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Magnetic Resonance Brain Imaging [[electronic resource] ] : Modelling and Data Analysis Using R / / by Jörg Polzehl, Karsten Tabelow
| Magnetic Resonance Brain Imaging [[electronic resource] ] : Modelling and Data Analysis Using R / / by Jörg Polzehl, Karsten Tabelow |
| Autore | Polzehl Jörg |
| Edizione | [2nd ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (268 pages) |
| Disciplina | 616.8047548 |
| Altri autori (Persone) | TabelowKarsten |
| Collana | Use R! |
| Soggetto topico |
Biometry
Radiology Image processing - Digital techniques Computer vision Mathematical statistics - Data processing Signal processing Biostatistics Computer Imaging, Vision, Pattern Recognition and Graphics Statistics and Computing Signal, Speech and Image Processing |
| ISBN | 3-031-38949-2 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface to the Second Edition -- Preface to First Edition -- Contents -- Acronyms -- 1 Introduction -- 2 Magnetic Resonance Imaging in a Nutshell -- 2.1 The Principles of Magnetic Resonance Imaging -- 2.1.1 The Zeeman effect for Atomic Nuclei -- 2.1.2 Macroscopic Magnetization Vector -- 2.1.3 Spin Excitation and Relaxation -- 2.1.4 Spatial Localization and Pulse Sequences -- 2.1.5 MR Image Formation and Parallel Imaging -- 2.2 Special MR Imaging Modalities -- 2.2.1 Functional Magnetic Resonance Imaging (fMRI) -- 2.2.2 Diffusion Weighted Magnetic Resonance Imaging(dMRI) -- 2.2.3 Multi-parameter Mapping (MPM) -- 2.2.4 Inversion Recovery Magnetic Resonance Imaging (IR-MRI) -- 3 Medical Imaging Data Formats -- 3.1 DICOM Format -- 3.2 ANALYZE and NIfTI format -- 3.3 The BIDS Standard for Neuroimaging Data -- 4 Functional Magnetic Resonance Imaging -- 4.1 Prerequisites for Running the Code in This Chapter -- 4.2 Pre-processing fMRI Data -- 4.2.1 Example Data -- Functional MRI Data on Visual Object Recognition (ds000105) -- Multi-subject and Multi-modal Neuroimaging Dataset on Face Processing (ds000117) -- Multi-modal Longitudinal Study of a Single Subject (ds000031) -- 4.2.2 Slice Time Correction -- 4.2.3 Motion Correction -- 4.2.4 Registration -- 4.2.5 Normalization -- 4.2.6 Brain Mask -- 4.2.7 Brain Tissue Segmentation -- 4.2.8 Using Brain Atlas Information -- 4.2.9 Spatial Smoothing -- 4.3 The General Linear Model (GLM) for fMRI -- 4.3.1 Modeling the BOLD Signal -- 4.3.2 The Linear Model -- 4.3.3 Simulated fMRI Data -- 4.4 Signal Detection in Single-Subject Experiments -- 4.4.1 Voxelwise Signal Detection and the Multiple Comparison Problem -- 4.4.2 Bonferroni Correction -- 4.4.3 Random Field Theory -- 4.4.4 False Discovery Rate (FDR) -- 4.4.5 Cluster Thresholds -- 4.4.6 Permutation Tests -- 4.5 Adaptive Smoothing in fMRI.
