Efficient Interpretation of 3D Vascular Image Data

描述（由申请人提供）：计算机断层扫描的最新发展 已经产生了三维血管成像的能力， 比传统血管造影术侵入性更低。然而，这些事态发展 也导致了为每位患者生成数千张图像的潜力 影像学解释研究。因此，它变得越来越 难以分别读取每个图像，并且难以在 合理的时间。因此，我们建议开发和验证 一种改变容积CT血管影像学解释的技术 从单个横截面视觉检查到范例的图像数据 结合了高效、符合人体工程学和交互式体积 可视化和定量分析。为此，在前三个 多年的这个项目，我们将开发一个系统的硬件和软件 专门为这项任务设计的。它将包括一个大面积的 高分辨率显示器，一套人机界面， 设计用于促进与大体积血管数据集的交互， 以及能够指导所需交互的智能软件， 生成血管特异性可视化和定量结果， 最小的努力我们会在一九九九年第四年进行一项临床试验研究， 拟议的工作，在此期间，放射科医生将比较使用一个 原型系统具有传统的逐图像阅读， 效率我们将重点分析（1）髂动脉瘤和（2） 下肢闭塞性疾病，作为新技术的测试案例。后 在完成这些研究后，我们预计我们的发展将很容易 适用于血管成像内外的其他应用，如 以及其它成像模态，例如MRI。我们的总体目标是改变 并验证通常解释横截面图像的方式，因此， 从而提高了评估日益增加的 大量的医学图像数据。

DESCRIPTION (Provided by Applicant): Recent developments in Computed Tomography have resulted in the capability to image blood vessels in three dimensions and less invasively than conventional angiography. However, these developments have also resulted in the potential to generate thousands of images per patient study for radiological interpretation. As a result, it has become increasingly difficult to read each image separately and to reach an accurate diagnosis in a reasonable amount of time. Therefore, we propose to develop and validate technology that changes radiological interpretation of volumetric CT vascular image data from visual inspection of individual cross-sections to a paradigm that combines highly efficient, ergonomic, and interactive volumetric visualization and quantitative analysis. To this end, over the first three years of this project, we will develop a system of hardware and software specifically designed for this task. It will consist of a large-area high-resolution display, a set of human-computer interfaces specifically designed for facilitating interaction with large volumetric vascular data sets, and intelligent software capable of guiding the required interactions and generating blood-vessel-specific visualizations and quantitative results with minimal effort. We will conduct a clinical pilot study in the fourth year of the proposed work, during which radiologists will compare the use of a prototype system with conventional image-by-image reading for accuracy and efficiency. We will focus our analyses on (1) aortoiliac aneurysm, and (2) lower extremity occlusive disease, as test cases for the new technology. Upon completion of these studies, we expect that our developments will be easily adaptable to other applications both within and outside of vascular imaging, as well as to other imaging modalities such as MRI. Our overall goal is to change and validate the way crosssectional images are interpreted in general, thus resulting in improved accuracy and efficiency in the assessment of increasingly large volumes of medical image data.