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.
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