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.
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