„Remote Scanning“ – Optimizing parameters and benchmarking of Remote Desktop Applications in Medical Imaging under emulated network conditions for remote access scenarios
With the growing infrastructure and demands in the healthcare domain, medical imaging has become an integral part of diagnosis. This increasing demand has naturally led to a shortage of trained radiologists required to operate Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scanner machines. In this scenario, remote scanning is emerging as a viable solution. Remote scanning is a clinical operational model in which a certified medical radiologist controls these scanners from a separate physical location via a secure network. This setup decouples the operator from the scanner console, allowing him to perform examinations and monitor patients across multiple sites without being physically present in the scanner control room. This approach has its own set of challenges, like poor network connectivity, IT infrastructure issues, or loss of image quality and framerate.
This master’s thesis focuses on testing different off-the-shelf remote desktop applications under varying network conditions by adjusting network parameters like bandwidth, latency, and packet loss. Along with that, different remote desktop tools have their own parameters that could be varied, which can impact the user experience. The goal is to benchmark the performance of these tools and identify the most significant parameters relevant to the remote scanning scenario in the domain of healthcare imaging, taking into account the latest scientific research regarding streaming, networking, and image compression algorithms. This involves studying the working principles and network protocols of different remote desktop applications and how different parameters can impact their usability. To emulate the actual scanning scenario, the goal is to build a remote scanning testbed to run multiple tests with differing parameters.
Once the test-setup is up and running, the goal is to run multiple tests, to gather relevant data and logs from each individual component. This data would help identify how Image Quality (IQ), Framerate (FPS), and System Response Time (SRT) are impacted under varying network conditions. These results will allow us to detect the most valuable parameters for “Remote scanning” solutions in medical imaging. In addition, this research can assist in identifying which tools have the most suitable approach to meet these requirements of the healthcare infrastructure.
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