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Computational Diffusion MRI and Brain Connectivity: MICCAI Workshops, Nagoya, Japan, September 22nd, 2013 2014 ed. [Kõva köide]

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  • Formaat: Hardback, 255 pages, kõrgus x laius: 235x155 mm, kaal: 5325 g, 67 Illustrations, color; 11 Illustrations, black and white; XIV, 255 p. 78 illus., 67 illus. in color., 1 Hardback
  • Sari: Mathematics and Visualization
  • Ilmumisaeg: 27-Jan-2014
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319024744
  • ISBN-13: 9783319024745
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Computational Diffusion MRI and Brain Connectivity: MICCAI Workshops, Nagoya, Japan, September 22nd, 2013 2014 ed.Suurem pilt
  • Formaat: Hardback, 255 pages, kõrgus x laius: 235x155 mm, kaal: 5325 g, 67 Illustrations, color; 11 Illustrations, black and white; XIV, 255 p. 78 illus., 67 illus. in color., 1 Hardback
  • Sari: Mathematics and Visualization
  • Ilmumisaeg: 27-Jan-2014
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319024744
  • ISBN-13: 9783319024745
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783319024745.html
Märksõnad:
This volume contains the proceedings from two closely related workshops: Computational Diffusion MRI (CDMRI13) and Mathematical Methods from Brain Connectivity (MMBC13), held under the auspices of the 16th International Conference on Medical Image Computing and Computer Assisted Intervention, which took place in Nagoya, Japan, September 2013.





Inside, readers will find contributions ranging from mathematical foundations and novel methods for the validation of inferring large-scale connectivity from neuroimaging data to the statistical analysis of the data, accelerated methods for data acquisition, and the most recent developments on mathematical diffusion modeling.





This volume offers a valuable starting point for anyone interested in learning computational diffusion MRI and mathematical methods for brain connectivity as well as offers new perspectives and insights on current research challenges for those currently in the field. It will be of interest to researchers and practitioners in computer science, MR physics, and applied mathematics.
Part I Acquisition of Diffusion MRI
Comparing Simultaneous Multi-slice Diffusion Acquisitions
3(10)
Yogesh Rathi
Borjan Gagoski
Kawin Setsompop
P. Ellen Grant
C.-F. Westin
1 Introduction
4(1)
2 Our Contributions
5(1)
3 Methods
5(1)
4 Experiments
6(4)
5 Conclusion
10(3)
References
10(3)
Effect of Data Acquisition and Analysis Method on Fiber Orientation Estimation in Diffusion MRI
13(12)
Bryce Wilkins
Namgyun Lee
Vidya Rajagopalan
Meng Law
Natasha Lepore
1 Introduction
14(1)
2 Materials and Methods
14(2)
2.1 Diffusion-Weighted Data Synthesis
14(1)
2.2 Quantitative Metrics
15(1)
3 Application: Comparison of Fiber Estimation of Several Diffusion Analysis Methods
16(6)
3.1 Establishment of Ground-Truth
16(1)
3.2 Data Synthesis
17(1)
3.3 Data Analysis
17(2)
3.4 Results
19(3)
4 Discussion and Conclusion
22(3)
References
23(2)
Model-Based Super-Resolution of Diffusion MRI
25(10)
Alexandra Tobisch
Peter F. Neher
Matthew C. Rowe
Klaus H. Maier-Hein
Hui Zhang
1 Introduction
26(2)
2 Model-Based Super-Resolution Reconstruction
28(3)
2.1 Forward Model
28(1)
2.2 Super-Resolution Reconstruction
29(1)
2.3 SRR Optimization Procedure
30(1)
3 Evaluation and Results
31(2)
3.1 Evaluation
