Efficient Groupwise Registration for Brain MRI by Fast Initialization.
Efficient Groupwise Registration for Brain MRI by Fast Initialization.
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DOI：
10.1007/978-3-319-67389-9_18
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Shen D
中科院分区：
文献类型：
--
作者：
Dong P;Cao X;Zhang J;Kim M;Wu G;Shen D
分组图像配准提供了一个无偏的配准解决方案，对人口的图像，这可以促进随后的人口分析。然而，对于在大的图像集合上执行成组配准通常在计算上是昂贵的。为了缓解这个问题，我们建议利用快速初始化技术，加快groupwise注册。我们的主要思想是生成一组模拟的大脑MRI样本，其组中心具有已知的变形。这可以在训练阶段通过两个步骤来实现。首先，一组训练的大脑MR图像被注册到他们的组中心与一定的现有groupwise注册方法。然后，为了增加样本，我们对所获得的变形场的集合（到组中心）执行PCA以参数化变形场。在这样做的过程中，我们可以生成大量的变形场，以及使用PCA的不同参数生成它们各自的模拟样本。在应用阶段，当给定一组新的测试脑MR图像时，我们可以将它们与增强的训练样本混合。然后，对于每幅测试图像，我们可以在增强的训练数据集中找到它最接近的样本，以快速估计其变形场到训练集的组中心。以这种方式，可以立即估计测试图像集的暂定组中心，并且可以获得每个测试图像相对于该估计的组中心的变形场。有了这种快速初始化的测试图像的分组配准，我们最终可以使用现有的分组配准方法，以快速细化分组配准结果。在ADNI数据集上的实验结果表明，与现有的分组配准方法相比，该方法在计算效率和配准精度上都有显著提高。
Groupwise image registration provides an unbiased registration solution upon a population of images, which can facilitate the subsequent population analysis. However, it is generally computationally expensive for performing groupwise registration on a large set of images. To alleviate this issue, we propose to utilize a fast initialization technique for speeding up the groupwise registration. Our main idea is to generate a set of simulated brain MRI samples with known deformations to their group center. This can be achieved in the training stage by two steps. First, a set of training brain MR images is registered to their group center with a certain existing groupwise registration method. Then, in order to augment the samples, we perform PCA on the set of obtained deformation fields (to the group center) to parameterize the deformation fields. In doing so, we can generate a large number of deformation fields, as well as their respective simulated samples using different parameters for PCA. In the application stage, when given a new set of testing brain MR images, we can mix them with the augmented training samples. Then, for each testing image, we can find its closest sample in the augmented training dataset for fast estimating its deformation field to the group center of the training set. In this way, a tentative group center of the testing image set can be immediately estimated, and the deformation field of each testing image to this estimated group center can be obtained. With this fast initialization for groupwise registration of testing images, we can finally use an existing groupwise registration method to quickly refine the groupwise registration results. Experimental results on ADNI dataset show the significantly improved computational efficiency and competitive registration accuracy, compared to state-of-the-art groupwise registration methods.
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