Irfanoglu M Okan, Nayak Amritha, Jenkins Jeffrey, Hutchinson Elizabeth B, Sadeghi Neda, Thomas Cibu P, Pierpaoli Carlo
Section on Quantitative Imaging and Tissue Sciences, National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD 20892, USA; Henry Jackson Foundation, Bethesda, MD 20814, USA.
Section on Quantitative Imaging and Tissue Sciences, National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD 20892, USA; Henry Jackson Foundation, Bethesda, MD 20814, USA.
Neuroimage. 2016 May 15;132:439-454. doi: 10.1016/j.neuroimage.2016.02.066. Epub 2016 Feb 28.
In this work, we propose DR-TAMAS (Diffeomorphic Registration for Tensor Accurate alignMent of Anatomical Structures), a novel framework for intersubject registration of Diffusion Tensor Imaging (DTI) data sets. This framework is optimized for brain data and its main goal is to achieve an accurate alignment of all brain structures, including white matter (WM), gray matter (GM), and spaces containing cerebrospinal fluid (CSF). Currently most DTI-based spatial normalization algorithms emphasize alignment of anisotropic structures. While some diffusion-derived metrics, such as diffusion anisotropy and tensor eigenvector orientation, are highly informative for proper alignment of WM, other tensor metrics such as the trace or mean diffusivity (MD) are fundamental for a proper alignment of GM and CSF boundaries. Moreover, it is desirable to include information from structural MRI data, e.g., T1-weighted or T2-weighted images, which are usually available together with the diffusion data. The fundamental property of DR-TAMAS is to achieve global anatomical accuracy by incorporating in its cost function the most informative metrics locally. Another important feature of DR-TAMAS is a symmetric time-varying velocity-based transformation model, which enables it to account for potentially large anatomical variability in healthy subjects and patients. The performance of DR-TAMAS is evaluated with several data sets and compared with other widely-used diffeomorphic image registration techniques employing both full tensor information and/or DTI-derived scalar maps. Our results show that the proposed method has excellent overall performance in the entire brain, while being equivalent to the best existing methods in WM.
在这项工作中,我们提出了DR-TAMAS(用于解剖结构张量精确对齐的微分同胚配准),这是一种用于扩散张量成像(DTI)数据集主体间配准的新型框架。该框架针对脑数据进行了优化,其主要目标是实现所有脑结构的精确对齐,包括白质(WM)、灰质(GM)和包含脑脊液(CSF)的空间。目前,大多数基于DTI的空间归一化算法都强调各向异性结构的对齐。虽然一些扩散衍生指标,如扩散各向异性和张量特征向量方向,对于WM的正确对齐具有很高的信息量,但其他张量指标,如迹或平均扩散率(MD),对于GM和CSF边界的正确对齐至关重要。此外,还希望纳入来自结构MRI数据的信息,例如T1加权或T2加权图像,这些通常与扩散数据一起提供。DR-TAMAS的基本特性是通过在其代价函数中局部纳入信息量最大的指标来实现全局解剖学精度。DR-TAMAS的另一个重要特征是基于对称时变速度的变换模型,这使其能够考虑健康受试者和患者中潜在的较大解剖变异性。使用多个数据集对DR-TAMAS的性能进行了评估,并与其他广泛使用的采用全张量信息和/或DTI衍生标量图的微分同胚图像配准技术进行了比较。我们的结果表明,所提出的方法在整个大脑中具有出色的整体性能,同时在WM方面与现有最佳方法相当。