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Computational diffusion MRI = MICCAI...

Fuster, Andrea.

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Computational diffusion MRI = MICCAI Workshop, Munich, Germany, October 9th, 2015 /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Computational diffusion MRI/ edited by Andrea Fuster ... [et al.].
Reminder of title:	MICCAI Workshop, Munich, Germany, October 9th, 2015 /
other author:	Fuster, Andrea.
Published:	Cham :Springer International Publishing : : 2016.,
Description:	ix, 234 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Diffusion magnetic resonance imaging - Congresses. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-28588-7
ISBN:	9783319285887

Computational diffusion MRI = MICCAI Workshop, Munich, Germany, October 9th, 2015 /
Computational diffusion MRIMICCAI Workshop, Munich, Germany, October 9th, 2015 /[electronic resource] :edited by Andrea Fuster ... [et al.]. - Cham :Springer International Publishing :2016. - ix, 234 p. :ill. (some col.), digital ;24 cm. - Mathematics and visualization,1612-3786. - Mathematics and visualization..

These 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.

ISBN: 9783319285887

Standard No.: 10.1007/978-3-319-28588-7doiSubjects--Topical Terms:

2191802
Diffusion magnetic resonance imaging
--Congresses.

LC Class. No.: RC78.7.N83

Dewey Class. No.: 616.07548

Computational diffusion MRI = MICCAI Workshop, Munich, Germany, October 9th, 2015 /
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520 $a These 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.
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