Computational Diffusion MRI, Enrico Kaden; Francesco Grussu; Lipeng Ning; Chant

Автор: Synge
Название: The Hypercircle in Mathematical Physics
ISBN: 1107666554 ISBN-13(EAN): 9781107666559
Издательство: Cambridge Academ
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Цена: 46470.00 T
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Описание: This 1957 book was written to help physicists and engineers solve partial differential equations subject to boundary conditions. The complexities of calculation are illuminated throughout by simple, intuitive geometrical pictures. This book will be of value to anyone with an interest in solutions to boundary value problems in mathematical physics.

Автор: Lauren O`Donnell; Gemma Nedjati-Gilani; Yogesh Rat
Название: Computational Diffusion MRI
ISBN: 3319363441 ISBN-13(EAN): 9783319363448
Издательство: Springer
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Цена: 93160.00 T
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I. Network Analysis: Vector weights and dual graphs: an emphasis on connections in brain network analysis: Peter Savadjiev, Carl-Fredrik Westin, and Yogesh Rathi.- Rich club network analysis shows distinct patterns of disruption in frontotemporal dementia and Alzheimer's disease: Madelaine Daianu, Neda Jahanshad, Julio E. Villalon-Reina, Mario F. Mendez, George Bartzokis, Elvira E. Jimenez, Aditi Joshi, Joseph Barsuglia and Paul M. Thompson.- Parcellation-Independent Multi-Scale Framework for Brain Network Analysis: Markus Schirmer et al.- II. Clinical Applications: Multiple stages classification of Alzheimer's disease based on structural brain networks using Generalized Low Rank Approximations (GLRAM): Zhan L, Nie Z, Ye J, Wang Y, Jin Y, Jahanshad N, Prasad G, de Zubicaray GI, McMahon KL, Martin NG, Wright MJ, Thompson PM.- The added value of diffusion tensor imaging for automated white matter hyperintensity segmentation: Hugo J. Kuijf, Chantal M. W. Tax, L. Karlijn Zaanen, Willem H. Bouvy, Jeroen de Bresser, Alexander Leemans, Max A. Viergever, Geert Jan Biessels, and Koen L. Vincken.- Algebraic connectivity of brain networks shows patterns of segregation leading to reduced network robustness in Alzheimer's disease: Madelaine Daianu, Neda Jahanshad, Talia M. Nir, Cassandra D. Leonardo, Clifford R. Jack, Jr., Michael W. Weiner, Matthew Bernstein and Paul M. Thompson.- Diffusion-Map: A Novel Visualizing Biomarker for Diffusion Tensor Imaging of Human Brain White Matter: Mohammad Hadi Aarabi and Hamidreza Saligheh Rad.- A Multi-Parametric Diffusion Magnetic Resonance Imaging Texture Feature Model for Prostate Cancer Analysis: Farzad Khalvati, Amen Modhafar, Andrew Cameron, Alexander Wong, Masoom A. Haider.- Predicting poststroke depression from brain connectivity: J. Mitra, K-K. Shen, S. Ghose, P. Bourgeat, J. Fripp, O. Salvado, B. Campbell, S. Palmer, L. Carey, S. Rose.- III. Tractography: Fiber Bundle Segmentation Using Spectral Embedding and Supervised Learning: Dorothйe Vercruysse, Daan Christiaens, Frederik Maes, Stefan Sunaert, and Paul Suetens.- Atlas-Guided Global Tractography: Imposing a Prior on the Local Track Orientation: Daan Christiaens, Marco Reisert, Thijs Dhollander, Frederik Maes, Stefan Sunaert, and Paul Suetens.- IV. Q-Space Reconstruction: Magnitude and complex based diffusion signal reconstruction: Marco Pizzolato, Aurobrata Ghosh, Timothй Boutelier, and Rachid Deriche.- Diffusion propagator estimation using Gaussians scattered in q-space: Lipeng Ning, Oleg Michailovich, Carl-Fredrik Westin, Yogesh Rathi.- An Analytical 3D Laplacian Regularized SHORE Basis and its Impact on EAP Reconstruction and Microstructure Recovery: Rutger Fick, Demian Wassermann, Gonzalo Sanguinetti, and Rachid Deriche.- V. Post Processing: Motion is Inevitable: The Impact of Motion Correction Schemes on HARDI Reconstructions: Shireen Elhabian, Yaniv Gur, Clement Vachet, Joseph Piven for IBIS∗, Martin Styner, Ilana Leppert, G. Bruce Pike and Guido Gerig.- Joint Super-Resolution Using Only One Anisotropic Low-Resolution Image per q-Space Coordinate: Vladimir Golkov, Tim Sprenger, Marion I. Menzel, Ek Tsoon Tan, Luca Marinelli, Christopher J. Hardy, Axel Haase, Daniel Cremers, and Jonathan I. Sperl.- Bilateral Filtering of Multiple Fiber Orientations in Diffusion MRI: Ryan P. Cabeen and David H. Laidlaw.- Dictionary Based Super-Resolution for Diffusion MRI: Burak Yoldemir, Mohammad Bajammal, Rafeef Abugharbieh.

