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You searched IISERK - Subject: Air Pollution Prévision.
Tag In 1 In 2 Data
001  vtls000031583
003  IISER-K
005  20180629132600.0
007  cr nn 008mamaa
008  180629s2014 gw | s |||| 0|eng d
020  \a 9783319111827 \9 978-3-319-11182-7
035  \a (DE-He213)978-3-319-11182-7
039 9\y 201806291326 \z Siladitya
08204\a 570.285 \2 23
24510\a Computational Diffusion MRI \h [electronic resource] : \b MICCAI Workshop, Boston, MA, USA, September 2014 / \c edited by Lauren O'Donnell, Gemma Nedjati-Gilani, Yogesh Rathi, Marco Reisert, Torben Schneider.
264 1\a Cham : \b Springer International Publishing : \b Imprint: Springer, \c 2014.
300  \a IX, 219 p. 63 illus., 51 illus. in color. \b online resource.
336  \a text \b txt \2 rdacontent
337  \a computer \b c \2 rdamedia
338  \a online resource \b cr \2 rdacarrier
347  \a text file \b PDF \2 rda
4901 \a Mathematics and Visualization, \x 1612-3786
5050 \a I. Network analysis -- II. Clinical applications -- III. Tractography -- IV. Q-space reconstruction -- V. Post-processing.
520  \a This book contains papers presented at the 2014 MICCAI Workshop on Computational Diffusion MRI, CDMRI’14. Detailing new computational methods applied to diffusion magnetic resonance imaging data, it offers readers a snapshot of the current state of the art and covers 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.   Inside, readers will find information on brain network analysis, mathematical modeling for clinical applications, tissue microstructure imaging, super-resolution methods, signal reconstruction, visualization, and more. Contributions include both careful mathematical derivations and a large number of rich full-color visualizations.   Computational techniques are key to the continued success and development of diffusion MRI and to its widespread transfer into the clinic. This volume will offer a valuable starting point for anyone interested in learning computational diffusion MRI. It also offers new perspectives and insights on current research challenges for those currently in the field. The book will be of interest to researchers and practitioners in computer science, MR physics, and applied mathematics.
650 0\a Mathematics.
650 0\a Image processing.
650 0\a Pattern Recognition.
650 0\a Computer mathematics.
650 0\a Biomathematics.
65014\a Mathematics.
65024\a Physiological, Cellular and Medical Topics.
65024\a Computational Mathematics and Numerical Analysis.
65024\a Image Processing and Computer Vision.
65024\a Pattern Recognition.
7001 \a O'Donnell, Lauren. \e editor.
7001 \a Nedjati-Gilani, Gemma. \e editor.
7001 \a Rathi, Yogesh. \e editor.
7001 \a Reisert, Marco. \e editor.
7001 \a Schneider, Torben. \e editor.
7102 \a SpringerLink (Online service)
7730 \t Springer eBooks
77608\i Printed edition: \z 9783319111810
830 0\a Mathematics and Visualization, \x 1612-3786
85640\u http://dx.doi.org/10.1007/978-3-319-11182-7
912  \a ZDB-2-SMA
950  \a Mathematics and Statistics (Springer-11649)

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