Iterative image reconstruction in MRI with separate magnitude and phase regularization
Iterative image reconstruction in MRI with separate magnitude and phase regularization
复制标题
具有单独幅度和相位正则化的 MRI 迭代图像重建
DOI：
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发表时间：
2004
期刊：
影响因子：
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通讯作者：
D. Noll
中科院分区：
文献类型：
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作者：
J. Fessler;D. Noll
MRI图像重建的迭代方法在许多应用中都很有用，包括非笛卡尔k空间样本的重建，磁场不均匀性的补偿以及多个接收线圈的成像。现有的迭代MR图像重建方法要么是非正则化的，因此对噪声敏感，要么使用正则化方法平滑复值图像。这些现有的方法对图像的实分量和虚分量进行同等的正则化。在许多MRI应用中，包括fMRI BOLD成像中使用的T/sub 2/*加权成像，人们期望大多数感兴趣的信号信息包含在体素值的大小中，而相位值则期望在空间上平滑变化。本文提出了对幅值分量和相位分量分别进行正则化的方法，在对相位分量进行强正则化的同时保留了幅值分量的空间分辨率。这导致了一个非凸正则化最小二乘代价函数。我们描述了一种新的迭代算法，该算法单调地降低了该代价函数。相对于传统的正则化方法，得到的图像具有较低的噪声。
Iterative methods for image reconstruction in MRI are useful in several applications, including reconstruction from non-Cartesian k-space samples, compensation for magnetic field inhomogeneities, and imaging with multiple receive coils. Existing iterative MR image reconstruction methods are either unregularized, and therefore sensitive to noise, or have used regularization methods that smooth the complex valued image. These existing methods regularize the real and imaginary components of the image equally. In many MRI applications, including T/sub 2/*-weighted imaging as used in fMRI BOLD imaging, one expects most of the signal information of interest to be contained in the magnitude of the voxel value, whereas the phase values are expected to vary smoothly spatially. This paper proposes separate regularization of the magnitude and phase components, preserving the spatial resolution of the magnitude component while strongly regularizing the phase component. This leads to a non-convex regularized least-squares cost function. We describe a new iterative algorithm that monotonically decreases this cost function. The resulting images have reduced noise relative to conventional regularization methods.