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Co-registration and distortion correction of diffusion and anatomical images based on inverse contrast normalization

机译:基于逆对比度归一化的弥散和解剖图像的共配准和畸变校正

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Diffusion MRI provides quantitative information about microstructural properties which can be useful in neuroimaging studies of the human brain. Echo planar imaging (EPI) sequences, which are frequently used for acquisition of diffusion images, are sensitive to inhomogeneities in the primary magnetic (B-0) field that cause localized distortions in the reconstructed images. We describe and evaluate a new method for correction of susceptibility-induced distortion in diffusion images in the absence of an accurate B-0 fieldmap. In our method, the distortion field is estimated using a constrained non-rigid registration between an undistorted T1-weighted anatomical image and one of the distorted EPI images from diffusion acquisition. Our registration framework is based on a new approach, INVERSION (Inverse contrast Normalization for VERy Simple registratION), which exploits the inverted contrast relationship between T1- and T2-weighted brain images to define a simple and robust similarity measure. We also describe how INVERSION can be used for rigid alignment of diffusion images and T1-weighted anatomical images. Our approach is evaluated with multiple in vivo datasets acquired with different acquisition parameters. Compared to other methods, INVERSION shows robust and consistent performance in rigid registration and shows improved alignment of diffusion and anatomical images relative to normalized mutual information for non-rigid distortion correction. (C) 2015 Elsevier Inc. All rights reserved.
机译:扩散MRI可提供有关微结构特性的定量信息,这些信息可用于人脑的神经成像研究。经常用于获取扩散图像的回波平面成像(EPI)序列对主磁场(B-0)场中的不均匀性敏感,该不均匀性会导致重建图像中的局部失真。我们描述和评估一种新的方法,用于在没有精确的B-0场图的情况下校正扩散图像中的磁化率诱发的失真。在我们的方法中,使用未失真的T1加权解剖图像与来自扩散采集的失真的EPI图像之一之间的受约束的非刚性配准来估计失真场。我们的注册框架基于一种新方法INVERSION(VERy简单注册的反对比度标准化),该方法利用T1和T2加权脑图像之间的反差关系来定义一种简单而强大的相似性度量。我们还描述了如何将INVERSION用于扩散图像和T1加权解剖图像的刚性对齐。我们的方法是通过使用不同采集参数采集的多个体内数据集进行评估的。与其他方法相比,INVERSION在刚性配准方面表现出鲁棒且一致的性能,并且相对于用于非刚性变形校正的归一化互信息而言,显示出扩散和解剖图像的对齐改善。 (C)2015 Elsevier Inc.保留所有权利。

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