Regularization becomes an integral part of the reconstruction process in accelerated parallel magnetic resonance imaging (pMRI) due to the need for utilizing the most discriminative information in the form of parsimonious models to generate high quality images with reduced noise and artifacts. Apart from providing a detailed overview and implementation details of various pMRI reconstruction methods, Regularized image reconstruction in parallel MRI with MATLAB examples interprets regularized image reconstruction in pMRI as a means to effectively control the balance between two specific types of error signals to either improve the accuracy in estimation of missing samples, or speed up the estimation process. The first type corresponds to the modeling error between acquired and their estimated values. The second type arises due to the perturbation of k-space values in autocalibration methods or sparse approximation in the compressed sensing based reconstruction model. Features: Provides details for optimizing regularization parameters in each type of reconstruction. Presents comparison of regularization approaches for each type of pMRI reconstruction. Includes discussion of case studies using clinically acquired data. MATLAB codes are provided for each reconstruction type. Contains method-wise description of adapting regularization to optimize speed and accuracy. This book serves as a reference material for researchers and students involved in development of pMRI reconstruction methods. Industry practitioners concerned with how to apply regularization in pMRI reconstruction will find this book most useful.

Providing many unique MATLAB codes and functions throughout, this book covers the basics of Magnetic Resonance Imaging (MRI), leading to an in-depth understanding of the concepts and tools required for analysis and interpretation of Phase Contrast MR Angiography (PC-MRA). The concept of PC-MRA is often difficult, but essential for practicing engineers and scientists working in MR related areas. The concepts are better understood by uniquely combining the physical principles of fluid flow and MR imaging, laid out by modeling the theory and applications using a commonly used software tool MATLAB (R). The book starts with a detailed theory of PC-MRA followed by a description of various image processing methods, including detailed MATLAB codes used for their implementation. The flow concepts in the context of MR imaging are explained using MATLAB based simulations.