Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

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.

Computer science. --- Mathematical statistics. --- Computer simulation. --- Image processing. --- Statistics. --- Biomedical engineering. --- Computer Science. --- Probability and Statistics in Computer Science. --- Statistics for Life Sciences, Medicine, Health Sciences. --- Image Processing and Computer Vision. --- Simulation and Modeling. --- Biomedical Engineering. --- Signal processing --- Magnetic resonance imaging --- Statistical methods. --- Clinical magnetic resonance imaging --- Diagnostic magnetic resonance imaging --- Functional magnetic resonance imaging --- Imaging, Magnetic resonance --- Medical magnetic resonance imaging --- MR imaging --- MRI (Magnetic resonance imaging) --- NMR imaging --- Nuclear magnetic resonance --- Nuclear magnetic resonance imaging --- Diagnostic use --- Cross-sectional imaging --- Diagnostic imaging --- Computer vision. --- Biomedical Engineering and Bioengineering. --- Computer modeling --- Computer models --- Modeling, Computer --- Models, Computer --- Simulation, Computer --- Electromechanical analogies --- Mathematical models --- Simulation methods --- Model-integrated computing --- Machine vision --- Vision, Computer --- Artificial intelligence --- Image processing --- Pattern recognition systems --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Informatics --- Science --- Clinical engineering --- Medical engineering --- Bioengineering --- Biophysics --- Engineering --- Medicine --- Statistics . --- Optical data processing. --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- Optical computing --- Visual data processing --- Bionics --- Electronic data processing --- Integrated optics --- Photonics --- Computers --- Optical equipment