Computational diffusion MRI : International MICCAI Workshop, Lima, Peru, October 2020 / / edited by Noemi Gyori [and five others]

• Record Nr.: UNISA996466396303316
• Pubbl/distr/stampa: Cham, Switzerland : , : Springer, , [2021]; ©2021
• ISBN: 3-030-73018-2
• Descrizione fisica: 1 online resource (301 pages)
• Collana: Mathematics and Visualization
• Disciplina: 616.07548
• Soggetti: Optical data processing; Imatges per ressonància magnètica; Congressos; Llibres electrònics
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Intro -- Programme Committee -- Preface -- Contents -- Diffusion MRI Signal Acquisition -- Image Reconstruction from Accelerated Slice-Interleaved Diffusion Encoding Data -- 1 Introduction -- 2 Methods -- 2.1 SIDE Acquisition -- 2.2 Reconstruction -- 2.3 Optimization -- 3 Experiments -- 3.1 Materials -- 3.2 Results -- 4 Conclusion -- References -- Towards Learned Optimal q-Space Sampling in Diffusion MRI -- 1 Introduction -- 1.1 Main Contributions -- 2 Method -- 2.1 Forward Model: Sub-Sampling Layer -- 2.2 Reconstruction Model -- 2.3 Optimization -- 3 Experimental Evaluation -- 3.1 Dataset -- 3.2 Training Settings -- 3.3 Results and Discussion -- 4 Conclusion -- 5 Supplementary Materials -- References -- A Signal Peak Separation Indexpg for Axisymmetric B-Tensor Encoding -- 1 Introduction -- 2 Theory -- 2.1 A Toy Model of Fascicle Crossing Under B-Tensor Encoding -- 2.2 The Signal Peak Separation Index -- 3 Methods -- 4 Results -- 5 Discussion and Conclusion -- References -- Orientation Processing: Tractography and Visualization -- Improving Tractography Accuracy Using Dynamic Filtering -- 1 Introduction -- 2 Materials and Methods -- 2.1 Initial Set of Streamlines -- 2.2 Parametric Representation of the Streamlines -- 2.3 Optimization -- 2.4 Data and ; Experiments -- 3 Results and Discussion -- 4 Conclusions -- References -- Diffeomorphic Alignment of Along-Tract Diffusion Profiles from Tractography -- 1 Introduction -- 2 Alignment of Along-Tract Diffusion Measure Profiles -- 2.1 Representation -- 2.2 Objective Function for Joint Alignment -- 2.3 Alternating Minimization for Subject-Level and Tract-Level Alignment -- 3 Results -- 3.1 Data -- 3.2 Along-Tract FA Profiles Before and After Joint Alignment -- 3.3 Reduced Coefficient of Variation -- 3.4 Subject-Wise Inter-tract Correlations.; 3.5 Intraclass Correlation Coefficient for Reliability Across Time Points -- 4 Discussion -- References -- Direct Reconstruction of Crossing Muscle Fibers in the Human Tongue Using a Deep Neural Network -- 1 Introduction -- 2 Methods -- 2.1 Training Data and Ground Truth -- 2.2 Fiber Estimation Network -- 2.3 Fiber Estimation Loss -- 2.4 Training Procedure -- 3 Experiments and Results -- 3.1 Quantitative Evaluation on Synthetic Tongue HARDI Data -- 3.2 Qualitative Results on Post-mortem Human Tongue Data -- 4 Discussion and Conclusions -- References -- Learning Anatomical Segmentations for Tractography from Diffusion MRI -- 1 Introduction -- 2 Materials and Methods -- 2.1 Data -- 2.2 Data Representations -- 2.3 Architecture -- 2.4 Training -- 2.5 Tracts -- 2.6 Evaluation Criteria -- 3 Results and Discussion -- 3.1 Evaluation 1: Q-Space Sampling Density -- 3.2 Evaluation 2: Input Representations -- 3.3 Evaluation 3: Generalization -- 3.4 Evaluation 4: Tract Similarity -- 4 Conclusion -- References -- Diffusion MRI Fiber Orientation Distribution Function Estimation Using Voxel-Wise Spherical U-Net -- 1 Introduction -- 2 Background and Method -- 2.1 Voxel-Wise Spherical U-Net -- 3 Dataset -- 4 Experiments and Implementation Details -- 5 Results and Conclusions -- References -- Microstructure Modeling and Representation -- Stick Stippling for Joint 3D Visualization of Diffusion MRI Fiber Orientations and Density -- 1 Introduction -- 2 Methods -- 2.1 Diffusion Modeling and the Fixel Representation -- 2.2 Fixel Glyph Visualization -- 3 Experiments and Results -- 3.1 Clinical Data Experiment -- 3.2 HCP Experiment -- 3.3 RESOLVE Experiment -- 4 Discussion and Conclusions -- References -- Q-Space Quantitative Diffusion MRI Measures Using a Stretched-Exponential Representation -- 1 Introduction -- 2 Theory -- 2.1 Diffusion MR Signal Representation.; 2.2 Q-Space Domain Quantitative Measures -- 2.3 Numerical Implementation -- 2.4 Optimization of Stretched-Exponential Representation -- 3 Materials and Methods -- 3.1 Ex Vivo rat brain data -- 3.2 In Vivo Human brain data -- 3.3 Comparison to the Q-Space Measures from Different Methods -- 4 Results and Discussion -- 5 Conclusions -- References -- Repeatability of Soma and Neurite Metrics in Cortical and Subcortical Grey Matter -- 1 Introduction -- 2 Methods -- 2.1 Image Acquisition and Pre-processing -- 2.2 Image Processing and Analysis -- 3 Results -- 4 Discussion -- References -- DW-MRI Microstructure Model of Models Captured Via Single-Shell Bottleneck Deep Learning -- 1 Introduction -- 2 Related Work -- 3 Data Acquisition -- 4 Proposed Method -- 5 Results -- 6 Discussion -- References -- Deep Learning Model Fitting for Diffusion-Relaxometry: A Comparative Study -- 1 Introduction -- 2 Methods -- 2.1 qMRI Model Fitting with DNNs -- 2.2 In Silico Study -- 2.3 In Vivo study -- 3 Results -- 4 Discussion -- 5 Conclusion -- References -- Pretraining Improves Deep Learning Based Tissue Microstructure Estimation -- 1 Introduction -- 2 Methods -- 2.1 Problem Formulation -- 2.2 Signal Generation for Pretraining -- 2.3 Backbone Deep Network -- 2.4 Pretraining with the Auxiliary Dataset and Fine-Tuning -- 2.5 Implementation Details -- 3 Results -- 4 Discussion -- 5 Conclusion ; -- References -- Signal Augmentation and Super Resolution -- Enhancing Diffusion Signal Augmentation Using Spherical Convolutions -- 1 Introduction -- 2 Signal Augmentation -- 2.1 Deep Learning Models -- 2.2 Spherical Deep Learning Models -- 2.3 Material -- 3 Evaluation -- 3.1 Results -- 4 Discussion -- 5 Conclusion -- References -- Hybrid Graph Convolutional Neural Networks for Super Resolution of DW Images -- 1 Introduction -- 2 Dataset -- 3 Methods.; 3.1 Coarse SR Prediction in 3D Grid Structure Space -- 3.2 Refinement by GCNN in Diffusion Gradient Space -- 3.3 Loss Function -- 4 Experiments -- 5 Conclusion -- References -- Manifold-Aware CycleGAN for High-Resolution Structural-to-DTI Synthesis -- 1 Introduction -- 2 Method -- 2.1 Log-Euclidean Metric -- 2.2 Adversarial Loss -- 2.3 Cycle Consistency Loss -- 2.4 Manifold-Aware Wasserstein CycleGAN -- 3 Experiments -- 4 Discussion and Conclusion -- References -- Diffusion MRI Applications -- Beyond Lesion-Load: Tractometry-Based Metrics for Characterizing White Matter Lesions within Fibre Pathways -- 1 Introduction -- 2 Theory and Methods -- 2.1 Clinical Assessment -- 2.2 Acquisition -- 2.3 Processing -- 2.4 Proposed Metrics -- 3 Results -- 3.1 Lesion Mapping -- 3.2 Volumetric Metrics -- 3.3 Tractometry-Based Metrics -- 4 Discussion and Conclusion -- References -- Multi-modal Brain Age Estimation: A Comparative Study Confirms the Importance of Microstructure -- 1 Introduction -- 2 Data and Materials -- 3 Methods -- 3.1 Brain Age Estimation -- 3.2 Associations with IDPs and Non-IDP Variables -- 4 Results -- 4.1 Brain Age Estimation -- 4.2 Association with Brain IDPs -- 4.3 Association with Cardiac Variables -- 5 Discussion -- References -- Longitudinal Parcellation of the Infant Cortex Using Multi-modal Connectome Harmonics -- 1 Introduction -- 2 Methods -- 2.1 Data and Preprocessing -- 2.2 Connectivity Matrices -- 2.3 Iterative Multi-modal Parcellation Via Connectome Harmonics -- 2.4 Optimal Cluster Number Determination -- 3 Results -- 3.1 Homogeneity -- 3.2 Community Detection -- 4 Discussion -- 5 Conclusion -- References -- Automatic Segmentation of Dentate Nuclei for Microstructure Assessment: Example of Application to Temporal Lobe Epilepsy Patients -- 1 Introduction -- 2 Methods -- 2.1 Subjects -- 2.2 MRI Protocol -- 2.3 DWI Processing.; 2.4 DNs Segmentation -- 2.5 Post Processing for OPAL and CNN -- 2.6 Quantitative Evaluation -- 2.7 Comparison of Automatic Methods -- 2.8 Clinical Application to TLE Data -- 3 Results -- 3.1 Comparison of the Three Automatic Methods -- 3.2 Application to TLE Dataset -- 4 Discussion -- 5 Conclusion -- References -- Two Parallel Stages Deep Learning Network for Anterior Visual Pathway Segmentation -- 1 Introduction -- 2 Methods -- 2.1 Data Preprocessing -- 2.2 Two Parallel Stages Network Architecture -- 3 Experiments -- 3.1 Dataset -- 3.2 Implementation Details -- 3.3 Results -- 4 Conclusion -- References -- Exploring DTI Benchmark Databases Through Visual Analytics -- 1 Introduction -- 2 Related Work -- 3 Use Case -- 4 Implementation -- 5 Discussion -- 6 Conclusions and Future Work -- References -- Index.