INTEGRATED PACS--RADIOLOGICAL EVALUATION/TREATMENT NEUROLOGICAL DISEASE

当代神经放射学的复杂性要求神经放射学家从庞大而复杂的数据集中提取、解释和交流重要内容。需要一种新的图像和数据管理范式，以智能地利用临床管理方案（途径）中固有的信息，并确定图像采集、组织、显示、处理和客观化患者主观发现的最佳方案；(b)提供及时准确的诊断；(c)通过简化审查和可能减少进行的测试数量来减少医疗保健服务的低效率。我们假设这些PACS技术将通过促进智能报告工具、创建新的报告和教学方法，以及为研究和医疗目的形成可搜索的数据库，促进快速临床行动，提高医生满意度。该项目的目标有两个：[1]通过开发临床驱动的适应性阅读协议，为神经系统疾病患者的影像学研究和相关文件的定制管理和呈现实施一种新的策略；[2]通过开发智能自适应报告编写协议，协助自动编写包含关键图像的多媒体报告，查找脑肿瘤患者。这些目标将通过开发面向对象的过程和成像过程、任务以及在患者护理过程中使用和生成的数据的数据模型来实现，从而导致开发用于数据表示、在线量化和自动报告创建的自适应成像协议。对这些模型、协议和工具包的技术性能进行正式评估。最后，我们将评估由此产生的基于pacs工作站的系统对神经放射学家和神经肿瘤学家的表现、信心和满意度的临床影响。

The complexity of contemporary neuroradiology requires neuroradiologists to extract, interpret, and communicate important content from large and complex data sets. A new paradigm for image and data management is required to intelligently utilizes the information inherent in clinical management schemes (pathways) and to determine the optimal protocols for image acquisition, organization, display, processing and objectifying patients' subjective findings; (b) providing timely anc accurate diagnosis; and (c) reducing the inefficiency of healthcare delivery by streamlining review and potentially reducing the number of tests performed. We hypothesize that these PACS technologies will facilitate rapid clinical actions and increase physician satisfaction by facilitating tools for intelligent reporting, creating new reporting, and teaching methods, and forming searchable databases for research and medical care purposes. This project's goals are twofold: [1] to implement a new strategy for customized management and presentation of imaging studies and related documents in patients with neurological disease by developing clinically driven adaptive reading protocols; and [2] to assist automatic authoring of multi-media reports containing critical images and finding sin brain tumor patients by developing intelligent adaptive report authoring protocols. These objectives will be achieved by developing object-oriented process and data models of the imaging processes, the tasks, and the data used and generated in the course of patient care, leading to the development of adaptive imaging protocols for data presentation, online quantification, and automated report creation. The technical performance of these models, protocols, and toolkits are formally evaluated. Lastly, we will evaluate the clinical impact of the resulting PACS-workstation-based system on the performance, confidence, and satisfaction of neuroradiologists and neuro-oncologists.