Correction of MR k-space data corrupted by spike noise
Correction of MR k-space data corrupted by spike noise
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DOI：
10.1109/42.875184
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发表时间：
2000-07-01
影响因子：
10.6
通讯作者：
MacFall, JR
中科院分区：
文献类型：
--
作者：
Kao, YH;MacFall, JR
磁共振图像是从数字化的原始数据中重建的，这些数据是在空间频率域（也称为k空间）中收集的。有时，k空间数据中的单个或多个数据点被尖峰噪声破坏，导致图像中的条纹伪影。用于检测损坏的数据点的采样保持方法可能由于小的改变而失败，特别是对于k空间变化大的低空间频率区域中的数据点。使用相邻像素的插值来恢复损坏的数据点可能会给出不正确的结果。我们提出了一种傅立叶变换方法，用于检测和恢复损坏的数据点，使用来自图像或中间域中的条纹伪影结构的窗口滤波器。该方法提供了一个分析解决方案，在每个损坏的数据点的变化。它可以有效地恢复损坏的L空间数据，消除图像中的条纹伪影，只要满足以下三个条件。首先，已知信号分布的区域（例如，空气背景）在图像或中间域中是可见的，使得可以使用窗口滤波器来选择该区域。第二，多个尖峰由窗口滤波器的点扩散函数的半最大值处的全宽分离。第三，尖峰的幅度大于由窗口滤波器和k空间随机噪声的标准偏差确定的最小可检测值。
Magnetic resonance images are reconstructed from digitized raw data, which are collected in the spatial-frequency domain (also called k-space). Occasionally, single or multiple data points in the k-space data are corrupted by spike noise, causing striation artifacts in images. Thresholding methods for detecting corrupted data points can fail because of small alterations, especially for data points in the low spatial frequency area where the k-space variation is large. Restoration of corrupted data points using interpolations of neighboring pixels can give incorrect results. We propose a Fourier transform method for detecting and restoring corrupted data points using a window filter derived from the striation-artifact structure in an image or an intermediate domain. The method provides an analytical solution for the alteration at each corrupted data point. It can effectively restore corrupted L-space data, removing striation artifacts in images, provided that the following three conditions are satisfied. First, a region of known signal distribution (for example, air background) is visible in either the image or the intermediate domain so that it can be selected using a window filter. Second, multiple spikes are separated by the full-width at half-maximum of the point spread function for the window filter. Third, the magnitude of a spike is larger than the minimum detectable value determined by the window filter and the standard deviation of k-space random noise.