31(1)
3.2 Results
31(2)
4 Discussion
33(2)
References
33(2)
A Quantitative Evaluation of Errors Induced by Reduced Field-of-View in Diffusion Tensor Imaging
35(12)
Jan Hering
Ivo Wolf
Hans-Peter Meinzer
Bram Stieltjes
Klaus H. Maier-Hein
1 Introduction
36(1)
2 Methods
37(3)
2.1 MRI Acquisition
37(1)
2.2 Eddy Current and Head Motion Correction Schemes
38(1)
2.3 Registration Parameters
38(1)
2.4 Performance Metrics
39(1)
3 Results
40(2)
3.1 Registration Parameters
40(1)
3.2 Precision of Registration
40(1)
3.3 Tensor Fit Quality
40(2)
3.4 Deviation in Fractional Anisotropy
42(1)
4 Discussion
42(5)
References
43(4)
Part II Diffusion MRI Modeling
The Diffusion Dictionary in the Human Brain Is Short: Rotation Invariant Learning of Basis Functions
47(10)
Marco Reisert
Henrik Skibbe
Valerij G. Kiselev
1 Introduction
47(2)
2 Method
49(2)
2.1 Representation of Basis Functions
50(1)
2.2 Implementation and Optimization
50(1)
3 Experiments
51(3)
4 Conclusion
54(3)
References
55(2)
Diffusion Propagator Estimation Using Radial Basis Functions
57(10)
Yogesh Rathi
Marc Niethammer
Frederik Laun
Kawin Setsompop
Oleg Michailovich
P. Ellen Grant
C.-F. Westin
1 Introduction
58(1)
2 Our Contributions
58(1)
3 Data Representation Using Radial Basis Functions (RBF)
59(4)
3.1 Application to Diffusion MRI
60(1)
3.2 Estimating the ADP with Radial Basis Functions
60(2)
3.3 Computing the Orientation Distribution Function (ODF)
62(1)
3.4 Estimation Procedure
62(1)
4 Experiments
63(2)
4.1 In-Vivo Results
63(2)
5 Conclusion
65(2)
References
66(1)
A Framework for ODF Inference by Using Fiber Tract Adaptive MPG Selection
67(14)
Hidekata Hontani
Kazunari Iwamoto
Yoshitaka Masutani
1 Introduction
67(3)
1.1 Background
67(1)
1.2 Problem Statement and Objective
68(2)
2 Proposed Method
70(2)
2.1 Interpolation with SRBF
70(1)
2.2 Optimization
70(2)
2.3 Preprocessing
72(1)
3 Experimental Method
72(3)
3.1 Simulation Experiments
73(1)
3.2 Phantom Experiments
74(1)
3.3 Clinical Image Experiments
75(1)
4 Experimental Results
75(3)
4.1 Simulation Experiments
75(2)
4.2 Phantom Experiments
77(1)
4.3 Clinical Image Experiments
78(1)
5 Conclusion
78(3)
References
78(3)
Non-negative Spherical Deconvolution (NNSD) for Fiber Orientation Distribution Function Estimation
81(16)
Jian Cheng
Rachid Deriche
Tianzi Jiang
Dinggang Shen
Pew-Thian Yap
1 Introduction
82(1)
2 Background on SD Methods
83(2)
3 Non-negative Spherical Deconvolution (NNSD)
85(2)
4 Experiments
87(4)
4.1 High-Resolution Data
90(1)
5 Discussion and Conclusion
91(6)
References
92(5)
Part III Tractography
A Novel Riemannian Metric for Geodesic Tractography in DTI
97(8)
Andrea Fuster
Antonio Tristan-Vega
Tom Dela Haije
Carl-Fredrik Westin
Luc Florack
1 Introduction
97(1)
2 Theory
98(2)
2.1 Preliminaries
98(1)
2.2 Riemannian Framework Revisited
99(1)
3 Experiments
100(2)
3.1 Method
100(1)
3.2 Results
101(1)
4 Conclusion and Discussion
102(3)
References
103(2)
Fiberfox: An Extensible System for Generating Realistic White Matter Software Phantoms
105(10)
Peter F. Neher
Frederik B. Laun
Bram Stieltjes
Klaus H. Maier-Hein
1 Introduction
106(1)
2 Materials and Methods
107(3)
2.1 Fiber Definition
107(1)
2.2 Signal Generation
107(1)
2.3 Artifact Simulation
108(1)
2.4 Simulations and Experiments
109(1)
3 Results
110(2)
4 Discussion and Conclusion
112(3)
References
112(3)
Choosing a Tractography Algorithm: On the Effects of Measurement Noise
115(14)
Andre Reichenbach
