Statistical analysis of noise in MRI
The book 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. It is based on our experience on the field along the last decade, and we think it provides very helpful materials for MRI researchers.
The book is now available in electronic form in [Springer Link] [Flyer]. The hardcopy will be ready in the next weeks, probably before August. In the following month we will set a webpage with all the code and examples used in the book.
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 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.