PACS IN NEURORADIOLOGY

当代诊断神经放射学在两个关键方面发展 对PACS体系结构提出了具有挑战性的要求。第一, 神经放射学与大容量图像数据集相关。这 要求智能图像分类和呈现算法 是仿照放射科医生的思维模式发展起来的 (例如，具有对比度的矢状T1)。第二，介入神经放射学 对血管造影数据进行了高度的定量图像分析 令人向往。在血管造影中提供有意义的决策支持 套件、图像数据和相关计算(如血流)必须 在手术过程中链接和访问。此外，还可以 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和 作为成果分析的基础。 在本项目中，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 在神经干预性环境中。基于定义以下内容的流程模型 放射科医生的职能和数据模型 要管理的数据类型、数据属性和数据关系， 我们将制定图像索引和演示策略，以提高 软拷贝诊断的效率。的分析能力 将通过部署密度测量工具来扩展工作站 从数字血管造影数据中获取血流量测量结果。跟随 在序贯模型和动物模型中的验证，这些的效用 计算技术将在流入分析中进行在线测试 和流出血管在血管造影数据中的变化 畸形。最后，通过PACS，包括字母数字和图像 将开发数据库，使所有人都能透明访问 与神经介入患者的治疗相关的信息。 这些先进的PACS功能的成功将有助于克服 全面PACS尚存的实际和心理障碍 接受。

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.