary
143(1)
Further reading
143(2)
4 Brain Extraction
145(12)
4.1 When it is needed
146(2)
4.2 Skull stripping versus cortical modeling
148(2)
Example Box: Brain extraction
150(1)
4.3 Brain masks
150(2)
Example Box: Brain masks
151(1)
4.4 Skull estimation
152(1)
4.5 Difficulties and troubleshooting
153(4)
Example Box: Troubleshooting brain extraction
154(1)
Summary
154(1)
Further reading
155(2)
5 Registration
157(44)
5.1 Spatial transformations
157(10)
Box 5.1 Transformation parameters, coordinates, and conventions
160(7)
Example Box: Spatial Transformations
167(1)
5.2 Cost functions
167(6)
Box 5.2 Similarity functions
168(1)
Box 5.3 Intensity models for cost functions
169(2)
Example Box: Registration
171(2)
5.3 Resampling and interpolation
173(9)
Box 5.4 Symmetric registration and halfway spaces
179(3)
5.4 Case studies
182(8)
Example Box: Evaluating registrations
183(7)
Example Box: Registration case studies
190(1)
5.5 Standard space and templates
190(2)
5.6 Atlases
192(5)
Example Box: Probabilistic atlas construction
195(2)
5.7 Atlas-based segmentation
197(4)
Summary
198(1)
Further reading
199(2)
6 Motion and Distortion Correction
201(22)
6.1 Motion correction
201(4)
6.2 Distortion correction
205(18)
Box 6.1 Gradient-echo fieldmap acquisitions
208(1)
Box 6.2 Calculating distortion from gradient-echo fieldmaps
209(4)
Box 6.3 Intensity model for BBR
213(1)
Box 6.4 Low contrast EPI and registration
213(1)
Box 6.5 Combining fieldmap-based distortion correction and registration
214(1)
Example Box: Distortion correction with fieldmaps
215(2)
Example Box: Distortion correction with blip-up-blip-down data
217(3)
Summary
220(1)
Further reading
220(3)
7 Surface-Based Analysis
223(12)
7.1 Cortical surface extraction
223(4)
7.2 Inflated, spherical, and flattened surfaces
227(1)
7.3 Registration of surface data
227(2)
7.4 Surface-based fMRI analysis
229(6)
Summary
232(1)
Further reading
233(2)
8 Epilogue
235(4)
8.1 Planning
235(1)
8.2 Analysis
236(1)
8.3 Interpretation
237(1)
8.4 Next steps
237(2)
Appendix A Short Introduction to Brain Anatomy for Neuroimaging
239(12)
A.1 Brain cells and tissues
239(2)
A.2 Navigating around brain images
241(4)
A.3 Brain structures
245(6)
Example Box: Learning anatomy with digital atlases
247(1)
Summary
248(1)
Further reading
248(3)
Index 251
Mark Jenkinson is a co-founder and the principal developer of FSL (FMRIB Software Library) and has written numerous tools for analysis of structural, diffusion and functional data. He has been a part of the FMRIB centre, University of Oxford, since 1998, working on analysis methodology for MRI-based neuroimaging research and is included in Thomson Reuters' list of Highly Cited Researchers. From 2002 he has been teaching neuroimaging analysis to students and researchers from a wide variety of backgrounds (medicine, psychology, physiology, engineering, physics, and philosophy) through both annual FSL Courses and the FMRIB Graduate Programme. He has won teaching awards from the University of Oxford and the International Society of Magnetic Resonance in Medicine (ISMRM) and authored over 150 journal papers and book chapters covering a wide range of methodology and applications in MRI-based neuroimaging.

Michael Chappell is an Associate Professor of Engineering Science in the Institute of Biomedical Engineering, University of Oxford. He remains a member of the Oxford Centre for Functional MRI of the Brain (FMRIB) where he started his work on the quantification and analysis of Arterial Spin Labelling perfusion MRI data. He is an active developer on the FMIRB Software Library, providing tools for the analysis of perfusion MRI. Since 2014 he has been the Director of Training for the EPSRC-MRC Centre for Doctoral Training in Biomedical Imaging. He has taught undergraduate and postgraduate students on a wide variety of imaging and quantitative physiology topics as part of the FSL course, FMRIB Graduate Programme, the undergraduate degree in Engineering Science and in various doctoral training centres. He has also won teaching awards from the International Society of Magnetic Resonance in Medicine. He has published over 50 journal papers and is the co-author of Physiology for Engineers.
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