Автор: Clive A J Fletcher
Название: Computational Techniques for Fluid Dynamics Vol..1
ISBN: 3540530584 ISBN-13(EAN): 9783540530589
Издательство: Springer
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Цена: 60550.00 T
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Описание: This well-known 2-volume textbook provides senior undergraduate and postgraduate engineers, scientists and applied mathematicians with the specific techniques, and the framework to develop skills in using the techniques in the various branches of computational fluid dynamics. A solutions manual to the exercises is in preparation.

Автор: Wazwaz Abdul-Majid
Название: First Course In Integral Equations, A (Second Edition)
ISBN: 9814675121 ISBN-13(EAN): 9789814675123
Издательство: World Scientific Publishing
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Цена: 42240.00 T
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Описание: This second edition integrates the newly developed methods with classical techniques to give both modern and powerful approaches for solving integral equations.

Автор: Andrea Fuster; Aurobrata Ghosh; Enrico Kaden; Yoge
Название: Computational Diffusion MRI
ISBN: 3319541293 ISBN-13(EAN): 9783319541297
Издательство: Springer
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Цена: 139750.00 T
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The 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.

Автор: Thomas Schultz; Gemma Nedjati-Gilani; Archana Venk
Название: Computational Diffusion MRI and Brain Connectivity
ISBN: 3319024744 ISBN-13(EAN): 9783319024745
Издательство: Springer
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Цена: 139750.00 T
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Part 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.Vallйe 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.

Автор: Lauren O`Donnell; Gemma Nedjati-Gilani; Yogesh Rat
Название: Computational Diffusion MRI
ISBN: 3319111817 ISBN-13(EAN): 9783319111810
Издательство: Springer
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Цена: 93160.00 T
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Описание:

