通过排除脑脊液信号来改善扩散加权图像中涡流引起的失真的校正。
Improving the correction of eddy current-induced distortion in diffusion-weighted images by excluding signals from the cerebral spinal fluid.
机构信息
Key Laboratory of Brain Functional Genomics, Ministry of Education & Shanghai Key Laboratory of Brain Functional Genomics, Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China.
出版信息
Comput Med Imaging Graph. 2012 Oct;36(7):542-51. doi: 10.1016/j.compmedimag.2012.06.004. Epub 2012 Jul 24.
Abstract
Iterative cross-correlation (ICC) is the most popularly used schema for correcting eddy current (EC)-induced distortion in diffusion-weighted imaging data, however, it cannot process data acquired at high b-values. We analyzed the error sources and affecting factors in parameter estimation, and propose an efficient algorithm by expanding the ICC framework with a number of techniques: (1) pattern recognition for excluding brain ventricles; (2) ICC with the extracted ventricle for parameter initialization; (3) gradient-based entropy correlation coefficient (GECC) for optimal and finer registration. Experiments demonstrated that our method is robust with high accuracy and error tolerance, and outperforms other ICC-family algorithms and popular approaches currently in use.
摘要
迭代互相关（ICC）是最常用于校正扩散加权成像数据中涡流（EC）引起的失真的方案，但是，它不能处理在高 b 值下采集的数据。我们分析了参数估计中的误差源和影响因素，并通过扩展 ICC 框架并采用多种技术提出了一种有效的算法：（1）模式识别以排除脑室内；（2）使用提取的脑室进行 ICC 以进行参数初始化；（3）基于梯度的熵相关系数（GECC）进行最佳和更精细的配准。实验表明，我们的方法具有高准确性和容错性，优于其他 ICC 族算法和当前使用的流行方法。
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