在加速并行磁共振成像（pMRI）中，正则化成为重建过程中不可或缺的一部分，因为需要以简约模型的形式利用最具鉴别能力的信息来生成高质量图像，同时减少噪声和伪影。
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.
除了提供各种pMRI重建方法的详细概述和实现细节之外，基于MATLAB的并行MRI正则化图像重建将pMRI正则化图像重建解释为一种有效控制两种特定类型误差信号之间平衡的方法，既可以提高缺失样本估计的精度，也可以加快估计过程。
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.
第一种类型对应于所获得的和其估计值之间的建模误差。
The first type corresponds to the modeling error between acquired and their estimated values.
第二类是由于自标定方法中k空间值的摄动或基于压缩感知的重建模型中的稀疏近似引起的。
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.
本书特点：
提供了在每种重建类型中优化正则化参数的详细信息。
比较了各种pMRI重建的正则化方法。
包括使用临床获得的数据进行案例研究讨论。
为每种重建类型提供了MATLAB代码。
包含自适应正则化以优化速度和精度的方法描述。
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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