4.5.1 Analyzing fMRI Experiments with Structural Adaptive Smoothing Procedures -- 4.5.2 Structural Adaptive Segmentation in fMRI -- 4.6 Other Approaches for fMRI Analysis Using R -- 4.6.1 Multivariate fMRI Analysis -- 4.6.2 Independent Component Analysis (ICA) -- 4.7 Functional Connectivity for Resting-State fMRI -- 5 Diffusion-Weighted Imaging -- 5.1 Prerequisites -- 5.2 Diffusion-Weighted MRI Data -- 5.2.1 The Diffusion Equation and MRI -- 5.2.2 Example Data -- 5.2.3 Data Pre-processing -- 5.2.4 Reading Pre-processed Data -- 5.2.5 Basic Data Properties -- 5.2.6 Definition of a Brain Mask -- 5.2.7 Characterization of Noise in Diffusion-Weighted MRI -- 5.3 Modeling Diffusion-Weighted MRI Data -- 5.3.1 The Apparent Diffusion Coefficient (ADC) -- 5.3.2 Diffusion Tensor Imaging (DTI) -- 5.3.3 Diffusion Kurtosis Imaging (DKI) -- 5.3.4 The Orientation Distribution Function -- 5.3.5 Tensor Mixture Models -- 5.4 Smoothing Diffusion-Weighted Data -- 5.4.1 Effects of Gaussian Filtering -- 5.4.2 Multi-shell Position-Orientation Adaptive Smoothing (msPOAS) -- 5.5 Fiber Tracking Methods -- 5.6 Structural Connectivity -- 6 Multiparameter Mapping -- 6.1 Prerequisites -- 6.2 Multiparameter Mapping -- 6.2.1 Signal Model in FLASH Sequences -- 6.2.2 Data from the Multiparameter Mapping (MPM) Protocol -- 6.2.3 Reparameterization of the Signal Model by ESTATICS -- 6.2.4 Correction for Instrumental B1-Bias -- 6.2.5 Correction for the Bias Induced by Low SNR -- 6.2.6 Structural Adaptive Smoothing of Relaxometry Data -- 7 Inversion Recovery Magnetic Resonance Imaging -- 7.1 Prerequisites -- 7.2 Tissue Porosity Estimation by Inversion Recovery MRI-based Experiments -- 7.3 Generating a Simulated Dataset -- 7.4 Estimation of Parameters from IR MRI Data in a Mixture Model -- A Smoothing Techniques for Imaging Problems -- A.1 Non-parametric Regression -- A.1.1 Kernel Smoothing. A.2 Adaptive Weigths Smoothing -- A.2.1 Local Constant Likelihood Models -- A.2.2 Patch-Wise Adaptive Weights Smoothing (PAWS) -- A.3 Special Settings in Neuroimaging Experiments -- A.3.1 Simultaneous Mean and Variance Estimation -- A.3.2 Vector Valued Data -- A.3.3 Diffusion Data -- A.3.4 Tensor-Valued Data -- A.3.5 Model-Driven Smoothing of Observed Images -- B Resources for Neuroimaging in R -- B.1 An Overview on Selected R Packages for Neuroimaging -- B.2 Open Neuroimaging Data Archives -- C Data, Software and Hardware Resources -- C.1 How to Get the Example Code -- C.2 Packages and Software to Install -- C.3 How to Acquire and Organize the Example Data -- C.3.1 Data from the `Kirby21' Reproducibility Study -- C.3.2 Data from OpenNeuro -- C.3.3 DICOM Example Data -- C.3.4 MPM Data Example -- C.3.5 Atlas Data -- C.4 How to Obtain Precomputed Results -- C.5 System Requirements -- References -- Index. |
| Record Nr. | UNINA-9910751383603321 |
Polzehl Jörg
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Magnetic Resonance Brain Imaging : Modeling and Data Analysis Using R / / by Jörg Polzehl, Karsten Tabelow
| Magnetic Resonance Brain Imaging : Modeling and Data Analysis Using R / / by Jörg Polzehl, Karsten Tabelow |
| Autore | Polzehl Jörg |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (XVIII, 231 p. 77 illus., 48 illus. in color.) |
| Disciplina | 519.5 |
| Collana | Use R! |
| Soggetto topico |
Statistics
Radiology Optical data processing Biostatistics Signal processing Image processing Speech processing systems R (Computer program language) Statistics for Life Sciences, Medicine, Health Sciences Imaging / Radiology Computer Imaging, Vision, Pattern Recognition and Graphics Statistics and Computing/Statistics Programs Signal, Image and Speech Processing |
| ISBN | 3-030-29184-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1 Introduction -- 2 Magnetic Resonance Imaging in a nutshell -- 3 Medical imaging data formats -- 4 Functional Magnetic Resonance Imaging -- 5 DiffusionWeighted Imaging -- 6 Multi Parameter Mapping -- Appendix -- References -- Index. |
| Record Nr. | UNINA-9910349319403321 |
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Polzehl Jörg
|
||
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||