Table of Contents |
pt. I | Acquisition of Diffusion MRI | |
| Comparing Simultaneous Multi-slice Diffusion Acquisitions / C.-F. Westin | 3 |
1. | Introduction | 4 |
2. | Our Contributions | 5 |
3. | Methods | 5 |
4. | Experiments | 6 |
5. | Conclusion | 10 |
| References | 10 |
| Effect of Data Acquisition and Analysis Method on Fiber Orientation Estimation in Diffusion MRI / Natasha Lepore | 13 |
1. | Introduction | 14 |
2. | Materials and Methods | 14 |
2.1. | Diffusion-Weighted Data Synthesis | 14 |
2.2. | Quantitative Metrics | 15 |
3. | Application: Comparison of Fiber Estimation of Several Diffusion Analysis Methods | 16 |
3.1. | Establishment of Ground-Truth | 16 |
3.2. | Data Synthesis | 17 |
3.3. | Data Analysis | 17 |
3.4. | Results | 19 |
4. | Discussion and Conclusion | 22 |
| References | 23 |
| Model-Based Super-Resolution of Diffusion MRI / Hui Zhang | 25 |
1. | Introduction | 26 |
2. | Model-Based Super-Resolution Reconstruction | 28 |
2.1. | Forward Model | 28 |
2.2. | Super-Resolution Reconstruction | 29 |
2.3. | SRR Optimization Procedure | 30 |
3. | Evaluation and Results | 31 |
3.1. | Evaluation | 31 |
3.2. | Results | 31 |
4. | Discussion | 33 |
| References | 33 |
| A Quantitative Evaluation of Errors Induced by Reduced Field-of-View in Diffusion Tensor Imaging / Klaus H. Maier-Hein | 35 |
1. | Introduction | 36 |
2. | Methods | 37 |
2.1. | MRI Acquisition | 37 |
2.2. | Eddy Current and Head Motion Correction Schemes | 38 |
2.3. | Registration Parameters | 38 |
2.4. | Performance Metrics | 39 |
3. | Results | 40 |
3.1. | Registration Parameters | 40 |
3.2. | Precision of Registration | 40 |
3.3. | Tensor Fit Quality | 40 |
3.4. | Deviation in Fractional Anisotropy | 42 |
4. | Discussion | 42 |
| References | 43 |
pt. II | Diffusion MRI Modeling | |
| The Diffusion Dictionary in the Human Brain Is Short: Rotation Invariant Learning of Basis Functions / Valerij G. Kiselev | 47 |
1. | Introduction | 47 |
2. | Method | 49 |
2.1. | Representation of Basis Functions | 50 |
2.2. | Implementation and Optimization | 50 |
3. | Experiments | 51 |
4. | Conclusion | 54 |
| References | 55 |
| Diffusion Propagator Estimation Using Radial Basis Functions / C.-F. Westin | 57 |
1. | Introduction | 58 |
2. | Our Contributions | 58 |
3. | Data Representation Using Radial Basis Functions (RBF) | 59 |
3.1. | Application to Diffusion MRI | 60 |
3.2. | Estimating the ADP with Radial Basis Functions | 60 |
3.3. | Computing the Orientation Distribution Function (ODF) | 62 |
3.4. | Estimation Procedure | 62 |
4. | Experiments | 63 |
4.1. | In-Vivo Results | 63 |
5. | Conclusion | 65 |
| References | 66 |
| A Framework for ODF Inference by Using Fiber Tract Adaptive MPG Selection / Yoshitaka Masutani | 67 |
1. | Introduction | 67 |
1.1. | Background | 67 |
1.2. | Problem Statement and Objective | 68 |
2. | Proposed Method | 70 |
2.1. | Interpolation with SRBF | 70 |
2.2. | Optimization | 70 |
2.3. | Preprocessing | 72 |
3. | Experimental Method | 72 |
3.1. | Simulation Experiments | 73 |
3.2. | Phantom Experiments | 74 |
3.3. | Clinical Image Experiments | 75 |
4. | Experimental Results | 75 |
4.1. | Simulation Experiments | 75 |
4.2. | Phantom Experiments | 77 |
