Computational Diffusion MRI: MICCAI Workshop, Athens, Greece, October 2016 - Computational Diffusion MRI: MICCAI Workshop, Athens, Greece, October 2016 -

The MR Physics of Advanced Diffusion Imaging Introduction The Continuity Equation for Vector-Valued Quantities Continuity of Magnetisation: The Bloch-Torrey Equation The Bloch Terms as Sources and Sinks The Flux Terms: Transport Processes T- and T2-WeightedImaging Diffusion Tensor Imaging Velocity-Weighted Phase Contrast Stretched-Exponentials and Space-Fractional Super-Diffusion Diffusion Kurtosis Imaging Multiple Continua Models with Boundary Conditions Spherical Deconvolution Continuous Parametric Compartments Other Models Random Permeable Barriers Fractional Diffusion Discussion References Noise Floor Removal via Phase Correction of Complex Diffusion-Weighted Images: Influence on DTI and q-Space Metrics Introduction Methods Simulation and Diffusion Signal Reconstruction Experiments and Results Conclusion References Regularized Dictionary Learning with Robust Sparsity Fitting for Compressed Sensing Multishell HARDI Introduction and Related Work Methods Dictionary Learning for Multishell HARDI Compressed Sensing Multishell HARDI Results and Conclusion Conclusion References Denoising Diffusion-Weighted Images Using Grouped Iterative Hard Thresholding of Multi-Channel Framelets Introduction Approach Tight Framelets Problem Formulation Optimization Setting the Weights Experimental Results Datasets Results Conclusion References Diffusion MRI Signal Augmentation: From Single Shell to Multi Shell with Deep Learning Introduction Material Neural Network for Regression Spherical Harmonics Deep Neural Network Results Impact of Noise and Number of Gradients Prediction of Another Shell Discussion Conclusion References Multi-Spherical Diffusion MRI: Exploring Diffusion Time Using Signal Sparsity Introduction Theory The Four-Dimensional Ensemble Average Propagator Multi-Spherical Signal Representation Estimation of т -Dependent q-Space Indices Data Set Specification Experiments and Results Discussion and Conclusion References Sensitivity of OGSE ActiveAx to Microstructural Dimensions on a Clinical Scanner Introduction Methods Diffusion MR Model Phantom Experiments Sample Preparation Image Acquisition Data Analysis Data Processing ActiveAx Model Fitting Results Discussion References Groupwise Structural Parcellation of the Cortex: A Sound Approach Based on Logistic Models Introduction Methods Data and Preprocessing Cortical Connectivity Model and Tractography Single Subject and Groupwise Parcelling Methodologies Experiments and Results Reliability of the Clustering Algorithm for the Model Parcelling Subjects From the Human Connectome Project Functional and Anatomical Comparison Anatomical Comparison Functional Comparison Discussion and Conclusion References Robust Construction of Diffusion MRI Atlases with Correction for Inter-Subject Fiber Dispersion Introduction Approach Patch Features Patch Matching Mean Shift Experimental Results Synthetic Data Real Data Conclusion References Parcellation of Human Amygdala Subfields Using Orientation Distribution Function and Spectral K-means Clustering Introduction Material and Methods Data Acquisition Post-processing Amygdala Segmentation Amygdala Parcellation: K-mean Spectral Clustering Results Discussion and Conclusions References Sparse Representation for White Matter Fiber Compression and Calculation of Inter-Fiber Similarity Introduction Methods Inter-Fiber Similarity and Sparse Representation Performance Evaluation How Well Do the Sparse Representations Approximate the Original Fibers How Well is the Original Similarity Measure Approximated by CWDS Representation of Different Fiber-Sets with Common Dictionary Discussion and Conclusions References An Unsupervised Group Average Cortical Parcellation Using Diffusion MRI to Probe Cytoarchitecture Introduction Methods Data and Pre-Processing Surface Reconstruction Feature Space Classification Results Central Sulcus Broca’s Region Auditory Areas Occipital Areas Gyrification Conclusion References Using Multiple Diffusion MRI Measures to Predict Alzheimer’s Disease with a TV-L1 Prior Introduction Methods Data Acquisition and Preprocessing DMRI Reconstruction Models, Scalar Maps, and Spatial Normalization Regularized Logistic Regression Classification Results Discussion References Accurate Diagnosis of SWEDD vs. Parkinson Using Microstructural Changes of Cingulum Bundle: Track-Specific Analysis Introduction Procedure Participants Data Acquisition Diffusion MRI Data Processing, Fibers Tractography and Group Analysis Result Conclusion References Colocalization of Functional Activity and Neurite Density Within Cortical Areas Introduction Material and Methods Acquisition Individual NODDI Maps Pial and White Surfaces Individual Quantitative Maps of Neurite Density Co-localization ofNeurite Density and Functional Activity S0rensen-Dice Coefficient Results and Discussion Statistical Evaluation of Left-Right Neurite Density Asymmetries Investigation of the Neurite Density in the Sensorymotor Cortex Investigation of the Neurite Density in Language Areas Investigation of the Neurite Density in the Visual Cortex Conclusion and Perspectives References Comparison of Biomarkers in Transgenic Alzheimer Rats Using Multi-Shell Diffusion MRI Introduction Materials and Methods Processing of Transgenic Alzheimer Rat Data Sets DTI Metrics NODDI Metrics MAP-MRI Metrics Results Discussion Conclusion References Working Memory Function in Recent-Onset Schizophrenia Patients Associated with White Matter Microstructure: Connectometry Approach Introduction Method Participants Data Acquisition Diffusion MRI Data Processing, Group Connectometry Result Conclusion References