Computational diffusion MRI : MICCAI Worskhop, Québec, Canada, September 2017 / Enrico Kaden, Francesco Grusso, Lipeng Ning, Chantal M.W. Tax, Jelle Veraart, editors.
This volume presents the latest developments in the highly active and rapidly growing field of diffusion MRI. The reader will find numerous contributions covering a broad range of topics, from the mathematical foundations of the diffusion process and signal generation, to new computational methods a...
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| Format: | Conference Proceeding eBook |
| Language: | English |
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Springer,
[2018]
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| Series: | Mathematics and visualization.
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Table of Contents:
- Intro; Preface; Program Committee; Contents; Part I Data Acquisition and Modeling; Estimating Tissue Microstructure Using Diffusion-Weighted Magnetic Resonance Spectroscopy of Brain Metabolites; 1 Introduction; 2 MRS and DW-MRS (Very) Briefly; 3 Geometrical Models for Tissue Microstructure Estimates Using the Diffusion of Brain Metabolites; 4 Computational Models for Cell Morphology Estimates Using the Diffusion of Brain Metabolites; 5 Conclusion; References; (k,q)-Compressed Sensing for dMRI with Joint Spatial-Angular Sparsity Prior; 1 Introduction; 2 Background and Prior Work; 3 Methods.
- Part II Image PostprocessingDiffusion Specific Segmentation: Skull Stripping with Diffusion MRI Data Alone; 1 Introduction; 1.1 Desired Properties of a dMRI Segmenter; 2 Methods; 2.1 Feature Extraction; 2.2 Classifier Selection; 2.3 Morphological Filtering; 2.4 Comparison with Other Intracranial Mask Generation Methods; 3 Data, Training, and Testing; 3.1 Data; 3.2 Training; 3.3 Testing; 4 Results; 5 Conclusions; References; Diffeomorphic Registration of Diffusion Mean Apparent Propagator Fields Using Dynamic Programming on a Minimum Spanning Tree; 1 Introduction; 2 Methods.
- 2.1 Discrete Representation of MAPs and Reorientation2.2 MAP Similarity Measure; 2.3 Diffeomorphic Registration of MAP Fields; 3 Results and Discussion; 4 Conclusion; References; Diffusion Orientation Histograms (DOH) for Diffusion Weighted Image Analysis; 1 Introduction; 2 Related Work; 3 Diffusion Orientation Histogram Descriptors; 4 Experiments; 5 Discussion; References; Part III Tractography and Connectivity; Learning a Single Step of Streamline Tractography Based on Neural Networks; 1 Introduction; 2 Methods; 2.1 Input to the Network; 2.2 Output of the Network and Post Processing.
- 3.1 (k,q)-CS for dMRI with Joint Spatial-Angular Sparsity3.2 Efficient Algorithm to Solve (k,q)-CS; 4 Experiments; 4.1 Spatial-Angular Transforms and (k,q) Subsampling Schemes; 4.2 (k,q)-CS Results for Phantom HARDI Data; 4.3 (k,q)-CS Results for Real HARDI Brain Data; 5 Conclusion; References; Spatio-Temporal dMRI Acquisition Design: Reducing the Number of qτ Samples Through a Relaxed Probabilistic Model; 1 Introduction; 2 Diffusion MRI Theory; 3 Methods; 3.1 Optimal Acquisition Design; 3.2 Relaxed Probabilistic Model; 3.3 qτ-Space Model with GraphNet Regularization; 4 Experiments; 4.1 Setup.
- 4.2 Objective Function and Performance Measures4.3 Diffusion Data; 4.4 Results; 5 Discussion; 5.1 Our Approach Largely Reduces Acquisition Time; 5.2 Spatio-Temporal Phenomena Are Preserved in Our Scheme; 5.3 The Acquisition Scheme That We Obtained Is Reasonable; 6 Conclusions; References; A Generalized SMT-Based Framework for Diffusion MRI Microstructural Model Estimation; 1 Introduction; 2 Methods; 2.1 Case of Study: The Two-Parameter Models; 2.2 Implementation Details; 2.3 Simulated Data; 3 Results; 4 Discussion and Conclusion; Appendix: Derivation of the Ψ Function; References.