Efficient medical image access in diagnostic environments with limited resources
Datasets usually provide raw data for analysis. This raw data often comes in spreadsheet form, but can be any collection of data, on which analysis can be performed.
Abstract Introduction A medical application running outside the workstation environment has to deal with several constraints, such as reduced available memory and low network bandwidth. The aim of this paper is to present an approach to optimize the data flow for fast image transfer and visualization on mobile devices and remote stationary devices. Methods We use a combination of client- and server-side procedures to reduce the amount of information transferred by the application. Our approach was implemented on top of a commercial PACS and evaluated through user experiments with specialists in typical diagnosis tasks. The quality of the system outcome was measured in relation to the accumulated amount of network data transference and the amount of memory used in the host device. Besides, the system's quality of use (usability) was measured through participants’ feedback. Results Contrarily to previous approaches, ours keeps the application within the memory constraints, minimizing data transferring whenever possible, allowing the application to run on a variety of devices. Moreover, it does that without sacrificing the user experience. Experimental data point that over 90% of the users did not notice any delays or degraded image quality, and when they did, they did not impact on the clinical decisions. Conclusion The combined activities and orchestration of our methods allow the image viewer to run on resource-constrained environments, such as those with low network bandwidth or little available memory. These results demonstrate the ability to explore the use of mobile devices as a support tool in the medical workflow.