Автор: Maosong Sun, Xiaojie Wang, Baobao Chang Название: Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data ISBN: 3319690043 ISBN-13(EAN): 9783319690049 Издательство: Springer Рейтинг: Цена: 5300.00 р. Наличие на складе: Есть (3 шт.) Описание: This book constitutes the proceedings of the 16th China National Conference on Computational Linguistics, CCL 2017, and the 5th International Symposium on Natural Language Processing Based on Naturally Annotated Big Data, NLP-NABD 2017, held in Nanjing, China, in October 2017. Minority language information processing.
Автор: Ashutosh Kumar Singh, Rajiv Singh Название: Computational Methodologies for Electrical and Electronics Engineers ISBN: 1799833283 ISBN-13(EAN): 9781799833284 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 29779.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Artificial intelligence has been applied to many areas of science and technology, including the power and energy sector. Renewable energy in particular has experienced the tremendous positive impact of these developments. With the recent evolution of smart energy technologies, engineers and scientists working in this sector need an exhaustive source of current knowledge to effectively cater to the energy needs of citizens of developing countries.
Computational Methodologies for Electrical and Electronics Engineers is a collection of innovative research that provides a complete insight and overview of the application of intelligent computational techniques in power and energy. Featuring research on a wide range of topics such as artificial neural networks, smart grids, and soft computing, this book is ideally designed for programmers, engineers, technicians, ecologists, entrepreneurs, researchers, academicians, and students.
Автор: Andrea Fuster; Aurobrata Ghosh; Enrico Kaden; Yoge Название: Computational Diffusion MRI ISBN: 3319541293 ISBN-13(EAN): 9783319541297 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
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: Lauren O`Donnell; Gemma Nedjati-Gilani; Yogesh Rat Название: Computational Diffusion MRI ISBN: 3319111817 ISBN-13(EAN): 9783319111810 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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: Lauren O`Donnell; Gemma Nedjati-Gilani; Yogesh Rat Название: Computational Diffusion MRI ISBN: 3319363441 ISBN-13(EAN): 9783319363448 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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: Kaden Название: Computational Diffusion MRI ISBN: 3319738380 ISBN-13(EAN): 9783319738383 Издательство: Springer Рейтинг: Цена: 15372.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
This volume presents the latest developments in the highly active and rapidly growing field of diffusion MRI. The reader will find numerous contributions covering a broad range of topics, from the mathematical foundations of the diffusion process and signal generation, to new computational methods and estimation techniques for the in-vivo recovery of microstructural and connectivity features, as well as frontline applications in neuroscience research and clinical practice.
These proceedings contain the papers presented at the 2017 MICCAI Workshop on Computational Diffusion MRI (CDMRI’17) held in Qu?bec, Canada on September 10, 2017, sharing new perspectives on the most recent research challenges for those currently working in the field, but also offering a valuable starting point for anyone interested in learning computational techniques in diffusion MRI. This book includes rigorous mathematical derivations, a large number of rich, full-colour visualisations and clinically relevant results. As such, it will be of interest to researchers and practitioners in the fields of computer science, MRI physics and applied mathematics.
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.
Описание: People use science every single day in their jobs! From a smoothie maker creating a vortex to blend fruit and vegetables to firefighters extinguishing a blaze by breaking the fire triangle, the jobs and occupations at the heart of this super-creative non-fiction read will inspire all children to seek out the everyday science in the world around us.