4.3. | Clinical Image Experiments | 78 |
5. | Conclusion | 78 |
| References | 78 |
| Non-negative Spherical Deconvolution (NNSD) for Fiber Orientation Distribution Function Estimation / Pew-Thian Yap | 81 |
1. | Introduction | 82 |
2. | Background on SD Methods | 83 |
3. | Non-negative Spherical Deconvolution (NNSD) | 85 |
4. | Experiments | 87 |
4.1. | High-Resolution Data | 90 |
5. | Discussion and Conclusion | 91 |
| References | 92 |
pt. III | Tractography | |
| A Novel Riemannian Metric for Geodesic Tractography in DTI / Luc Florack | 97 |
1. | Introduction | 97 |
2. | Theory | 98 |
2.1. | Preliminaries | 98 |
2.2. | Riemannian Framework Revisited | 99 |
3. | Experiments | 100 |
3.1. | Method | 100 |
3.2. | Results | 101 |
4. | Conclusion and Discussion | 102 |
| References | 103 |
| Fiberfox: An Extensible System for Generating Realistic White Matter Software Phantoms / Klaus H. Maier-Hein | 105 |
1. | Introduction | 106 |
2. | Materials and Methods | 107 |
2.1. | Fiber Definition | 107 |
2.2. | Signal Generation | 107 |
2.3. | Artifact Simulation | 108 |
2.4. | Simulations and Experiments | 109 |
3. | Results | 110 |
4. | Discussion and Conclusion | 112 |
| References | 112 |
| Choosing a Tractography Algorithm: On the Effects of Measurement Noise / Gerik Scheuermann | 115 |
1. | Introduction | 115 |
2. | Material and Methods | 117 |
2.1. | Data Acquisition and Subjects | 117 |
2.2. | Creation of the Reference Dataset | 118 |
2.3. | Choice of Algorithms and Parameters | 119 |
2.4. | Evaluating Tractography Robustness | 120 |
3. | Results | 121 |
4. | Discussion | 124 |
5. | Conclusion | 127 |
| References | 127 |
| Uncertainty in Tractography via Tract Confidence Regions / Ghassan Hamarneh | 129 |
1. | Introduction | 129 |
2. | Method | 131 |
2.1. | Path Confidence Regions | 131 |
2.2. | Confidence Region Visualization | 132 |
3. | Results | 133 |
4. | Conclusions | 137 |
| References | 137 |
| Estimating Uncertainty in White Matter Tractography Using Wild Non-local Bootstrap / Dinggang Shen | 139 |
1. | Introduction | 140 |
2. | Approach | 141 |
2.1. | Non-local Estimation as Non-parametric Kernel Regression | 141 |
2.2. | Wild Non-local Bootstrap (W-NLB) | 142 |
2.3. | Kernel and Bandwidth | 143 |
3. | Experimental Results | 143 |
3.1. | In Silico | 143 |
3.2. | In Vivo | 145 |
4. | Conclusion | 146 |
| References | 147 |
pt. IV | Group Studies and Statistical Analysis | |
| Groupwise Deformable Registration of Fiber Track Sets Using Track Orientation Distributions / Paul Suetens | 151 |
1. | Introduction | 151 |
2. | Methods | 152 |
2.1. | Track Orientation Distribution | 152 |
2.2. | TOD Registration and Reorientation | 153 |
3. | Experiments and Results | 154 |
3.1. | Data, Processing and Fiber Tracking | 154 |
3.2. | Experiment 1: Synthetically Deformed Single Subject | 155 |
3.3. | Experiment 2: Multiple Subjects | 156 |
4. | Discussion | 158 |
5. | Conclusion and Future Work | 159 |
| References | 160 |
| Groupwise Registration for Correcting Subject Motion and Eddy Current Distortions in Diffusion MRI Using a PCA Based Dissimilarity Metric / S. Klein | 163 |
1. | Introduction | 164 |
2. | Method | 164 |
