語系:
繁體中文
English
說明(常見問題)
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Computational Diffusion MRI = MICCAI...
~
Rathi, Yogesh.
Computational Diffusion MRI = MICCAI Workshop, Munich, Germany, October 9th, 2015 /
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Computational Diffusion MRI/ edited by Andrea Fuster, Aurobrata Ghosh, Enrico Kaden, Yogesh Rathi, Marco Reisert.
其他題名:
MICCAI Workshop, Munich, Germany, October 9th, 2015 /
其他作者:
Fuster, Andrea.
面頁冊數:
IX, 234 p. 83 illus., 63 illus. in color.online resource. :
Contained By:
Springer Nature eBook
標題:
Mathematics. -
電子資源:
https://doi.org/10.1007/978-3-319-28588-7
ISBN:
9783319285887
Computational Diffusion MRI = MICCAI Workshop, Munich, Germany, October 9th, 2015 /
Computational Diffusion MRI
MICCAI Workshop, Munich, Germany, October 9th, 2015 /[electronic resource] :edited by Andrea Fuster, Aurobrata Ghosh, Enrico Kaden, Yogesh Rathi, Marco Reisert. - 1st ed. 2016. - IX, 234 p. 83 illus., 63 illus. in color.online resource. - Mathematics and Visualization,1612-3786. - Mathematics and Visualization,.
An 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.
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:
527692
Mathematics.
LC Class. No.: QA76.9.I52
Dewey Class. No.: 004
Computational Diffusion MRI = MICCAI Workshop, Munich, Germany, October 9th, 2015 /
LDR
:05273nam a22004095i 4500
001
977007
003
DE-He213
005
20200630134600.0
007
cr nn 008mamaa
008
201211s2016 gw | s |||| 0|eng d
020
$a
9783319285887
$9
978-3-319-28588-7
024
7
$a
10.1007/978-3-319-28588-7
$2
doi
035
$a
978-3-319-28588-7
050
4
$a
QA76.9.I52
072
7
$a
PBV
$2
bicssc
072
7
$a
MAT013000
$2
bisacsh
072
7
$a
PBV
$2
thema
082
0 4
$a
004
$2
23
245
1 0
$a
Computational Diffusion MRI
$h
[electronic resource] :
$b
MICCAI Workshop, Munich, Germany, October 9th, 2015 /
$c
edited by Andrea Fuster, Aurobrata Ghosh, Enrico Kaden, Yogesh Rathi, Marco Reisert.
250
$a
1st ed. 2016.
264
1
$a
Cham :
$b
Springer International Publishing :
$b
Imprint: Springer,
$c
2016.
300
$a
IX, 234 p. 83 illus., 63 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
490
1
$a
Mathematics and Visualization,
$x
1612-3786
505
0
$a
An Efficient Finite Element Solution of the Generalised Bloch-Torrey Equation for Arbitrary Domains: L. Beltrachini et al -- Super-Resolution Reconstruction of Diffusion-Weighted Images using 4D Low-Rank and Total Variation: Feng Shi et al -- Holistic Image Reconstruction for Diffusion MRI: V. Golkov et al -- Alzheimer’s Disease Classification with Novel Microstructural Metrics from Diffusion-Weighted MRI: T. M. Nir et al -- Brain Tissue Micro-Structure Imaging from Diffusion MRI Using Least Squares Variable Separation: H. Farooq et al -- Multi-Tensor MAPMRI: How to Estimate Microstructural Information from Crossing Fibers: M. Zucchelli et al -- On the Use of Antipodal Optimal Dimensionality Sampling Scheme on the Sphere for Recovering Intra-Voxel Fibre Structure in Diffusion MRI: A.P. Bates et al -- Estimation of Fiber Orientations Using Neighborhood Information: C. Ye et al -- A framework for creating population specific multimodal brain atlas using clinical T1 and diffusion tensor images: V. Gupta et al -- Alignment of Tractograms as Linear Assignment Problem: N. Sharmin -- Accelerating Global Tractography Using Parallel Markov Chain Monte Carlo: H. Wu et al -- Adaptive Enhancement in Diffusion MRI Through Propagator Sharpening: T. Dela Haije et al -- Angular Resolution Enhancement of Diffusion MRI Data Using Inter-Image Information Transfer: Geng Chen et al -- Crossing versus Fanning: Model Comparison Using HCP Data: A. Ghosh et al -- White Matter Fiber Set Simplification by Redundancy Reduction with Minimum Anatomical Information Loss: G. Zimmerman Moreno et al -- A Temperature Phantom to Probe the Ensemble Average Propagator Asymmetry: an In-Silico Study: M. Pizzolato et al -- Registration Strategies for Whole-Body Diffusion-Weighted MRI Stitching: J. Ceranka et al -- HARDI Feature Selection, Registration and Atlas Building for A$\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.
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.
650
0
$a
Mathematics.
$3
527692
650
0
$a
Visualization.
$3
574210
650
0
$a
Bioinformatics.
$3
583857
650
0
$a
Computer mathematics.
$3
1199796
650
0
$a
Computer simulation.
$3
560190
650
0
$a
Optical data processing.
$3
639187
650
0
$a
Statistics .
$3
1253516
650
2 4
$a
Computational Biology/Bioinformatics.
$3
677363
650
2 4
$a
Computational Science and Engineering.
$3
670319
650
2 4
$a
Simulation and Modeling.
$3
669249
650
2 4
$a
Image Processing and Computer Vision.
$3
670819
650
2 4
$a
Statistics for Life Sciences, Medicine, Health Sciences.
$3
670172
700
1
$a
Fuster, Andrea.
$4
edt
$4
http://id.loc.gov/vocabulary/relators/edt
$3
1108186
700
1
$a
Ghosh, Aurobrata.
$e
editor.
$4
edt
$4
http://id.loc.gov/vocabulary/relators/edt
$3
1270859
700
1
$a
Kaden, Enrico.
$4
edt
$4
http://id.loc.gov/vocabulary/relators/edt
$3
1202725
700
1
$a
Rathi, Yogesh.
$e
editor.
$4
edt
$4
http://id.loc.gov/vocabulary/relators/edt
$3
1270860
700
1
$a
Reisert, Marco.
$e
editor.
$4
edt
$4
http://id.loc.gov/vocabulary/relators/edt
$3
1270861
710
2
$a
SpringerLink (Online service)
$3
593884
773
0
$t
Springer Nature eBook
776
0 8
$i
Printed edition:
$z
9783319285863
776
0 8
$i
Printed edition:
$z
9783319285870
776
0 8
$i
Printed edition:
$z
9783319803814
830
0
$a
Mathematics and Visualization,
$x
1612-3786
$3
1258559
856
4 0
$u
https://doi.org/10.1007/978-3-319-28588-7
912
$a
ZDB-2-SMA
912
$a
ZDB-2-SXMS
950
$a
Mathematics and Statistics (SpringerNature-11649)
950
$a
Mathematics and Statistics (R0) (SpringerNature-43713)
筆 0 讀者評論
多媒體
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼[密碼必須為2種組合(英文和數字)及長度為10碼以上]
登入