LEADER 07308nam 22008775 450 001 9910254075803321 005 20200630134600.0 010 $a3-319-28588-2 024 7 $a10.1007/978-3-319-28588-7 035 $a(CKB)3710000000636361 035 $a(EBL)4501072 035 $a(SSID)ssj0001665928 035 $a(PQKBManifestationID)16455272 035 $a(PQKBTitleCode)TC0001665928 035 $a(PQKBWorkID)15000298 035 $a(PQKB)10237753 035 $a(DE-He213)978-3-319-28588-7 035 $a(MiAaPQ)EBC4501072 035 $a(PPN)193444542 035 $a(EXLCZ)993710000000636361 100 $a20160408d2016 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aComputational Diffusion MRI$b[electronic resource] $eMICCAI Workshop, Munich, Germany, October 9th, 2015 /$fedited by Andrea Fuster, Aurobrata Ghosh, Enrico Kaden, Yogesh Rathi, Marco Reisert 205 $a1st ed. 2016. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2016. 215 $a1 online resource (236 p.) 225 1 $aMathematics and Visualization,$x1612-3786 300 $a"With 73 Figures, 68 in color." 311 $a3-319-28586-6 320 $aIncludes bibliographical references and index. 327 $aAn Efficient Finite Element Solution of the Generalised Bloch-Torrey Equation for Arbitrary Domains: L. Beltrachini et al -- Super-Resolution Reconstruction of Diffusion-Weighted Images using 4D Low-Rank and Total Variation: Feng Shi et al -- Holistic Image Reconstruction for Diffusion MRI: V. Golkov et al -- Alzheimer?s Disease Classification with Novel Microstructural Metrics from Diffusion-Weighted MRI: T. M. Nir et al -- Brain Tissue Micro-Structure Imaging from Diffusion MRI Using Least Squares Variable Separation: H. Farooq et al -- Multi-Tensor MAPMRI: How to Estimate Microstructural Information from Crossing Fibers: M. Zucchelli et al -- On the Use of Antipodal Optimal Dimensionality Sampling Scheme on the Sphere for Recovering Intra-Voxel Fibre Structure in Diffusion MRI: A.P. Bates et al -- Estimation of Fiber Orientations Using Neighborhood Information: C. Ye et al -- A framework for creating population specific multimodal brain atlas using clinical T1 and diffusion tensor images: V. Gupta et al -- Alignment of Tractograms as Linear Assignment Problem: N. Sharmin -- Accelerating Global Tractography Using Parallel Markov Chain Monte Carlo: H. Wu et al -- Adaptive Enhancement in Diffusion MRI Through Propagator Sharpening: T. Dela Haije et al -- Angular Resolution Enhancement of Diffusion MRI Data Using Inter-Image Information Transfer: Geng Chen et al -- Crossing versus Fanning: Model Comparison Using HCP Data: A. Ghosh et al -- White Matter Fiber Set Simplification by Redundancy Reduction with Minimum Anatomical Information Loss: G. Zimmerman Moreno et al -- A Temperature Phantom to Probe the Ensemble Average Propagator Asymmetry: an In-Silico Study: M. Pizzolato et al -- Registration Strategies for Whole-Body Diffusion-Weighted MRI Stitching: J. Ceranka et al -- HARDI Feature Selection, Registration and Atlas Building for A$\beta$ Pathology Classification: E. Schwab et al -- Reliability of Structural Connectivity Examined with Four Different Diffusion Reconstruction Methods at Two Different Spatial and Angular Resolutions: J. E. Villalon-Reina et al. 330 $aThese Proceedings of the 2015 MICCAI Workshop ?Computational Diffusion MRI? offer a snapshot of the current state of the art on a broad range of topics within the highly active and growing field of diffusion MRI. The topics vary from fundamental theoretical work on mathematical modeling, to the development and evaluation of robust algorithms, new computational methods applied to diffusion magnetic resonance imaging data, and applications in neuroscientific studies and clinical practice. Over the last decade interest in diffusion MRI has exploded. The technique provides unique insights into the microstructure of living tissue and enables in-vivo connectivity mapping of the brain. Computational techniques are key to the continued success and development of diffusion MRI and to its widespread transfer into clinical practice. New processing methods are essential for addressing issues at each stage of the diffusion MRI pipeline: acquisition, reconstruction, modeling and model fitting, image processing, fiber tracking, connectivity mapping, visualization, group studies and inference. This volume, which includes both careful mathematical derivations and a wealth of rich, full-color visualizations and biologically or clinically relevant results, offers a valuable starting point for anyone interested in learning about computational diffusion MRI and mathematical methods for mapping brain connectivity, as well as new perspectives and insights on current research challenges for those currently working in the field. It will be of interest to researchers and practitioners in the fields of computer science, MR physics, and applied mathematics. 410 0$aMathematics and Visualization,$x1612-3786 606 $aMathematics 606 $aVisualization 606 $aBioinformatics 606 $aComputer mathematics 606 $aComputer simulation 606 $aOptical data processing 606 $aStatistics  606 $aVisualization$3https://scigraph.springernature.com/ontologies/product-market-codes/M14034 606 $aComputational Biology/Bioinformatics$3https://scigraph.springernature.com/ontologies/product-market-codes/I23050 606 $aComputational Science and Engineering$3https://scigraph.springernature.com/ontologies/product-market-codes/M14026 606 $aSimulation and Modeling$3https://scigraph.springernature.com/ontologies/product-market-codes/I19000 606 $aImage Processing and Computer Vision$3https://scigraph.springernature.com/ontologies/product-market-codes/I22021 606 $aStatistics for Life Sciences, Medicine, Health Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/S17030 615 0$aMathematics. 615 0$aVisualization. 615 0$aBioinformatics. 615 0$aComputer mathematics. 615 0$aComputer simulation. 615 0$aOptical data processing. 615 0$aStatistics . 615 14$aVisualization. 615 24$aComputational Biology/Bioinformatics. 615 24$aComputational Science and Engineering. 615 24$aSimulation and Modeling. 615 24$aImage Processing and Computer Vision. 615 24$aStatistics for Life Sciences, Medicine, Health Sciences. 676 $a616.07548 702 $aFuster$b Andrea$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aGhosh$b Aurobrata$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aKaden$b Enrico$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aRathi$b Yogesh$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aReisert$b Marco$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910254075803321 996 $aComputational diffusion MRI$91409948 997 $aUNINA