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Regularized Image Reconstruction in Parallel MRI with MATLAB (Hardcover)
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Regularized Image Reconstruction in Parallel MRI with MATLAB (Hardcover)
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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.
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