Removing background phase variations in susceptibility-weighted imaging using a fast, forward-field calculation.
Removing background phase variations in susceptibility-weighted imaging using a fast, forward-field calculation.
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DOI：
10.1002/jmri.21693
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发表时间：
2009-04
影响因子：
4.4
通讯作者：
Haacke, E. Mark
中科院分区：
文献类型：
--
作者：
Neelavalli, Jaladhar;Cheng, Yu-Chung N.;Jiang, Jing;Haacke, E. Mark
关键词：
估计空气-组织界面几何形状引起的磁场变化，并从磁敏感加权成像（SWI）数据中消除其影响。基于傅立叶变换的场估计方法用于计算由空气-组织界面几何形状引起的场偏差。这首先通过手动绘制或自动检测鼻窦、乳突腔和头部几何形状来实现。然后使用剩余相位最小化方法计算脑组织和空气空间之间的磁化率差Δ X。通过从原始相位图像中减去预测相位来校正SWI数据。然后使用所得相位图像来执行SWI后处理。显着改善后处理的SWI数据证明，最显着的是在额叶和中脑区域，并在较小程度上在大脑的边界。具体而言，在空气-组织界面附近的相位校正之后，信号的丢失少得多，使得可以看到通常被常规SWI后处理错误地去除的血管和结构。基于傅里叶变换的场估计方法是一种强大的3D背景相位去除方法，用于改善SW图像，提供更清晰的前脑和中脑区域的图像。
To estimate magnetic field variations induced from air-tissue interface geometry and remove their effects from susceptibility weighted imaging (SWI) data. A Fourier-transform-based field estimation method is used to calculate the field deviation arising from air-tissue interface geometry. This is accomplished by, first, manually drawing or automatically detecting the sinuses, the mastoid cavity and the head geometry. The difference in susceptibility, Δχ, between brain tissue and air-spaces is then calculated using a residual-phase minimization approach. SWI data are corrected by subtracting the predicted phase from the original phase image. Resultant phase images are then used to perform the SWI post-processing. Significant improvement in the post-processed SWI data is demonstrated, most notably in the frontal and midbrain regions and to a lesser extent at the boundary of the brain. Specifically, there is much less dropout of signal after phase correction near air-tissue interfaces making it possible to see vessels and structures that were often incorrectly removed by the conventional SWI post-processing. The Fourier-transform-based field estimation method is a powerful 3D background phase removal method for improving SW images, providing clearer images of the fore-brain and the mid-brain regions.
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DOI：
10.1002/cmr.b.10083
发表时间：
2003-10-01
影响因子：
0.9
作者：
Salomir, R;De Senneville, BD;Moonen, CTW
通讯作者：
Moonen, CTW
影响因子：
4.4
作者：
Pandian, Daniel S. J.;Ciulla, Carlo;Ayaz, Muhammad
通讯作者：
Ayaz, Muhammad
影响因子：
3.5
作者：
Tong, K. A.;Ashwal, S.;Haacke, E. M.
通讯作者：
Haacke, E. M.
影响因子：
4.4
作者：
Rauscher, A;Barth, M;Moser, E
通讯作者：
Moser, E
影响因子：
4.4
作者：
Haacke, E. Mark;Ayaz, Muhammad;Kirsch, Wolff
通讯作者：
Kirsch, Wolff