I. Network Analysis: Vector weights and dual graphs: an emphasis on connections in brain network analysis: Peter Savadjiev, Carl-Fredrik Westin, and Yogesh Rathi.- Rich club network analysis shows distinct patterns of disruption in frontotemporal dementia and Alzheimer's disease: Madelaine Daianu, Neda Jahanshad, Julio E. Villalon-Reina, Mario F. Mendez, George Bartzokis, Elvira E. Jimenez, Aditi Joshi, Joseph Barsuglia and Paul M. Thompson.- Parcellation-Independent Multi-Scale Framework for Brain Network Analysis: Markus Schirmer et al.- II. Clinical Applications: Multiple stages classification of Alzheimer's disease based on structural brain networks using Generalized Low Rank Approximations (GLRAM): Zhan L, Nie Z, Ye J, Wang Y, Jin Y, Jahanshad N, Prasad G, de Zubicaray GI, McMahon KL, Martin NG, Wright MJ, Thompson PM.- The added value of diffusion tensor imaging for automated white matter hyperintensity segmentation: Hugo J. Kuijf, Chantal M. W. Tax, L. Karlijn Zaanen, Willem H. Bouvy, Jeroen de Bresser, Alexander Leemans, Max A. Viergever, Geert Jan Biessels, and Koen L. Vincken.- Algebraic connectivity of brain networks shows patterns of segregation leading to reduced network robustness in Alzheimer's disease: Madelaine Daianu, Neda Jahanshad, Talia M. Nir, Cassandra D. Leonardo, Clifford R. Jack, Jr., Michael W. Weiner, Matthew Bernstein and Paul M. Thompson.- Diffusion-Map: A Novel Visualizing Biomarker for Diffusion Tensor Imaging of Human Brain White Matter: Mohammad Hadi Aarabi and Hamidreza Saligheh Rad.- A Multi-Parametric Diffusion Magnetic Resonance Imaging Texture Feature Model for Prostate Cancer Analysis: Farzad Khalvati, Amen Modhafar, Andrew Cameron, Alexander Wong, Masoom A. Haider.- Predicting poststroke depression from brain connectivity: J. Mitra, K-K. Shen, S. Ghose, P. Bourgeat, J. Fripp, O. Salvado, B. Campbell, S. Palmer, L. Carey, S. Rose.- III. Tractography: Fiber Bundle Segmentation Using Spectral Embedding and Supervised Learning: Dorothйe Vercruysse, Daan Christiaens, Frederik Maes, Stefan Sunaert, and Paul Suetens.- Atlas-Guided Global Tractography: Imposing a Prior on the Local Track Orientation: Daan Christiaens, Marco Reisert, Thijs Dhollander, Frederik Maes, Stefan Sunaert, and Paul Suetens.- IV. Q-Space Reconstruction: Magnitude and complex based diffusion signal reconstruction: Marco Pizzolato, Aurobrata Ghosh, Timothй Boutelier, and Rachid Deriche.- Diffusion propagator estimation using Gaussians scattered in q-space: Lipeng Ning, Oleg Michailovich, Carl-Fredrik Westin, Yogesh Rathi.- An Analytical 3D Laplacian Regularized SHORE Basis and its Impact on EAP Reconstruction and Microstructure Recovery: Rutger Fick, Demian Wassermann, Gonzalo Sanguinetti, and Rachid Deriche.- V. Post Processing: Motion is Inevitable: The Impact of Motion Correction Schemes on HARDI Reconstructions: Shireen Elhabian, Yaniv Gur, Clement Vachet, Joseph Piven for IBIS∗, Martin Styner, Ilana Leppert, G. Bruce Pike and Guido Gerig.- Joint Super-Resolution Using Only One Anisotropic Low-Resolution Image per q-Space Coordinate: Vladimir Golkov, Tim Sprenger, Marion I. Menzel, Ek Tsoon Tan, Luca Marinelli, Christopher J. Hardy, Axel Haase, Daniel Cremers, and Jonathan I. Sperl.- Bilateral Filtering of Multiple Fiber Orientations in Diffusion MRI: Ryan P. Cabeen and David H. Laidlaw.- Dictionary Based Super-Resolution for Diffusion MRI: Burak Yoldemir, Mohammad Bajammal, Rafeef Abugharbieh.

Автор: Thomas Schultz; Gemma Nedjati-Gilani; Archana Venk
Название: Computational Diffusion MRI and Brain Connectivity
ISBN: 3319376845 ISBN-13(EAN): 9783319376844
Издательство: Springer
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Цена: 111790.00 T
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Описание:

Part 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.Vallйe 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.

Автор: Kaden
Название: Computational Diffusion MRI
ISBN: 3319738380 ISBN-13(EAN): 9783319738383
Издательство: Springer
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Цена: 102480.00 T
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Описание:

Название: Computational diffusion mri
ISBN: 3030058301 ISBN-13(EAN): 9783030058302
Издательство: Springer
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Цена: 93160.00 T
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Описание: This volume gathers papers presented at the Workshop on Computational Diffusion MRI (CDMRI`18), which was held under the auspices of the International Conference on Medical Image Computing and Computer Assisted Intervention in Granada, Spain on September 20, 2018.

Автор: Nahin Paul J.
Название: Digital Dice: Computational Solutions to Practical Probability Problems (New in Paperback)
ISBN: 0691158215 ISBN-13(EAN): 9780691158211
Издательство: Wiley
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Цена: 17940.00 T
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Описание: Some probability problems are so difficult that they stump the smartest mathematicians. But even the hardest of these problems can often be solved with a computer and a Monte Carlo simulation, in which a random-number generator simulates a physical process, such as a million rolls of a pair of dice. This is what Digital Dice is all about: how to ge

Автор: Wanpracha Chaovalitwongse; Panos Pardalos; Petros
Название: Computational Neuroscience
ISBN: 1461425999 ISBN-13(EAN): 9781461425991
Издательство: Springer
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Цена: 186340.00 T
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Описание: This volume includes contributions from numerous disciplines, bridging a vital gap between the mathematical sciences and neuroscience research. This book demonstrates how methods from data mining, signal processing, optimization and cutting-edge medical techniques can be used to tackle the most challenging modern neuroscience problems.