A complete image management and communications system has been installed at Georgetown University Hospital (GUH). The network is based on the A T & T CommView® System. In the Neuroradiology Division, this comprehensive network supports a multiscreen workstation with access to multiple imaging modalities such as CT and MRI from both the hospital and a remote imaging center. In addition, the radiologist can access these images from various workstations located throughout the hospital as well as from remote sites such as the home. Among the radiology services supported by the network, neuroradiology has the greatest need for such a system with extensive daily requirements involving the remote imaging center and on -line consultation around the clock. By providing neuroradiology with all available communication links, the radiologist can monitor, diagnose, and consult.The remote site has a subsystem capable of acquiring images and transmitting them over a high speed Ti data circuit. The GUH neuroradiologist can view these images on the neuro workstation or any of the workstations available in the Hospital. Fast and easy access to the images allows a radiologist to monitor multiple examinations as well as to utilize the workstation for diagnosis.To provide the neuroradiologist quick access to images at all times, a PC -based Results Viewing Station (RVS) has been placed in a doctor's home. Images may be sent to the RVS, or the user may request images from the central database at the hospital. Images can be viewed at home either as they are transmitted, or following transfer of a whole study.The efficiency and effectiveness of the system's capabilities with special regard to remote and teleradiology (RVS) operations have been studied for the neuroradiology service. This paper will discuss the current clinical acceptance and use, problems in implementation, and ways these difficulties are being surmounted.
In expanding our image management and communications system (IMACS) to include a new machine in the Ultrasound Section, we first modeled the impact of attaching the unit to either of our two acquisition modules (AM). Using the software package available to us, we could predict image queue lengths and average image waiting times (before transmission to the central archive). The AMs have attached a variety of devices with differing image production loads. The modeling allowed us to select the AM which would be least impacted (in terms of resoponse time to the devices sending data to the AM) by the added ultrasound machine.We found that though the input response times would not change much with the ultrasound machine connected to either AM, there was a significant impact on the predicted output queue length, with the more heavily loaded AM suffering larger increases in output queue dwell time if the new machine were connected to it. Based on these results, we elected to redistribute the AM loads, and connect the ultmsound machine so as to maintain a relatively balanced load.We tested certain parameters of the model by measuring input and output response times ofan AM under different, artificially induced, acquisition loading. The changes predicted by the model agreed with those measured in order-ofmagnitude terms.
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