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020 _a9783319024752
_9978-3-319-02475-2
024 7 _a10.1007/978-3-319-02475-2
_2doi
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072 7 _aCOM014000
_2bisacsh
072 7 _aMAT003000
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082 0 4 _a004
_223
100 1 _aSchultz, Thomas.
_eeditor.
245 1 0 _aComputational Diffusion MRI and Brain Connectivity
_h[electronic resource] :
_bMICCAI Workshops, Nagoya, Japan, September 22nd, 2013 /
_cedited by Thomas Schultz, Gemma Nedjati-Gilani, Archana Venkataraman, Lauren O'Donnell, Eleftheria Panagiotaki.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2014.
300 _aXIV, 255 p. 78 illus., 67 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aMathematics and Visualization,
_x1612-3786
505 0 _aPart I Acquisition of Diffusion MRI: Comparing Simultaneous Multi-slice Diffusion Acquisitions by Y.Rathi et al -- Effect of Data Acquisition and Analysis Method on Fiber Orientation Estimation in Diffusion MRI by B.Wilkins et al -- Model-based super-resolution of diffusion MRI by A.Tobisch et al -- A quantitative evaluation of errors induced by reduced field-of-view in diffusion tensor imaging by J.Hering et al -- Part II Diffusion MRI Modeling: The Diffusion Dictionary in the Human Brain is Short: Rotation Invariant Learning of Basis Functions by M.Reisert et al -- Diffusion Propagator Estimation Using Radial Basis Functions by Y.Rathi et al -- A Framework for ODF Inference by using Fiber Tract Adaptive MPG Selection by H.Hontani et al -- Non-Negative Spherical Deconvolution (NNSD) for Fiber Orientation Distribution Function Estimation by J.Cheng et al -- Part III Tractography: A Novel Riemannian Metric for Geodesic Tractography in DTI by A.Fuster et al -- Fiberfox: An extensible system for generating realistic white matter software phantoms by P.F.Neher et al -- Choosing a Tractography Algorithm: On the Effects of Measurement Noise by A.Reichenbach et al -- Uncertainty in Tractography via Tract Confidence Regions by C.J.Brown et al -- Estimating Uncertainty in White Matter Tractography Using Wild Non-Local Bootstrap by P -- T. Yap et al -- Part IV Group Studies and Statistical Analysis: Groupwise Deformable Registration of Fiber Track Sets using Track Orientation Distributions by D. Christiaens et al -- Groupwise registration for correcting subject motion and eddy current distortions in diffusion MRI using a PCA based dissimilarity metric by W. Huizinga et al -- Fiber Based Comparison of Whole Brain Tractographies with Application to Amyotrophic Lateral Sclerosis by G. Zimmerman-Moreno et al -- Statistical Analysis of White Matter Integrity for the Clinical Study of Typical Specific Language Impairment in Children by E.Vallée et al -- Part V Brain Connectivity: Disrupted Brain Connectivity in Alzheimer’s Disease: Effects of Network Thresholding: M. Daianu et al -- Rich Club Analysis of Structural Brain Connectivity at 7 Tesla versus 3 Tesla: E. Dennis et al -- Coupled Intrinsic Connectivity: A Principled Method for Exploratory Analysis of Paired Data: D. Scheinost et al -- Power Estimates for Voxel-Based Genetic Association Studies using Diffusion Imaging: N. Jahanshad et al -- Global changes in the connectome in autism spectrum diseases: C. Jonas Goch et al.
520 _aThis volume contains the proceedings from two closely related workshops: Computational Diffusion MRI (CDMRI’13) and Mathematical Methods from Brain Connectivity (MMBC’13), held under the auspices of the 16th International Conference on Medical Image Computing and Computer Assisted Intervention, which took place in Nagoya, Japan, September 2013. Inside, readers will find contributions ranging from mathematical foundations and novel methods for the validation of inferring large-scale connectivity from neuroimaging data to the statistical analysis of the data, accelerated methods for data acquisition, and the most recent developments on mathematical diffusion modeling. This volume offers a valuable starting point for anyone interested in learning computational diffusion MRI and mathematical methods for brain connectivity as well as offers new perspectives and insights on current research challenges for those currently in the field. It will be of interest to researchers and practitioners in computer science, MR physics, and applied mathematics.
650 0 _aMathematics.
650 0 _aComputer vision.
650 0 _aOptical pattern recognition.
650 0 _aComputer science.
650 0 _aVisualization.
650 0 _aStatistics.
650 1 4 _aMathematics.
650 2 4 _aComputational Science and Engineering.
650 2 4 _aImage Processing and Computer Vision.
650 2 4 _aVisualization.
650 2 4 _aPattern Recognition.
650 2 4 _aTheoretical, Mathematical and Computational Physics.
650 2 4 _aStatistics for Life Sciences, Medicine, Health Sciences.
700 1 _aNedjati-Gilani, Gemma.
_eeditor.
700 1 _aVenkataraman, Archana.
_eeditor.
700 1 _aO'Donnell, Lauren.
_eeditor.
700 1 _aPanagiotaki, Eleftheria.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319024745
830 0 _aMathematics and Visualization,
_x1612-3786
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-02475-2
912 _aZDB-2-SMA
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