PACS queue monitor helps predict image delivery time

March 23, 2007

A backlog in the PACS queue that results from too many simultaneous study requests has implications beyond slow image delivery. A recent paper demonstrated the feasibility of a PACS Queue Manager algorithm to monitor PACS performance under peak loads.

A backlog in the PACS queue that results from too many simultaneous study requests has implications beyond slow image delivery. A recent paper demonstrated the feasibility of a PACS Queue Manager algorithm to monitor PACS performance under peak loads.

"Monitoring the queue of our PACS allowed us to predict delivery time, potentially offering a means to manage user expectations of fast response, especially during peak hours," said Nelson King, Ph.D., a business professor at American University in Beirut.

King's paper (J Digit Imaging 2006 Dec;19 Suppl 1:35-43) documents an algorithm that can predict approximate image delivery times. The idea is that users can adjust their tasks more efficiently if given feedback on the length of delay.

"Making a radiologist or physician wait for image delivery should, of course, be avoided, but if they have to wait, this at least lets them know how long," King said.

A PACS with a stand-alone architecture under peak load typically holds study requests in a queue until the DICOM C-Move command can take place. King investigated the contents of a stand-alone architecture PACS Retrieve-Send queue and identified parameters and behaviors that enable a more accurate prediction of delivery time.

"While most modern PACS are unlikely to have such obvious delays as our laboratory system, the images generated by modalities will only increase (and number of users as well), which at some point will create a backlog as images are transferred," he said.

The prediction algorithm takes into consideration site-specific conditions, including speed of transfer depending upon bandwidth, client computer capability, DICOM application software, PACS load, and specific file parameters of a clinical study.

"All these factors affect the transfer rate of images to a workstation," King said.

Predicting the waiting time to download a file is not new. In 2003, Paul Nagy, Ph.D., an assistant professor of radiology at the University of Maryland School of Medicine, released an open-source tool called PACSPulse, which was designed to identify and analyze PACS performance bottlenecks. PACSPulse provides a graphical web interface for straightforward analysis of PACS performance on the basis of data acquired by tracking usage by network, server, workstation, type of traffic, and time of day.

King's contribution is to get PACS administrators to look at their systems and see if backlogs can be prevented and, if not, how the end user can benefit from feedback on delay time.