In accelerated parallel magnetic resonance imaging (pMRI), regularization becomes an indispensable part of the reconstruction process, because the most discriminative information needs to be used in the form of a reduced model to generate high-quality images while reducing noise and artifacts.
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.
In addition to providing a detailed overview and implementation details of various pMRI reconstruction methods, MATLAB-based parallel MRI regularized image reconstruction interprets pMRI regularized image reconstruction as a method to effectively control the balance between two specific types of error signals. The accuracy of missing sample estimation can be improved, and the estimation process can also be speeded up.
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 the obtained and its estimated value.
The first type corresponds to the modeling error between acquired and their estimated values.
The second category is caused by the perturbation of the k-space value in the self-calibration method or the sparse approximation in the reconstruction model based on compressed sensing.
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 of this book:
Provides detailed information on optimizing regularization parameters in each reconstruction type.
Various regularization methods of pMRI reconstruction are compared.
Include case study discussions using clinically obtained data.
MATLAB code is provided for each reconstruction type.
Contains a description of methods for adaptive regularization to optimize speed and accuracy.
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.
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