Patch-Based Discrete Registration of Clinical Brain Images.
Patch-Based Discrete Registration of Clinical Brain Images.
复制标题
DOI：
10.1007/978-3-319-47118-1_8
复制
发表时间：
2016-10
期刊：
影响因子：
--
通讯作者：
Golland P
中科院分区：
文献类型：
--
作者：
Dalca AV;Bobu A;Rost NS;Golland P
我们介绍了一种方法，在临床环境中获得的大脑图像的配准。该算法依赖于离散配准框架中的三维补丁来估计对应关系。临床图像对计算分析提出了重大挑战。快速采集通常会导致图像切片稀疏、伪影严重和视野可变。然而，大型临床数据集包含丰富的临床相关信息。尽管在图像配准方面取得了重大进展，但大多数算法对图像数据的连续性做出了强有力的假设，当呈现违反这些假设的临床图像时，这些算法就会失败。在本文中，我们展示了一种非刚性配准方法，用于对齐这些图像。该方法显式地对稀疏可用的图像信息进行建模，以实现鲁棒配准。我们证明了中风患者的临床图像的算法。所提出的方法优于最先进的配准算法，并避免了灾难性的失败，往往造成这些图像。我们提供了一个免费提供的开源实现的算法。
We introduce a method for registration of brain images acquired in clinical settings. The algorithm relies on three-dimensional patches in a discrete registration framework to estimate correspondences. Clinical images present significant challenges for computational analysis. Fast acquisition often results in images with sparse slices, severe artifacts, and variable fields of view. Yet, large clinical datasets hold a wealth of clinically relevant information. Despite significant progress in image registration, most algorithms make strong assumptions about the continuity of image data, failing when presented with clinical images that violate these assumptions. In this paper, we demonstrate a non-rigid registration method for aligning such images. The method explicitly models the sparsely available image information to achieve robust registration. We demonstrate the algorithm on clinical images of stroke patients. The proposed method outperforms state of the art registration algorithms and avoids catastrophic failures often caused by these images. We provide a freely available open source implementation of the algorithm.