Zhang Jinpeng, Zhang Lichi, Xiang Lei, Shao Yeqin, Wu Guorong, Zhou Xiaodong, Shen Dinggang, Wang Qian
Med-X Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States.
Pattern Recognit. 2017 Mar;63:531-541. doi: 10.1016/j.patcog.2016.09.019. Epub 2016 Sep 29.
It is fundamentally important to fuse the brain atlas from magnetic resonance (MR) images for many imaging-based studies. Most existing works focus on fusing the atlases from high-quality MR images. However, for low-quality diagnostic images (i.e., with high inter-slice thickness), the problem of atlas fusion has not been addressed yet. In this paper, we intend to fuse the brain atlas from the high-thickness diagnostic MR images that are prevalent for clinical routines. The main idea of our works is to extend the conventional groupwise registration by incorporating a novel super-resolution strategy. The contribution of the proposed super-resolution framework is two-fold. First, each high-thickness subject image is reconstructed to be isotropic by the patch-based sparsity learning. Then, the reconstructed isotropic image is enhanced for better quality through the random-forest-based regression model. In this way, the images obtained by the super-resolution strategy can be fused together by applying the groupwise registration method to construct the required atlas. Our experiments have shown that the proposed framework can effectively solve the problem of atlas fusion from the low-quality brain MR images.
对于许多基于成像的研究而言，融合来自磁共振（MR）图像的脑图谱至关重要。大多数现有工作都集中于融合来自高质量MR图像的图谱。然而，对于低质量的诊断图像（即切片间厚度较大），图谱融合问题尚未得到解决。在本文中，我们打算融合来自临床常规中普遍存在的高厚度诊断MR图像的脑图谱。我们工作的主要思想是通过纳入一种新颖的超分辨率策略来扩展传统的组间配准。所提出的超分辨率框架的贡献有两方面。首先，通过基于块的稀疏性学习将每个高厚度的受试者图像重建为各向同性。然后，通过基于随机森林的回归模型增强重建后的各向同性图像以获得更好的质量。通过这种方式，通过超分辨率策略获得的图像可以通过应用组间配准方法融合在一起以构建所需的图谱。我们的实验表明，所提出的框架能够有效解决来自低质量脑MR图像的图谱融合问题。
