Efficient Interpretation of 3D Vascular Image Data

描述(申请人提供)：计算机断层扫描的最新发展 已经产生了对血管进行三维成像的能力 比传统的血管造影术侵袭性更小。然而，这些发展已经 还有可能为每个患者生成数千张图像 放射学解释研究。因此，它变得越来越多 很难分别读取每个图像并在 合理的时间长度。因此，我们建议开发和验证 改变容积CT血管放射学解释的技术 从单个横截面的目视检查到范例的图像数据 它结合了高效、符合人体工程学和交互式的体积 可视化和定量分析。为此，在前三年 在这个项目的多年中，我们将开发一套硬件和软件系统 专门为这项任务设计的。它将由一大片区域组成 高分辨率显示，一套专门的人机界面 设计用于促进与大体积血管数据集的交互， 和智能软件，能够指导所需的交互和 生成特定于血管的可视化和量化结果 最小的努力。我们会在第四年进行临床试验研究。 拟议的工作，在此期间放射科医生将比较使用 具有传统逐个图像读取的原型系统，以提高准确性和 效率。我们将集中分析(1)主-髂动脉瘤，和(2) 下肢闭塞症，作为新技术的测试案例。vt.在.的基础上 随着这些研究的完成，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 以及其他成像手段，如磁共振成像。我们的总体目标是改变 并验证通常解释横断面图像的方式，从而 从而提高了对日益增长的 海量的医学图像数据。

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.