PACS IN NEURORADIOLOGY

现代诊断神经放射学已经发展到两个关键阶段， 方面，对PACS架构提出了具有挑战性的要求。 第一、 神经放射学与高容量图像数据集相关联。 这 要求智能图像分类和呈现算法 根据放射科医生的思维模式 (e.g.,矢状T1与对比度）。 第二，介入神经放射学 已经使得血管造影数据的定量图像分析高度 令人向往 在血管造影中提供有意义的决策支持 套件、图像数据和诸如血流之类相关计算必须 在手术过程中连接和访问。 此外， 询问累积图像和字母数字的整个库存 神经介入患者的数据将提供决策支持， 成果分析的基础。 在这个项目中，我们提出：（1）实现逻辑排序和显示 快速查看大型数据集的策略和（2）在线演示 定量信息的获取、计算和整合 在神经介入的环境中。 基于定义了 放射科医师的职能和表征 要管理的数据类型，数据属性和数据关系， 我们将开发图像索引和呈现策略，以改善 软拷贝诊断的效率。 的分析能力， 将通过部署密度测量工具扩大工作站， 从数字血管造影数据获取血流测量值。 以下 在连续体模和动物模型中的验证，这些 计算技术将在流入分析中进行在线测试 和流出道血管的血管造影数据， 畸形 最后，通过PACS， 将建立一个数据库，使所有人都能透明地查阅。 与神经介入患者治疗相关的信息。 这些先进的PACS功能的成功将有助于克服 实现完整PACS的其余实际和心理障碍 验收

Contemporary diagnostic Neuroradiology has evolved in two critical respects, imposing challenging requirements on PACS architectures. First, neuroradiology is associated with high volume image datasets. This requires that intelligent image sorting and presentation algorithms be developed that are patterned after the radiologists' mental paradigms (e.g., sagittal T1 with contrast). Second, interventional neuroradiology has made quantitative image analysis of angiographic data highly desirable. To provide meaningful decision support in the angiographic suite, image data and related computations such as blood flow must be linked and accessible during the procedure. In addition, the ability to interrogate the entire inventory of accumulated image and alphanumeric data on neurointerventional patients would offer both decision support and a basis for outcomes analyses. In this project, we propose to: (1) implement logical sorting and display strategies for rapidly viewing large data sets and (2) demonstrate on-line acquisition, computation, and integration of quantitative information within a neurointerventional setting. Based on process models that define the functions of the radiologist and data models that characterize the types of data to be managed, the data attributes and data relationships, we will develop image indexing and presentation strategies to improve the efficiency of soft-copy diagnosis. The analytical capabilities of the workstation will be extended through deployment of densitometric tools to acquire blood flow measurements from digital angiographic data. Following validation in sequential phantom and animal models, the utility of these computational techniques will be tested on-line in the analysis of inflow and outflow vessels in angiographic data on patients with vascular malformations. Finally, through PACS, an alphanumeric and image inclusive database will be developed which allows transparent access to all information pertinent to the treatment of neurointerventional patients. The success of these sophisticated PACS capabilities will help to overcome the remaining practical and psychological barriers to full PACS acceptance.