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Statistical analysis of noise in MRI[electronic resource] :modeling, filtering and estimation /

紀錄類型:	書目-電子資源 : Monograph/item
杜威分類號:	621.3822
書名/作者:	Statistical analysis of noise in MRI : modeling, filtering and estimation // by Santiago Aja-Fernandez, Gonzalo Vegas-Sanchez-Ferrero.
作者:	Aja-Fernandez, Santiago.
其他作者:	Vegas-Sanchez-Ferrero, Gonzalo.
出版者:	Cham : : Springer International Publishing :, 2016.
面頁冊數:	xxi, 327 p. : : ill. (some col.), digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Signal processing - Statistical methods.
標題:	Magnetic resonance imaging - Statistical methods.
標題:	Computer Science.
標題:	Probability and Statistics in Computer Science.
標題:	Statistics for Life Sciences, Medicine, Health Sciences.
標題:	Image Processing and Computer Vision.
標題:	Simulation and Modeling.
標題:	Biomedical Engineering.
ISBN:	9783319399348
ISBN:	9783319399331
內容註:	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.
摘要、提要註:	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-Fernandez 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-Sanchez-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.
電子資源:	http://dx.doi.org/10.1007/978-3-319-39934-8

Statistical analysis of noise in MRI[electronic resource] :modeling, filtering and estimation /
Aja-Fernandez, Santiago.

Statistical analysis of noise in MRImodeling, filtering and estimation /[electronic resource] :by Santiago Aja-Fernandez, Gonzalo Vegas-Sanchez-Ferrero. - Cham :Springer International Publishing :2016. - xxi, 327 p. :ill. (some col.), digital ;24 cm.

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-Fernandez 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-Sanchez-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.

ISBN: 9783319399348

Standard No.: 10.1007/978-3-319-39934-8doiSubjects--Topical Terms:

416778
Signal processing
--Statistical methods.

LC Class. No.: TK5102.9 / .A33 2016

Dewey Class. No.: 621.3822

Statistical analysis of noise in MRI[electronic resource] :modeling, filtering and estimation /
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