Mario Hlawitschka
Marc Tittgemeyer
Gerik Scheuermann
1 Introduction
115(2)
2 Material and Methods
117(4)
2.1 Data Acquisition and Subjects
117(1)
2.2 Creation of the Reference Dataset
118(1)
2.3 Choice of Algorithms and Parameters
119(1)
2.4 Evaluating Tractography Robustness
120(1)
3 Results
121(3)
4 Discussion
124(3)
5 Conclusion
127(2)
References
127(2)
Uncertainty in Tractography via Tract Confidence Regions
129(10)
Colin J. Brown
Brian G. Booth
Ghassan Hamarneh
1 Introduction
129(2)
2 Method
131(2)
2.1 Path Confidence Regions
131(1)
2.2 Confidence Region Visualization
132(1)
3 Results
133(4)
4 Conclusions
137(2)
References
137(2)
Estimating Uncertainty in White Matter Tractography Using Wild Non-local Bootstrap
139(12)
Pew-Thian Yap
Hongyu An
Yasheng Chen
Dinggang Shen
1 Introduction
140(1)
2 Approach
141(2)
2.1 Non-local Estimation as Non-parametric Kernel Regression
141(1)
2.2 Wild Non-local Bootstrap (W-NLB)
142(1)
2.3 Kernel and Bandwidth
143(1)
3 Experimental Results
143(3)
3.1 In Silico
143(2)
3.2 In Vivo
145(1)
4 Conclusion
146(5)
References
147(4)
Part IV Group Studies and Statistical Analysis
Groupwise Deformable Registration of Fiber Track Sets Using Track Orientation Distributions
151(12)
Daan Christiaens
Thijs Dhollander
Frederik Maes
Stefan Sunaert
Paul Suetens
1 Introduction
151(1)
2 Methods
152(2)
2.1 Track Orientation Distribution
152(1)
2.2 TOD Registration and Reorientation
153(1)
3 Experiments and Results
154(4)
3.1 Data, Processing and Fiber Tracking
154(1)
3.2 Experiment 1: Synthetically Deformed Single Subject
155(1)
3.3 Experiment 2: Multiple Subjects
156(2)
4 Discussion
158(1)
5 Conclusion and Future Work
159(4)
References
160(3)
Groupwise Registration for Correcting Subject Motion and Eddy Current Distortions in Diffusion MRI Using a PCA Based Dissimilarity Metric
163(12)
W. Huizinga
C.T. Metz
D.H.J. Poot
M. de Groot
W.J. Niessen
A. Leemans
S. Klein
1 Introduction
164(1)
2 Method
164(5)
2.1 Groupwise Registration Framework
164(1)
2.2 Dissimilarity Metric
165(1)
2.3 Metric Derivative
166(1)
2.4 Transformation Model
167(1)
2.5 Optimization
168(1)
2.6 Groupwise Approaches Proposed by Others
168(1)
2.7 Implementation
168(1)
3 Experiments and Results
169(3)
3.1 Synthetic Data
169(2)
3.2 Real Diffusion Weighted Data
171(1)
4 Conclusions
172(3)
References
173(2)
Fiber Based Comparison of Whole Brain Tractographies with Application to Amyotrophic Lateral Sclerosis
175(12)
Gali Zimmerman-Moreno
Dafna ben Bashat
Moran Artzi
Beatrice Nefussy
Vivian Drory
Orna Aizenstein
Hayit Greenspan
1 Introduction
175(2)
2 Methods
177(5)
3 Results
182(2)
4 Discussion and Future Work
184(3)
References
184(3)
Statistical Analysis of White Matter Integrity for the Clinical Study of Typical Specific Language Impairment in Children
187(12)
Emmanuel Vallee
Olivier Commowick
Camille Maumet
Aymeric Stamm
Elisabeth Le Rumeur
Catherine Allaire
Jean-Christophe Ferre
Clement de Guibert
Christian Barillot
1 Introduction
188(1)
2 Material and Methods
189(2)
2.1 Participants
189(1)
2.2 Data Acquisition
189(1)
2.3 Processing Pipeline
190(1)
3 Results
191(1)
3.1 ROI-Based Analysis
191(1)
3.2 Tractography-Based Analysis
192(1)
4 Discussion and Conclusion
192(7)
References
194(5)
Part V Brain Connectivity
Disrupted Brain Connectivity in Alzheimer's Disease: Effects of Network Thresholding
199(10)
Madelaine Daianu
Emily L. Dennis
Neda Jahanshad
Talia M. Nir
Arthur W. Toga
Clifford R. Jack, Jr.
