AUTOMATIC ANALYSIS OF 3-D AND 4-D RADIOLOGICAL IMAGES

描述：(改编自申请者摘要)多种放射成像 现在存在生成三维(3-D)和四个 解剖的三维(4-D)图像。不幸的是，这些技术 对这样的数据集进行分析和测量仍然很麻烦。 简化3-D/4-D图像的分析对于能够 评估3-D/4-D成像方式的长期实用性。这项建议 解决了这一普遍问题。据信，自动化分析 系统在操作员提供的提示的辅助下，可以更彻底、更 更准确、更高效地从 3-D/4-D放射成像技术优于纯手工技术。 申请者提议设计一种交互式的自动化3-D/4-D 放射图像分析系统，操作员可通过该系统定义多个 通过灵活的显示屏提供各种标志性和象征性的问题提示 与进入面向对象模型的提示接口时， 交互式动态场景(IDS)模型。利用问题信息 存储在入侵检测系统模型中的自动场景分析(ASA)引擎提取 所需的解剖区域。ASA引擎使用多阶段分析 利用器官形态、几何(基于形状)和 功能特性；它还使用全局拓扑 (器官到器官)限制。 这个项目的早期重点是设计用于 ASA引擎和入侵检测系统模型。随着方法的成熟，申请者提议 构建该系统的工作原型。验证将会完成 并将重点放在四个应用上：(1)提取 3-D/4-D X射线计算机断层扫描的心内膜和心外膜边界 (2)真三维血管造影的分析；(3)提取和 上呼吸道的三维磁共振成像(MRI)分析 (4)先天性心脏病的三维MRI图像分析。 虽然算法正在设计中，但验证测试涉及到一个小的 数据子集，将完成以建立算法功能。 后来，随着算法的成熟，使用更大的序列进行详细的测试 数据，将对自动生成的数据进行定量比较 结果到手动生成的结果。

DESCRIPTION: (Adapted from Applicant's Abstract) Many radiological imaging modalities now exist that generate three-dimensional (3-D) and four dimensional (4-D) images of the anatomy. Unfortunately, techniques for analyzing and making measurements on such data sets remain cumbersome. Simplying the analysis of 3-D/4-D images is critical to being able to assess the long-term utility of 3-D/4-D imaging modalities. This proposal addresses this general issue. It is believed that an automated analysis system, assisted by operator-supplied cues, can more thoroughly, more accurately, and more efficiently extract anatomical information from a 3-D/4-D radiological image than purely manual techniques. The applicants propose to devise an interactive automated 3-D/4-D radiological image analysis system by which the operator can define many types of iconic and symbolic problem cues through a flexible display interface with the cues going into an object-oriented model, the interactive dynamic scene (IDS) model. Drawing on the problem information stored in the IDS model, an automatic scene-analysis (ASA) engine extracts the desired anatomical regions. The ASA engine uses a multi-stage analysis procedure that exploits organ morphological, geometrical (shape-based), and functional characteristics; it also uses global topological (organ-to-organ) constraints. The early emphasis of this project is on devising the algorithms for the ASA engine and IDS model. As methods mature, the applicants propose to construct a working prototype of the system. Validation would be done throughout and would focus on four applications: (1) extraction of the endocardial and epicardial borders from 3-D/4-D X-ray computed tomography (CT) images; (2) analysis of true 3-D angiograms; (3) extraction and analysis of the upper airway in 3-D Magnetic Resonance Imaging (MRI) images; and (4) analysis of congenital heart defects in 3-D MRI images. While algorithms are being devised, validation tests, involving a small subset of data, would be done to establish the algorithm functionality. Later, as algorithms mature, detailed tests, using larger sequences of data, would be done to quantitatively compare automatically generated results to manually generated results.