2.1. | Groupwise Registration Framework | 164 |
2.2. | Dissimilarity Metric | 165 |
2.3. | Metric Derivative | 166 |
2.4. | Transformation Model | 167 |
2.5. | Optimization | 168 |
2.6. | Groupwise Approaches Proposed by Others | 168 |
2.7. | Implementation | 168 |
3. | Experiments and Results | 169 |
3.1. | Synthetic Data | 169 |
3.2. | Real Diffusion Weighted Data | 171 |
4. | Conclusions | 172 |
| References | 173 |
| Fiber Based Comparison of Whole Brain Tractographies with Application to Amyotrophic Lateral Sclerosis / Hayit Greenspan | 175 |
1. | Introduction | 175 |
2. | Methods | 177 |
3. | Results | 182 |
4. | Discussion and Future Work | 184 |
| References | 184 |
| Statistical Analysis of White Matter Integrity for the Clinical Study of Typical Specific Language Impairment in Children / Christian Barillot | 187 |
1. | Introduction | 188 |
2. | Material and Methods | 189 |
2.1. | Participants | 189 |
2.2. | Data Acquisition | 189 |
2.3. | Processing Pipeline | 190 |
3. | Results | 191 |
3.1. | ROI-Based Analysis | 191 |
3.2. | Tractography-Based Analysis | 192 |
4. | Discussion and Conclusion | 192 |
| References | 194 |
pt. V | Brain Connectivity | |
| Disrupted Brain Connectivity in Alzheimer's Disease: Effects of Network Thresholding / Paul M. Thompson | 199 |
1. | Introduction | 200 |
2. | Methods | 201 |
2.1. | Subjects and Diffusion Imaging of the Brain | 201 |
2.2. | Image Analysis | 201 |
2.3. | Brain Network Measures | 202 |
3. | Results | 204 |
4. | Discussion | 206 |
| References | 207 |
| Rich Club Analysis of Structural Brain Connectivity at 7 Tesla Versus 3 Tesla / Paul M. Thompson | 209 |
1. | Introduction | 210 |
2. | Methods | 211 |
2.1. | Subject Demographic and Image Acquisition | 211 |
2.2. | Image Preprocessing and Registration | 211 |
2.3. | Brain Connectivity Computation | 212 |
2.4. | Rich Club Analyses | 212 |
3. | Results | 214 |
3.1. | Rich Club Coefficient (φ(k) and φnorm(k)) | 214 |
3.2. | Rich Club Organization: Young Cohort Results | 214 |
3.3. | Rich Club Organization: AD/HC Comparison | 215 |
4. | Discussion | 216 |
5. | Conclusion | 217 |
| References | 217 |
| Coupled Intrinsic Connectivity: A Principled Method for Exploratory Analysis of Paired Data / Xenophon Papademetris | 219 |
1. | Introduction | 219 |
2. | Theory | 221 |
3. | Functional Connectivity Estimation | 223 |
4. | Results | 224 |
5. | Discussion | 226 |
| References | 226 |
| Power Estimates for Voxel-Based Genetic Association Studies Using Diffusion Imaging / Paul M. Thompson | 230 |
1. | Introduction | 230 |
2. | Methods | 232 |
2.1. | Heritability and Power Estimates | 232 |
2.2. | HWE, MAF, and Multiple Comparisons Correction | 233 |
2.3. | Accounting for Uncertainties in Genotype Frequency | 234 |
2.4. | Voxelwise GWAS of the ADNI2 Dataset | 235 |
3. | Results | 235 |
3.1. | Voxels with Power > 0.8 as Functions of N, MAFc, HWEc | 235 |
3.2. | Voxelwise GWAS in the ADNI2 Dataset | 235 |
4. | Discussion | 236 |
| References | 237 |
| Global Changes in the Connectome in Autism Spectrum Disorders / Klaus H. Maier-Hein | 239 |
1. | Introduction | 240 |
2. | Materials and Methods | 240 |
3. | Results | 242 |
4. | Discussion | 246 |
| References | 246 |
| Index | 253 |