Statistical Analysis of Noise in MRI [electronic resource] : Modeling, Filtering and Estimation /
Aja-Fernández, Santiago.author.
Vegas-Sánchez-Ferrero, Gonzalo.author.
This unique text/reference presents a comprehensive review of methods for modeling signal and noise in magnetic resonance imaging (MRI), providing a systematic study, classifying and comparing the numerous and varied estimation and filtering techniques drawn from more than ten years of research in this area. Topics and features: Provides a complete framework for the modeling and analysis of noise in MRI, considering different modalities and acquisition techniques Describes noise and signal estimation for MRI from a statistical signal processing perspective Surveys the different methods to remove noise in MRI acquisitions, under different approaches and from a practical point of view Reviews different techniques for estimating noise from MRI data in single- and multiple-coil systems for fully sampled acquisitions Examines the issue of noise estimation when accelerated acquisitions are considered, and parallel imaging methods are used to reconstruct the signal Includes appendices covering probability density functions, combinations of random variables used to derive estimators, and useful MRI datasets This practically-focused work serves as a reference manual for researchers dealing with signal processing in MRI acquisitions, and is also suitable as a textbook for postgraduate students in engineering with an interest in medical image processing. Dr. Santiago Aja-Fernández is an Associate Professor at the School of Telecommunications of the University of Valladolid, Spain. His other publications include the Springer title Tensors in Image Processing and Computer Vision. Dr. Gonzalo Vegas-Sánchez-Ferrero is a Research Fellow at Brigham and Women’s Hospital, and in the Applied Chest Imaging Laboratory of Harvard Medical School, Boston, MA, USA.
Includes bibliographical references and index.
The Problem of Noise in MRI -- Part I: Noise Models and the Noise Analysis Problem -- Acquisition and Reconstruction of Magnetic Resonance Imaging -- Statistical Noise Models for MRI -- Noise Analysis in MRI: Overview -- Noise Filtering in MRI -- Part II: Noise Analysis in Non-Accelerated Acquisitions -- Noise Estimation in the Complex Domain -- Noise Estimation in Single-Coil MR Data -- Noise Estimation in Multiple-Coil MR Data -- Parametric Noise Analysis from Correlated Multiple-Coil MR Data -- Part III: Noise Estimators in pMRI -- Parametric Noise Analysis in Parallel MRI -- Blind Estimation of Non-Stationary Noise in MRI -- Appendix A: Probability Distributions and Combination of Random Variables -- Appendix B: Variance Stabilizing Transformation -- Appendix C: Data Sets Used in the Experiments.
This unique text/reference presents a comprehensive review of methods for modeling signal and noise in magnetic resonance imaging (MRI), providing a systematic study, classifying and comparing the numerous and varied estimation and filtering techniques drawn from more than ten years of research in this area. Topics and features: Provides a complete framework for the modeling and analysis of noise in MRI, considering different modalities and acquisition techniques Describes noise and signal estimation for MRI from a statistical signal processing perspective Surveys the different methods to remove noise in MRI acquisitions, under different approaches and from a practical point of view Reviews different techniques for estimating noise from MRI data in single- and multiple-coil systems for fully sampled acquisitions Examines the issue of noise estimation when accelerated acquisitions are considered, and parallel imaging methods are used to reconstruct the signal Includes appendices covering probability density functions, combinations of random variables used to derive estimators, and useful MRI datasets This practically-focused work serves as a reference manual for researchers dealing with signal processing in MRI acquisitions, and is also suitable as a textbook for postgraduate students in engineering with an interest in medical image processing. Dr. Santiago Aja-Fernández is an Associate Professor at the School of Telecommunications of the University of Valladolid, Spain. His other publications include the Springer title Tensors in Image Processing and Computer Vision. Dr. Gonzalo Vegas-Sánchez-Ferrero is a Research Fellow at Brigham and Women’s Hospital, and in the Applied Chest Imaging Laboratory of Harvard Medical School, Boston, MA, USA.
Mathematical statistics.
Statistics .
Optical data processing.
Computer simulation.
Biomedical engineering.
Probability and Statistics in Computer Science.
Statistics for Life Sciences, Medicine, Health Sciences.
Image Processing and Computer Vision.
Simulation and Modeling.
Biomedical Engineering and Bioengineering.