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.
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| Sinha, Usha; Kangarloo, Hooshang (2002) Image study summarization of MR brain images by automated localization of relevant structures. Ann N Y Acad Sci 980:278-86 |
| Bui, Alex A T; Taira, Ricky K; Dionisio, John David N et al. (2002) Evidence-based radiology: requirements for electronic access. Acad Radiol 9:662-9 |
| Morioka, Craig A; Sinha, Usha; Taira, Ricky et al. (2002) Structured reporting in neuroradiology. Ann N Y Acad Sci 980:259-66 |
| Bui, Aleex A T; Taira, Ricky K; Churchill, Bernard et al. (2002) Integrated visualization of problemcentric urologic patient records. Ann N Y Acad Sci 980:267-77 |
| Dionisio, John David N; Bui, Alexander A T; Johnson, David et al. (2002) Designing a patient education framework via use case analysis. Ann N Y Acad Sci 980:225-35 |
| Son, Roderick Y; Taira, Ricky K; Bui, Alex A T et al. (2002) A context-sensitive methodology for automatic episode creation. Proc AMIA Symp :707-11 |
| Goldin, Jonathan G (2002) Quantitative CT of the lung. Radiol Clin North Am 40:145-62 |
| Bui, Alex A T; Weinger, Gregory S; Barretta, Susan J et al. (2002) An XML Gateway to Patient Data for Medical Research Applications. Ann N Y Acad Sci 980:236-46 |
| Bui, Alex A T; Dionisio, John David N; Morioka, Craig A et al. (2002) DataServer: an infrastructure to support evidence-based radiology. Acad Radiol 9:670-8 |
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