摘要
人脑弥散张量DTI图像作为一种非侵入性成像技术,能够提供传统CT、MRI等结构成像方式难以捕捉的白质纤维走行信息,拓展了人脑连接组(human brain connectome)研究、人脑疾病诊断等多个研究领域的数据采集、处理和分析手段,丰富了描述人脑微观解剖结构及功能特征的表示方法,在理论及工程上引起了极大的关注。数字化统计图谱(digital statistical atlas)是计算神经解剖学(computational neuroanatomy)研究框架中一个十分重要的分支。通过对大规模正常人群医学影像样本数据集进行统计意义上的学习和建模,形成蕴含正常人群解剖结构共性特征,并带有一定泛化能力(generalization ability)的数字化统计图谱,是检测未知个体与正常人群结构形态差异性的基础。
研究创建三维DTI图像数字化统计图谱(简称"DTI统计图谱”)具有十分重要的意义,国内外相关研究仍处于起步阶段。DTI图像本质上是一个三维二阶张量场。DTI统计图谱的创建涉及张量模型优化、张量图像配准、张量图像特征表示、图谱统计模型估计等基本问题。本文旨在将适用于传统标量医学影像的统计图谱创建技术加以改进,全面、完整地推广到三维二阶张量场,并通过人脑连接组研究领域的有关应用展示和验证DTI统计图谱的创建效果。具体研究成果和创新点介绍如下：
1.在DTI张量模型中引入了Riemannian流形上的Log-Euclidean测度理论,重构了张量基本运算,为后续的配准、特征表示等研究提供了统一的数学基础。同时,利用该理论提出了保持张量内蕴几何结构一致性的插值算法及改进的张量场正则化算法。实验表明,Log-Euclidean测度能够很好地提高张量场插值和正则化的精度。
2.提出了一种基于Log-Euclidean测度和张量重定向的非刚性配准算法。在将适用于标量图像配准的LDDMM (large deformation diffeomorphic metric mapping)算法和微分同胚Demons (diffeomorphic Demons)算法推广到三维二阶张量场的基础上,利用有限应变(finite strain)模型对微分同胚Demons算法进行了改进,在配准形变过程中增加了张量重定向变换,保证了张量内蕴几何特征的一致性。实验结果表明,改进的微分同胚Demons算法具有较高的配准精度,同时具有较小的计算复杂度,为本文后续创建DTI统计图谱提供了有利条件。
3.在前期高分辨率CT图像纹理特征提取算法研究的基础上,提出了一种DTI图像多尺度特征表示的方法,并设计了TIDA (tensor image discriminative attributes)算法用于构造特征描述向量。利用TIDA算法提取的图像特征结合半自动的Random Walks算法,实现了对脑白质、灰质和脑脊液的分割,以及脑部占位性病变区域的提取。
4.提出了基于大规模样本集DTI统计图谱自动化创建的完整框架,利用前面提出的Log-Euclidean测度下的微分同胚Demons算法,实现了基于多尺度特征描述向量的DTI统计图谱自动化创建。同时,基于统计图谱的微分同胚性和无偏性改进了图谱创建过程中配准模板的交叉优化流程,保证了统计图谱的无偏性和微分同胚性。
5.利用国际上最新的DTI公共数据库成功创建了基于20例正常人群样本数据的Beijing_Zang_20人脑三维DTI统计图谱,并以人脑结构连接组作为研究方向,对自主创建的DTI统计图谱进行了脑网络建模的实证研究。实验中通过配准的方法建立了Beijing_Zang_20图谱与MNI152及ICBM-DTI-81等标准图谱的空间坐标映射关系,并利用ICBM-DTI-81中的脑区划分图谱对自主创建的图谱进行脑区分割。最后结合白质纤维跟踪的结果,获得了脑结构网络的抽象网络拓扑,并计算了网络的几个主要属性参数。
Diffusion tensor imaging technique is adopted to capture the intensity, direction and anisotropy of water diffusion, which is convinced to reflect the micro-structure of biological tissues. As a non-invasive imaging technique, brain DTI images can provide more information about white matter fiber traveling patterns and structures than traditional CT and MRI images. Benefiting from this advantage, DTI has largely broadened the framework of biomedical image collection, pre-and post-processing, and statistical analysis in human brain connectome research and brain diseases diagnosis. Moreover, DTI has contributed to develop new representation theory of brain micro anatomical features and functional characteristics, which has drawn more and more attention in its research. Digital statistical atlas is another important topic in computational neuroanatomy. Construction of statistical atlas based on a large number of normal subjects'medical images can give an abstract description of normal brain anatomy, and the procedure of inter-subject learning and anatomy modeling yields generalization ability, which is crucial to population-based morphometry variation tests.
It's very important to study the construction of three-dimensional DTI statistical atlas in detail. Research in this field has just started, both in domestic and abroad. Essentially, a3D DTI image is equivalent to3D second-order tensor field. The construction of DTI atlas relates to tensor estimation and optimization, tensor field registration, feature representation in tensor field, and atlas estimation. In this paper, techniques of digital statistical atlas construction for scalar medical images, like CT and MRI, have been discussed in detail. Furthermore, these techniques were modified and extended to tensor field for DTI images. Finally, the DTI atlas formed in this paper was validated and evaluated within the application of human brain connectome modeling. The major contributions of this dissertation are as follows.
1. Riemannian manifold and Log-Euclidean metrics were introduced to improve the basic tensor model and the calculus theory of tensor field was reconstructed, which provided the fundamentals of registration, feature representation in tensor field. Meanwhile, the tensor interpolation and regularization algorithm were improved by using Log-Euclidean metrics. Experiments illustrated the improvement of tensor estimation and fiber tracking.
2. An improved diffeomorphic image registration algorithm in tensor field was put forward based on Log-Euclidean metrics and tensor reorientation. Firstly, LDDMM (large deformation diffeomorphic metric mapping) and diffeomorphic Demons were studied and extended to3D2nd-order tensor field. Then, by incorporating finite strain theory, tensor reorientation was added to diffeomorphic Demons, which was proved to keep the intrinsic geometric features of tensor during the deformation procedure. Experiments showed that diffeomorphic Demons overwhelmed with less computational burden, which was more applicable for atlas construction in vast population.
3. On the basis of previous research in high-resolution brain CT texture extraction, a novel multi-scale feature extraction method, named TIDA (tensor image discriminative attributes), was put forward. By integrating the tensor geometric parameters and boundary information, TIDA revealed considerable discriminative power in segmenting brain tissues, like white matter, gray matter and cerebrospinal fluid, as well as brain occupying lesions.
4. An automated DTI digital statistical atlas construction framework was put forward. In this framework, Log-Euclidean diffeomorphic Demons was performed to register inter-subject DTI images, and TIDA provided the multi-scale representation of DTI image features. The most important thing was that the diffeomorphic and non-biasd property had been guaranteed by the improved atlas construction procedure.
5. Within the open-source research database of Human Connectome Project,20cases of normal brain3D DTI images were used to create statistical atlas, named Beijing_Zang_20. And white matter fiber bundles were tracked in the intact brain DTI atlas, with respect to the brain parcellation. The network anatomy of DTI atlas was studied in by incorporating MNI152and ICBM-DTI-81atlas. Various types of network characteristics were generated accordingly.
引文
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