Hadjidemetriou Stathis, Studholme Colin, Mueller Susanne, Weiner Michael, Schuff Norbert
NCIRE/VA UCSF, San Francisco, CA 94121.
Proc SPIE Int Soc Opt Eng. 2007 Mar 5;6512:65121L. doi: 10.1117/12.711533.
MRI at high magnetic fields (> 3.0 T ) is complicated by strong inhomogeneous radio-frequency fields, sometimes termed the "bias field". These lead to nonuniformity of image intensity, greatly complicating further analysis such as registration and segmentation. Existing methods for bias field correction are effective for 1.5 T or 3.0 T MRI, but are not completely satisfactory for higher field data. This paper develops an effective bias field correction for high field MRI based on the assumption that the nonuniformity is smoothly varying in space. Also, nonuniformity is quantified and unmixed using high order neighborhood statistics of intensity cooccurrences. They are computed within spherical windows of limited size over the entire image. The restoration is iterative and makes use of a novel stable stopping criterion that depends on the scaled entropy of the cooccurrence statistics, which is a non monotonic function of the iterations; the Shannon entropy of the cooccurrence statistics normalized to the effective dynamic range of the image. The algorithm restores whole head data, is robust to intense nonuniformities present in high field acquisitions, and is robust to variations in anatomy. This algorithm significantly improves bias field correction in comparison to N3 on phantom 1.5 T head data and high field 4 T human head data.
高磁场（> 3.0 T）下的磁共振成像（MRI）会受到强非均匀射频场的影响，这种场有时被称为“偏置场”。这些会导致图像强度不均匀，极大地增加了诸如配准和分割等进一步分析的复杂性。现有的偏置场校正方法对1.5 T或3.0 T的MRI有效，但对于更高场的数据并不完全令人满意。本文基于非均匀性在空间中平滑变化的假设，开发了一种针对高场MRI的有效偏置场校正方法。此外，利用强度共生的高阶邻域统计对非均匀性进行量化和分离。它们在整个图像上有限大小的球形窗口内计算。恢复过程是迭代的，并使用一种新颖的稳定停止准则，该准则取决于共生统计的缩放熵，缩放熵是迭代次数的非单调函数；共生统计的香农熵归一化到图像的有效动态范围。该算法可恢复全脑数据，对高场采集中存在的强烈非均匀性具有鲁棒性，并且对解剖结构的变化具有鲁棒性。与N3算法相比，该算法在1.5 T头部模型数据和4 T高场人体头部数据上显著改善了偏置场校正效果。