Michael W. Weiner
Paul M. Thompson
1 Introduction
200(1)
2 Methods
201(3)
2.1 Subjects and Diffusion Imaging of the Brain
201(1)
2.2 Image Analysis
201(1)
2.3 Brain Network Measures
202(2)
3 Results
204(2)
4 Discussion
206(3)
References
207(2)
Rich Club Analysis of Structural Brain Connectivity at 7 Tesla Versus 3 Tesla
209(10)
Emily L. Dennis
Liang Zhan
Neda Jahanshad
Bryon A. Mueller
Yan Jin
Christophe Lenglet
Essa Yacoub
Guillermo Sapiro
Kamil Ugurbil
Noam Harel
Arthur W. Toga
Kelvin O. Lim
Paul M. Thompson
1 Introduction
210(1)
2 Methods
211(3)
2.1 Subject Demographic and Image Acquisition
211(1)
2.2 Image Preprocessing and Registration
211(1)
2.3 Brain Connectivity Computation
212(1)
2.4 Rich Club Analyses
212(2)
3 Results
214(2)
3.1 Rich Club Coefficient (φ(k) and φnorm(k))
214(1)
3.2 Rich Club Organization: Young Cohort Results
214(1)
3.3 Rich Club Organization: AD/HC Comparison
215(1)
4 Discussion
216(1)
5 Conclusion
217(2)
References
217(2)
Coupled Intrinsic Connectivity: A Principled Method for Exploratory Analysis of Paired Data
219(11)
Dustin Scheinost
Xilin Shen
Emily Finn
Rajita Sinha
R. Todd Constable
Xenophon Papademetris
1 Introduction
219(2)
2 Theory
221(2)
3 Functional Connectivity Estimation
223(1)
4 Results
224(2)
5 Discussion
226(4)
References
226(4)
Power Estimates for Voxel-Based Genetic Association Studies Using Diffusion Imaging
230(9)
Neda Jahanshad
Peter Kochunov
David C. Glahn
John Blangero
Thomas E. Nichols
Katie L. McMahon
Greig I. de Zubicaray
Nicholas G. Martin
Margaret J. Wright
Clifford R. Jack, Jr.
Matt A. Bernstein
Michael W. Weiner
Arthur W. Toga
Paul M. Thompson
1 Introduction
230(2)
2 Methods
232(3)
2.1 Heritability and Power Estimates
232(1)
2.2 HWE, MAF, and Multiple Comparisons Correction
233(1)
2.3 Accounting for Uncertainties in Genotype Frequency
234(1)
2.4 Voxelwise GWAS of the ADNI2 Dataset
235(1)
3 Results
235(1)
3.1 Voxels with Power > 0.8 as Functions of N, MAFc, HWEc
235(1)
3.2 Voxelwise GWAS in the ADNI2 Dataset
235(1)
4 Discussion
236(3)
References
237(2)
Global Changes in the Connectome in Autism Spectrum Disorders
239(14)
Caspar J. Goch
Basak Oztan
Bram Stieltjes
Romy Henze
Jan Hering
Luise Poustka
Hans-Peter Meinzer
Bulent Yener
Klaus H. Maier-Hein
1 Introduction
240(1)
2 Materials and Methods
240(2)
3 Results
242(4)
4 Discussion
246(7)
References
246(7)
Index 253