KEYWORDS: Received signal strength, Imaging informatics, Databases, Radio propagation, Medical imaging, Document management, Picture Archiving and Communication System, Internet, Image processing, Information science
There are over 40 open source projects in the field of radiology informatics. Because these are organized and written by volunteers, the development speed varies greatly from one project to the next. To keep track of updates, users must constantly check in on each project's Web page. Many projects remain dormant for years, and ad hoc checking becomes both an inefficient and unreliable means of determining when new versions are available. The result is that most end users track only a few projects and are unaware when others that may be more germane to their interests leapfrog in development. RSS feeds provide a machine readable XML format to track software project updates. Currently only 8 of the 40 projects provide RSS feeds for automatic propagation of news updates. We have a built a news aggregation engine around open source projects in radiology informatics.
The relatively low (20%-25%) sensitivity of conventional radiography for lung nodules is an impetus for investigations into computer-assisted diagnostic (CAD) algorithms and into alternative acquisition techniques (such as dual-energy subtraction [DES]), both of which have been shown to increase diagnostic sensitivity for lung nodule detection. This pilot study combined these synergistic techniques in the diagnosis of digital clinical chest radiographs in 26 individuals. A total of 59 marks were identified by the CAD algorithm as suspicious for a nodule using a "conventional" chest direct radiography posterior/anterior image (an average of 2.3 marks per radiograph). Only 39 marks were identified on the soft tissue image of the corresponding DES radiographs (an average of 1.5 marks per radiograph). The sensitivity for nodules considered subtle but "actionable" in the 10-15-mm range was 0% (correctly identifying 0 of 4 nodules), whereas the sensitivity for the same radiographs with DES was 75% (correctly identifying 3 of 4 nodules). These pilot data suggest that the algorithms for at least one commercial CAD system may not be fully able to differentiate overlying bones and other calcifications from pulmonary lesions (which is also a difficult task for radiologists) and that the combination of CAD and DES acquisition may result in a substantial improvement in both sensitivity and specificity in the detection of relatively subtle lung nodules. This study has been expanded to evaluate a much larger set of images to further investigate the potential for the routine use of CAD with DES.
KEYWORDS: Picture Archiving and Communication System, Performance modeling, Monte Carlo methods, Computer simulations, Radiology, Data modeling, Imaging systems, Diagnostics, Data centers
Determining the performance bottleneck of a PACS system is a challenging task. System performance is dependent on several variables such as the workstation, network, servers, type of data, and different loading conditions. This makes planning difficult to ensure the system capacity will deliver fast access to images throughout the enterprise of a hospital even during rush periods. The rules of thumb that most vendors use for the number of workstations per server are based upon heuristic experience and may not apply from institution to institution where usage and infrastructures are different. Rules of thumb can be problematic and usually cannot predict the impact when new technology is introduced like Gigabit Ethernet or distributed architectures. We have developed a Monte Carlo Model in an attempt to develop a more accurate model to predict loading on a system at peak “rush hour” times. The focus of the model was on user metrics of performance such as the latency and throughput of images to their workstation. Analysis demonstrates that “traffic jams” can occur and dissipate in a matter of minutes and be relatively irreproducible to the PACS administrator.
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