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Dynamic elasticity model for inter-subject non-rigid registration of 3D MRI brain scans

机译:用于3D MRI脑部扫描的受试者间非刚性配准的动态弹性模型

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The purpose of this paper is to present a new approach for inter-subject non-rigid registration of 3D MR brain images which could assist in automatic labeling of brain structures. A physical dynamic elasticity model (DEM) is developed which tends to represent the complex non-linear deformations as elastic waves. The transformation is governed by elastodynamics wave equation. The registration process ensues in a hierarchical fashion, thus reducing the risk of obtaining a local optimal transformation. Along with the correction of local misalignments, it also removes global shape differences without any prior initialization. The proposed scheme was compared against high ranking registration methods including: DROP, SyN, ART and DRAMMS. The results were quantitatively analyzed by computing and testing the statistical significance of the volume overlap measures and Hausdorff distance for segmented structures with DROP, SyN, ART, DRAMMS and DEM registration methods. Experimental results demonstrate that the proposed DEM registration method leads to very promising results when applied to the problem of inter-subject registration and that favorably compares against classical registration approaches. Since DEM registration method is able to reduce registration errors significantly, hence it could be used to automatically label the anatomical structures for clinical studies. (C) 2016 Elsevier Ltd. All rights reserved.
机译:本文的目的是提出一种用于3D MR脑图像的受试者间非刚性配准的新方法,该方法可以帮助自动标记脑结构。建立了物理动态弹性模型(DEM),该模型倾向于将复杂的非线性变形表示为弹性波。该变换由弹性动力学波动方程控制。登记过程以分层的方式进行,因此降低了获得局部最优变换的风险。除了校正局部未对准以外,它还可以消除全局形状差异,而无需任何事先初始化。将该提议的方案与包括DROP,SyN,ART和DRAMMS的高级注册方法进行了比较。通过计算和测试采用DROP,SyN,ART,DRAMMS和DEM配准方法的分段结构的体积重叠量度和Hausdorff距离的统计显着性,对结果进行了定量分析。实验结果表明,提出的DEM配准方法在应用于科目间配准的问题上可带来非常有希望的结果,并且与经典配准方法相比具有优势。由于DEM配准方法能够显着减少配准错误,因此可用于自动标记解剖结构以进行临床研究。 (C)2016 Elsevier Ltd.保留所有权利。

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