Computational Diffusion MRI

This volume gathers papers presented at the Workshop on Computational Diffusion MRI (CDMRI'18), which was held under the auspices of the International Conference on Medical Image Computing and Computer Assisted Intervention in Granada, Spain on September 20, 2018. It presents the latest developments in the highly active and rapidly growing field of diffusion MRI. The reader will find papers on a broad range of topics, from the mathematical foundations of the diffusion process and signal generation, to new computational methods and estimation techniques for the in-vivo recovery of microstructural and connectivity features, as well as harmonisation and frontline applications in research and clinical practice. The respective papers constitute invited works from high-profile researchers with a specific focus on three topics that are now gaining momentum within the diffusion MRI community: i) machine learning for diffusion MRI; ii) diffusion MRI outside the brain (e.g. in the placenta); and iii) diffusion MRI for multimodal imaging. The book shares new perspectives on the latest research challenges for those currently working in the field, but also offers a valuable starting point for anyone interested in learning computational techniques in diffusion MRI. It includes rigorous mathematical derivations, a wealth of full-colour visualisations, and clinically relevant results. As such, it will be of interest to researchers and practitioners in the fields of computer science, MRI physics and applied mathematics alike.

Contenu
Part I Diffusion MRI Signal Acquisition and Processing Strategies: Towards Optimal Sampling in Diffusion MRI: H. Knutsson.- Joint Image Reconstruction and Phase Corruption Maps Estimation in Multi-Shot Echo Planar Imaging: I. Rabanillo-Viloria et al.- Return-to_Axis Probability Calculation from Single-Shell Acquisitions: S. Aja-Fernández et al.- A Novel Spatial-Angular Domain Regularisation Approach for Restoration of Diffusion MRI: A. Mella et al.- Dmipy, a Diffusion Microstructure Imaging Toolbox in Python to Improve Research Reproducibility: A. Alimi et al.- Tissue Segmentation Using Sparse Non-Negative Matrix Factorization of Spherical Mean Diffusion MRI Data: P. Sun et al.- A Closed-Form Solution of Rotation Invariant Spherical Harmonic Features in Diffusion MRI: M. Zucchelli et al.- Orientation-Dispersed Apparent Axon Diameter via Multi-Stage Spherical Mean Optimization: M. Pizzolato et al.- .- Part II Machine Learning for Diffusion MRI: Crurent Applications and Future Promises of Machine Learning in Diffusion MRI: D. Ravi et al.- q-Space Learning with Synthesized Training Data: C. Ye et al.- Graph-Based Deep Learning for Prediction of Longitudinal Infant Diffusion MRI Data: J. Kim et al.- Supervised Classification of White Matter Fibers Based on Neighborhood Fiber Orientation Distributions Using an Ensemble of Neural Networks: D. Ugurlu et al.- .- Part III Diffusion MRI Signal Harmonisation: Challenges and Opportunities in dMRI Data Harmonization: A.H. Zhu et al.- Spherical Harmonic Residual Network for Diffusion Signal Harmonization: S. Koppers et al.- Longitudinal Harmonization for Improving Tractography in Baby Diffusion MRI: K. M. Huynh et al.- Inter-Scanner Harmonization of High Angular Resolution DW-MRI Using Null Space Deep Learning: V. Nath et al.- Effects of Diffusion MRI Model and Harmonization on the Consistency of Findings in an International Multi-Cohort HIV Neuroimaging Study: T.M. Nir et al.- Multi-Shell Diffusion MRI Harmonisation and enhancement Challenge (MUSHAC): Progress and Results: L. Ning et al.- Part IV Diffusion MRI Outside the Brain and Clinical Applications: Diffusion MRI Outside the Brain: R.G. Nunes et al.- A Framework for Calculating Time-Efficient Diffusion MRI Protocols for Anisotropic IVIM and an Application in the Placenta: P.J. Slator et al.- Spatial Characterisation of Fibre Response Functions for Spherical Deconvolution in Multiple Sclerosis: C. Tur et al.- Edge Weights and Network Properties in Multiple Sclerosis: E. Powell et al.- Part V Tractography and Connectivity Mapping: Measures of Tractography Convergence: D.C. Moyer et al.- Brain Connectivity Measures via Direct Sub-Finslerian Front Propagation on the 5D Sphere Bundle of Positions and Directions: J. Portegies et al.- Inference of an Extended Short Fiber Bundle Atlas Using Sulcus-Based Constraints for a Diffeomorphic Inter-Subject Alignment: N.L. Avila et al.- Resolving the Crossing/Kissing Fiber Ambiguity Using Functionally Informed COMMIT: M. Frigo et al.- Voxel-Wise Clustering of Tractography Data for Building Atlases of Local Fiber Geometry: I. Brusini et al.- Obtaining Representative Core Streamlines for White Matter Tractometry of the Human Brain: M. Chamberland et al.- Improving Graph-Based Tractography Plausibility Using Microstructure Information: M. Battocchio et al.- Deterministic Group Tractography with Local Uncertainty Quantification: A.N. Holm et al.- Index.