Dalca Adrian V, Bobu Andreea, Rost Natalia S, Golland Polina
Computer Science and Artificial Intelligence Lab, EECS, MIT, Cambridge, USA.
Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, USA.
Patch Based Tech Med Imaging (2016). 2016 Oct;9993:60-67. doi: 10.1007/978-3-319-47118-1_8. Epub 2016 Sep 22.
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.
我们介绍一种用于临床环境中获取的脑图像配准的方法。该算法在离散配准框架中依赖三维补丁来估计对应关系。临床图像给计算分析带来了重大挑战。快速采集常常导致图像切片稀疏、存在严重伪影以及视野可变。然而，大型临床数据集包含丰富的临床相关信息。尽管图像配准取得了显著进展，但大多数算法对图像数据的连续性做出了很强的假设，当面对违反这些假设的临床图像时就会失效。在本文中，我们展示了一种用于对齐此类图像的非刚性配准方法。该方法明确地对稀疏可用的图像信息进行建模，以实现稳健的配准。我们在中风患者的临床图像上演示了该算法。所提出的方法优于现有最先进的配准算法，并且避免了这些图像经常导致的灾难性失败。我们提供了该算法的免费开源实现。
