Magnetic Resonance (MR) imaging produces images of the human tissues in a noninvasive manner, revealing the structure, metabolism, and function of tissues and organs. The impact of this image technique in diagnostic radiology is impressive, due to its versatility and flexibility in joining high-quality anatomical images with functional information.
Signal and image processing play a decisive role in the exploitation of MR imaging features, allowing for the extraction of diagnostic and metabolic information from images.This book attempts to cover all updated aspects of MR image processing, ranging from new acquisition techniques to state-of-art imaging techniques. Because the textbook provides the tools necessary to understand the physical and chemical principles, and the basic signal and image processing concepts and applications, it is a valuable reference book for scientists, and an essential source for upper-level undergraduate and graduate students in these disciplines.
The book’s 18 chapters are divided into five sections. The first section focuses on MR signal and image generation and reconstruction, the basics of MR imaging, advanced reconstruction algorithms, and the parallel MRI field. In the second section, the state-of-art techniques for MR images filtering are described. In particular, the second section covers the signal and noise estimation, the inhomogeneities correction and the more advanced image filtering techniques, taking into account the peculiar features of the noise in MR images. Quantitative analysis is a key issue in MR diagnostic imaging. The third section’s topics range from image registration to integration of EEG and MEG techniques with MR imaging. Two chapters cover the cardiac image quantitative analysis issue. In the fourth section, MR spectroscopy is described, from both the signal generation and the data analysis point of view. Diffusion tensor MR imaging and MR elastography are also examined. Finally, in the last section, the functional MR image processing is described in detail. Fundamentals and advanced data analysis (as exploratory approach), bayesian inference, and nonlinear analysis are also depicted.