LEADER 06639nam 22007455 450 001 9910254284003321 005 20200706195455.0 010 $a3-319-54130-7 024 7 $a10.1007/978-3-319-54130-3 035 $a(CKB)3710000001364448 035 $a(DE-He213)978-3-319-54130-3 035 $a(MiAaPQ)EBC4858111 035 $a(PPN)201472074 035 $a(EXLCZ)993710000001364448 100 $a20170512d2017 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aComputational Diffusion MRI $eMICCAI Workshop, Athens, Greece, October 2016 /$fedited by Andrea Fuster, Aurobrata Ghosh, Enrico Kaden, Yogesh Rathi, Marco Reisert 205 $a1st ed. 2017. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017. 215 $a1 online resource (XI, 212 p. 70 illus., 66 illus. in color.) 225 1 $aMathematics and Visualization,$x1612-3786 311 $a3-319-54129-3 320 $aIncludes bibliographical references and index. 327 $aThe MR Physics of Advanced Diffusion Imaging: Matt Hall -- Noise Floor Removal via Phase Correction of Complex Diffusion-Weighted Images: Influence on DTI and q-Space Metrics: M. Pizzolato et al -- Regularized Dictionary Learning with Robust Sparsity Fitting for Compressed Sensing Multishell HARDI: K. Gupta et al -- Denoising Diffusion-Weighted Images Using Grouped Iterative Hard Thresholding of Multi-Channel Framelets: Jian Zhang et al -- Diffusion MRI Signal Augmentation ? From Single Shell to Multi Shell with Deep Learning: S. Koppers et al -- Multi-Spherical Diffusion MRI: Exploring Diffusion Time Using Signal Sparsity: R.H.J. Fick et al -- Sensitivity of OGSE ActiveAx to Microstructural Dimensions on a Clinical Scanner: L.S. Kakkar et al -- Groupwise Structural Parcellation of the Cortex: A Sound Approach Based on Logistic Models: G. Gallardo et al -- Robust Construction of Diffusion MRI Atlases with Correction for Inter-Subject Fiber Dispersion: Z. Yang et al -- Parcellation of Human Amygdala Subfields Using Orientation Distribution Function and Spectral K-means Clustering: Q. Wen et al -- Sparse Representation for White Matter Fiber Compression and Calculation of Inter-Fiber Similarity: G. Zimmerman Moreno et al -- An Unsupervised Group Average Cortical Parcellation using Diffusion MRI to Probe Cytoarchitecture: T. Ganepola et al -- Using multiple Diffusion MRI Measures to Predict Alzheimer?s Disease with a TV-L1 Prior: J.E. Villalon-Reina et al -- Accurate Diagnosis of SWEDD vs. Parkinson Using Microstructural Changes of Cingulum Bundle: Track-Specific Analysis: F. Rahmani et al -- Colocalization of Functional Activity and Neurite Density within Cortical Areas: A. Teillac et al -- Comparison of Biomarkers in Transgenic Alzheimer Rats Using Multi-shell Diffusion MRI: R.H.J. Fick -- Working Memory Function in Recent-onset Schizophrenia Patients Associated with White Matter Microstructure: Connectometry Approach: M. Dolatshahi et al. 330 $aThis volume offers a valuable starting point for anyone interested in learning computational diffusion MRI and mathematical methods for brain connectivity, while also sharing new perspectives and insights on the latest research challenges for those currently working in the field. Over the last decade, interest in diffusion MRI has virtually exploded. The technique provides unique insights into the microstructure of living tissue and enables in-vivo connectivity mapping of the brain. Computational techniques are key to the continued success and development of diffusion MRI and to its widespread transfer into the clinic, while new processing methods are essential to addressing issues at each stage of the diffusion MRI pipeline: acquisition, reconstruction, modeling and model fitting, image processing, fiber tracking, connectivity mapping, visualization, group studies and inference. These papers from the 2016 MICCAI Workshop ?Computational Diffusion MRI? ? which was intended to provide a snapshot of the latest developments within the highly active and growing field of diffusion MR ? cover a wide range of topics, from fundamental theoretical work on mathematical modeling, to the development and evaluation of robust algorithms and applications in neuroscientific studies and clinical practice. The contributions include rigorous mathematical derivations, a wealth of rich, full-color visualizations, and biologically or clinically relevant results. As such, they will be of interest to researchers and practitioners in the fields of computer science, MR physics, and applied mathematics. . 410 0$aMathematics and Visualization,$x1612-3786 606 $aBiomathematics 606 $aMathematics 606 $aVisualization 606 $aComputer simulation 606 $aOptical data processing 606 $aStatistics  606 $aMathematical and Computational Biology$3https://scigraph.springernature.com/ontologies/product-market-codes/M31000 606 $aVisualization$3https://scigraph.springernature.com/ontologies/product-market-codes/M14034 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$3https://scigraph.springernature.com/ontologies/product-market-codes/S17030 615 0$aBiomathematics. 615 0$aMathematics. 615 0$aVisualization. 615 0$aComputer simulation. 615 0$aOptical data processing. 615 0$aStatistics . 615 14$aMathematical and Computational Biology. 615 24$aVisualization. 615 24$aSimulation and Modeling. 615 24$aImage Processing and Computer Vision. 615 24$aStatistics for Life Sciences, Medicine, Health Sciences. 676 $a510 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 $a9910254284003321 996 $aComputational diffusion MRI$91409948 997 $aUNINA