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基于核磁共振图像的心脏运动分析
其他题名Cardiac Contraction Analysis Based on MRI
丛龙飞
学位类型工学博士
导师蒋田仔 ; 杨广中
2007-05-24
学位授予单位中国科学院研究生院
学位授予地点中国科学院自动化研究所
学位专业模式识别与智能系统
关键词磁共振图像 心脏运动跟踪 调和映射 属性向量 脑皮层配准 心肌运动统计分析 Mri Attribute Vector Based Registration Harmonic Embedding Cardiac Motion Tracking Cardiac Contractility Analysis Cortical Surface Registration
摘要基于核磁共振的心脏运动分析是诊断心肌萎缩等疾病和评价治疗效果的重要方法之一。基于核磁共振速度编码成像所获取的心肌运动速度图像,本文分析了心肌三维收缩运动。根据心脏形状建立模板,本文结合基于属性向量的图像配准方法与调和映射方法来完成左心室的运动跟踪和个体间配准,并且基于结构映射的向量场映射进行速度场的映射,对心肌运动进行统计分析。论文的主要贡献如下: 1. 提出了一种新的结合调和映射和属性向量的配准方法。首先通过把原图像和目标图像用控制点约束的调和映射映射到一个光滑的、凸的简单模板,得到初始配准结果。然后利用基于属性向量的配准方法在模板上进行原图像和目标图像间的细化配准。同时本文把配准方法应用到大脑皮层配准,即通过调和映射把半脑皮层映射到球面模板,然后利用基于属性向量的配准方法进行配准。基于皮层配准结果进行多人的统计分析和脑皮层结构的特征分析。 2. 通过分析、讨论流形的映射与流形上纤维丛映射之间的关系,对比流形上切空间和法空间的映射方式得到心肌速度场的映射方式。基于左心室与模板间的映射,把心肌运动的速度场映射到模板的向量空间上。由于模板是光滑的凸半球形,所以心肌运动的径向应变、周向应变和经向应变等张量应变可以更容易计算,并不必担心计算区域边界问题。同时对比、分析基于不同模板的向量场、速度场的映射结果,分析模板对向量映射分析的影响。 3. 在心肌运动统计分析中,由于数据采集时不同个体在一个心动周期内获得图像的数目不同,所以本文首先通过个体内的运动跟踪配准建立每个人不同时相左心室体节点的一一对应。并把所有人的数据线性插值成相同数目的数据,然后进行个体间同一时相心室的配准。基于心室体与模板之间的映射把心肌运动的速度场、张量应变场映射到目标个体对应时相,并进行统计分析,获得心肌运动的平均速度场和平均张量应变场等心肌运动参数。
其他摘要The analysis of myocardial contractility is important to the understanding of intrinsic mechanics of cardiovascular diseases. In this thesis, a new technique based on Magnetic Resonance (MR) velocity mapping is presented, which uses attribute vector based image registration combined with harmonic mapping for revealing intra- and inter-subject contractile variability. With this technique, the myocardial velocity data obtained by MRI is first warped to a uniform domain followed by statistical analysis of the cardiac motion. The main contributions of this dissertation are as follows: Feature based registration and harmonic embedding - We combine harmonic mapping with an attribute vector based mesh matching method to obtain accurate matching of the anatomical volume to a uniform domain made of a hollow semi-spherical template. By harmonic mapping with manually identified landmarks, the epi- and endo-cardium surfaces of the left ventricle (LV) are first mapped to the template. This presents a good initial matching result and a smooth convex search region for subsequent mesh matching. The optimal position for each point in the template is found by solving a quadratic optimization problem based on these attribute vectors. The proposed method enables registration at sub-voxel accuracy and is more accurate than the usual feature based registration approach. In this thesis, the proposed method has also been generalized to cortical surface matching, for which further landmarks including central sulcus, precentral gyrus and postcentral gyrus are used to improve the matching results. This makes the cortical surface analysis more accurate, as required for cortical complexity analysis. Warping of myocardial velocity field for cardiac contraction analysis - By analyzing the relationship between vector bundle and manifold mapping, issues related to the use of Jacobian matrix for manifold mapping are identified. This is the mapping function of the manifold tangent vector bundle, which may work incorrectly, for example, for the normal vector bundle. In the thesis, a volumetric embedding framework is proposed for myocardial velocity mapping. This provides the basis for consistent volume matching and vector correspondence, in addition to the ease of calculating biomechanical indices such as radial, circumferential and longitudinal strain rates without the concern of boundary effects. Group-wise left ventricular volumetric matching and myocardial strain rate analysis – For this study, in vivo datasets from six subjects with different number of time frames (TF) were used. For group-wise myocardial contractility analysis, the LV volumes of all TF in the cardiac cycle are matched first. For temporal alignment, the LV volumes of different patients are interpolated into the same number of TF by linear interpolation.
馆藏号XWLW1116
其他标识符200318014602999
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/5971
专题毕业生_博士学位论文
推荐引用方式
GB/T 7714
丛龙飞. 基于核磁共振图像的心脏运动分析[D]. 中国科学院自动化研究所. 中国科学院研究生院,2007.
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