基于CTA影像数据的3D冠脉狭窄自动检测及其量化评估研究

项目名称： 基于CTA影像数据的3D冠脉狭窄自动检测及其量化评估研究

项目编号： No.61472042

项目类型： 面上项目

立项/批准年度： 2015

项目学科： 计算机科学学科

项目作者： 田沄

作者单位： 北京师范大学

项目金额： 82万元

中文摘要： 冠状动脉CT血管造影术(CCTA)是当前诊断冠心病的常用手段，而冠脉量化分析是计算机辅助诊断该类疾病的重要依据；目前常用的多平面重组或曲面重组技术需大量人工干预，难以实现狭窄的自动分析。针对上述问题，本项目拟研究3D冠脉狭窄检测及其量化评估的关键技术，建立冠脉量化分析的理论框架，将目标描述、目标相似性测度、变分模型和参数关系模型结合起来，解决边界模糊、结构细小和灰度分布不均匀冠脉的狭窄检测及量化评估问题。具体包括：研究复杂形状的目标分割，构造先验约束变分模型和形状概率模型；研究冠脉形状描述方法，定义血管测度，建立冠脉分支搜索、连接准则，提出3D冠脉精确高效分割和中心线精确抽取算法；研究管腔直径参数估计方法，确定观测数据一致性策略，建立直径参数关系模型，检测血管狭窄位置，量化狭窄程度，实现狭窄分级；采用概率模型及最新标准评估框架和平台，验证所提算法，以满足对冠心病辅助诊疗的临床需要。

中文关键词： 图像分割；图像分析；冠状动脉；变分模型；参数估计模型

英文摘要： Coronary computed tomography angiography (CCTA) is a common means of diagnosis in coronary artery diseases, and quantitative analysis of coronary artery is important for computer-aided diagnosis. Currently, the multiplanar reformation or the curve planner reformation is widely used in the clinical diagnosis. However, this method requires a large amount of manual intervention, and it is difficult to automatically analyse coronary artery stenoses. To address the above-mentioned problems,we will study the key techniques of automatic detection and quantification of 3D coronary artery stenoses in the project, and establish the general theoretical framework for stenosis quantitative analysis based on partial differential equations. We combine the object description, object similarity measure, variational model and parameter relationship model to resolve the problems of detection and quantification of coronary artery stenoses, such as blur vessel boundaries, thin vessels, and intensity inhomogeneity.Specific studies are as follows: Study the segmentation of objects with complex shapes, and construct the variational model with a priori constraint and a shape probabilistic model; Study the description of coronary shape, define a vesselness measure based on tensor analysis, build the search and connection criterions for the branches of coronary artery tree, and propose the segmentation algorithm for the 3D coronary artery with accuracy and efficiency, and the extraction algorithm for the centerlines with accuracy; Study the parameter estimation model for diameter parameter of the lumen, clear the corresponding relationship between fuzzy distance transform and observed diameter, determine the coherence analysis strategy of the observed data, build the functional relationship model of the expected diameter and observed diameter, detect the stenosis locations, quantify the stenoses degree, and realize the stenoses grade; Employ the probabilistic model and the lastest standard evaluation framework and platform, and verify and evaluate the proposed algorithms, to satisfy the requirments of clinical application.

英文关键词： image segmentation;image analysis;coronry artery;variational model;parameter estimation model