Описание: Multi-atlas segmentation of the aorta from 4D flow MRI: comparison of several fusion strategie.- Quality-aware Cine Cardiac MRI Reconstruction and Analysis from Undersampled k-space Data.- Coronary Artery Centerline Refinement using GCN Trained with Synthetic Data.- Novel imaging biomarkers to evaluate heart dysfunction post-chemotherapy: a preclinical MRI feasibility study.- A bi-atrial statistical shape model as a basis to classify left atrial enlargement from simulated and clinical 12-lead ECGs.- Vessel Extraction and Analysis of Aortic Dissection.- The Impact of Domain Shift on Left and Right Ventricle Segmentation in Short Axis Cardiac MR Images.- Characterizing myocardial ischemia and reperfusion patterns with hierarchical manifold learning.- Generating Subpopulation-Specific Biventricular Anatomy Models Using Conditional Point Cloud Variational Autoencoders.- Improved AI-based Segmentation of Apical and Basal Slices from Clinical Cine CMR.- Mesh Convolutional Neural Networks for Wall Shear Stress Estimation in 3D Artery Models.- Hierarchical multi-modality prediction model to assess obesity-related remodelling.- Neural Angular Plaque Characterization: Automated Quantification of Polar Distributionfor Plaque Composition.- Simultaneous Segmentation and Motion Estimation of Left Ventricular Myocardium in 3D Echocardiography using Multi-task Learning.- Statistical shape analysis of the tricuspid valve in hypoplastic left heart syndrome.- An Unsupervised 3D Recurrent Neural Networkfor Slice Misalignment Correction in CardiacMR Imaging.- Unsupervised Multi-Modality RegistrationNetwork based on Spatially Encoded Gradient Information.- In-silico analysis of device-related thrombosis for different left atrial appendage occluder settings.- Valve flattening with functional biomarkers for the assessment of mitral valve repair.- Multi-modality cardiac segmentation via mixing domains for unsupervised adaptation.- Uncertainty-Aware Training for Cardiac Resynchronisation Therapy Response Prediction.- Cross-domain Artefact Correction of Cardiac MRI.- Detection and Classification of Coronary Artery Plaques in Coronary Computed Tomography Angiography Using 3D CNN.- Predicting 3D Cardiac Deformations With Point Cloud Autoencoders.- Influence of morphometric and mechanical factors in thoracic aorta finite element modeling.- Right Ventricle Segmentation via Registration and Multi-input Modalities in Cardiac Magnetic Resonance Imaging from Multi-Disease, Multi-View and Multi-Center.- Using MRI-specific Data Augmentation to Enhance the Segmentation of Right Ventricle in Multi-disease, Multi-center and Multi-view Cardiac MRI.- Right Ventricular Segmentation from Short- and Long-Axis MRIs via Information Transition.- Tempera: Spatial Transformer Feature Pyramid Network for Cardiac MRI Segmentation.- Multi-view SA-LA Net: A framework for simultaneous segmentation of RV on multi-view cardiac MR Images.- Right ventricular segmentation in multi-view cardiac MRI using a unified U-net model.- Deformable Bayesian Convolutional Networks for Disease-Robust Cardiac MRI Segmentation.- Consistency based Co-Segmentation for Multi-View Cardiac MRI using Vision Transformer.- Refined Deep Layer Aggregation for Multi-Disease, Multi-View & Multi-Center Cardiac MR Segmentation.- A Multi-View Cross-Over Attention U-Net Cascade With Fourier Domain Adaptation For Multi-Domain Cardiac MRI Segmentation.- Multi-Disease, Multi-View & Multi-Center Right Ventricular Segmentation in Cardiac MRI using Efficient Late-Ensemble Deep Learning Approach.- Automated Segmentation of the Right Ventricle from Magnetic Resonance Imaging Using Deep Convolutional Neural Networks.- 3D right ventricle reconstruction from 2D U-Net segmentation of sparse short-axis and 4-chamber cardiac cine MRI views.- Late Fusion U-Net with GAN-based Augmentation for Generalizable Cardiac MRI Segmentation.- Using Out-of-Distribution Detection for Model Refinement in Cardiac Im
Автор: Thomas Schultz; Gemma Nedjati-Gilani; Archana Venk Название: Computational Diffusion MRI and Brain Connectivity ISBN: 3319024744 ISBN-13(EAN): 9783319024745 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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: Thomas Schultz; Gemma Nedjati-Gilani; Archana Venk Название: Computational Diffusion MRI and Brain Connectivity ISBN: 3319376845 ISBN-13(EAN): 9783319376844 Издательство: Springer Рейтинг: Цена: 16769.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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: Enrico Kaden; Francesco Grussu; Lipeng Ning; Chant Название: Computational Diffusion MRI ISBN: 3030088669 ISBN-13(EAN): 9783030088668 Издательство: Springer Рейтинг: Цена: 15372.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
This volume presents the latest developments in the highly active and rapidly growing field of diffusion MRI. The reader will find numerous contributions covering a broad range of topics, from the mathematical foundations of the diffusion process and signal generation, to new computational methods and estimation techniques for the in-vivo recovery of microstructural and connectivity features, as well as frontline applications in neuroscience research and clinical practice.
These proceedings contain the papers presented at the 2017 MICCAI Workshop on Computational Diffusion MRI (CDMRI’17) held in Qu?bec, Canada on September 10, 2017, sharing new perspectives on the most recent research challenges for those currently working in the field, but also offering a valuable starting point for anyone interested in learning computational techniques in diffusion MRI. This book includes rigorous mathematical derivations, a large number of rich, full-colour visualisations and clinically relevant results. As such, it will be of interest to researchers and practitioners in the fields of computer science, MRI physics and applied mathematics.
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